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THE
QUARTERLY JOURNALOF ECONOMICS
Vol 131 November 2016 Issue 4
MEASURING ECONOMIC POLICY UNCERTAINTY
SCOTT R BAKER
NICHOLAS BLOOM
STEVEN J DAVIS
We develop a new index of economic policy uncertainty (EPU) based onnewspaper coverage frequency Several types of evidencemdashincluding humanreadings of 12000 newspaper articlesmdashindicate that our index proxies for move-ments in policy-related economic uncertainty Our US index spikes near tightpresidential elections Gulf Wars I and II the 911 attacks the failure ofLehman Brothers the 2011 debt ceiling dispute and other major battles overfiscal policy Using firm-level data we find that policy uncertainty is associatedwith greater stock price volatility and reduced investment and employment inpolicy-sensitive sectors like defense health care finance and infrastructure con-struction At the macro level innovations in policy uncertainty foreshadow de-clines in investment output and employment in the United States and in apanel vector autoregressive setting for 12 major economies Extending our USindex back to 1900 EPU rose dramatically in the 1930s (from late 1931) and hasdrifted upward since the 1960s JEL Codes D80 E22 E66 G18 L50
We thank Adam Jorring Kyle Kost Abdulla Al-Kuwari Sophie Biffar JornBoehnke Vladimir Dashkeyev Olga Deriy Eddie Dinh Yuto Ezure Robin GongSonam Jindal Ruben Kim Sylvia Klosin Jessica Koh Peter Lajewski DavidNebiyu Rebecca Sachs Ippei Shibata Corinne Stephenson Naoko TakedaMelissa Tan Sophie Wang and Peter Xu for research assistance and theNational Science Foundation MacArthur Foundation Sloan Foundation BeckerFriedman Institute Initiative on Global Markets and Stigler Center at theUniversity of Chicago for financial support We thank Ruedi Bachmann SanjaiBhagat Vincent Bignon Yongsung Chang Vladimir Dashkeyev JesusFernandez-Villaverde Laurent Ferrara Luis Garicano Matt Gentzkow YuriyGorodnichenko Kevin Hassett Takeo Hoshi Greg Ip Anil Kashyap PatrickKehoe John Makin Johannes Pfeifer Meijun Qian Itay Saporta John ShovenSam Schulhofer-Wohl Jesse Shapiro Erik Sims Stephen Terry Cynthia Wu andmany seminar and conference audiences for comments We also thank the refereesand editors Robert Barro and Larry Katz for comments and suggestions
The Author(s) 2016 Published by Oxford University Press on behalf of Presidentand Fellows of Harvard College All rights reserved For Permissions please emailjournalspermissionsoupcomThe Quarterly Journal of Economics (2016) 1593ndash1636 doi101093qjeqjw024Advance Access publication on July 11 2016
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I Introduction
Concerns about policy uncertainty have intensified in thewake of the global financial crisis serial crises in the Eurozoneand partisan policy disputes in the United States For examplethe Federal Open Market Committee (2009) and theInternational Monetary Fund (IMF) (2012 2013) suggest thatuncertainty about US and European fiscal regulatory and mon-etary policies contributed to a steep economic decline in 2008ndash2009 and slow recoveries afterward1
To investigate the role of policy uncertainty we first developan index of economic policy uncertainty (EPU) for the UnitedStates and examine its evolution since 19852 Our index reflectsthe frequency of articles in 10 leading US newspapers that con-tain the following trio of terms lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo lsquolsquouncer-tainrsquorsquo or lsquolsquouncertaintyrsquorsquo and one or more of lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquolsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquo lsquolsquoregulationrsquorsquo or lsquolsquoWhite HousersquorsquoThe index spikes near tight presidential elections Gulf Wars I andII the 911 attacks the 2011 debt ceiling dispute and other majorbattles over fiscal policy We extend our newspaper-based ap-proach to measuring policy uncertainty along three dimensionsback in time across countries and to specific policy categories
To push back to 1900 we rely on archives for six major USnewspapers published throughout the past century Thislong-span EPU index highlights prendashWorld War II political de-velopments and shocks like the Gold Standard Act of 1900 theoutbreak of World War I the Versailles conference in 1919 and asustained surge in policy uncertainty from late 1931 whenPresident Herbert Hoover and then President FranklinRoosevelt introduced a rash of major new policies The indexalso shows an upward drift since the 1960s perhaps due torising political polarization or the growing economic role for gov-ernment (Baker et al 2014)Using similar methods we constructEPU indexes for 11 other countries including all G10 economiesThese indexes are particularly helpful in countries with feweralternative uncertainty measures We develop category-specific
1 lsquolsquoWidespread reports from business contacts noted that uncertainties abouthealth-care tax and environmental policies were adding to businessesrsquo reluctanceto commit to higher capital spendingrsquorsquo (Federal Open Market Committee 2009) Seealso IMF (2012 pp xvndashxvi and 49ndash53 and 2013 pp 70ndash76)
2 Our data are available at monthly and daily frequencies at httpwwwpolicyuncertaintycom and are carried by Bloomberg Haver FRED and Reuters
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policy uncertainty indexes for the United States by specifyingmore restrictive criteria for those articles that contain termsabout the economy policy and uncertainty For example wedevelop indexes of health care policy uncertainty and nationalsecurity policy uncertainty based on the presence of additionalterms like lsquolsquohealth carersquorsquo lsquolsquohospitalrsquorsquo or lsquolsquohealth insurancersquorsquo andlsquolsquowarrsquorsquo lsquolsquoterrorismrsquorsquo or lsquolsquodepartment of defensersquorsquo respectivelyCategory-specific shocks and policy initiatives are clearly visible
Our approach to measuring policy uncertainty raises potentialconcerns about newspaper reliability accuracy bias and consis-tency To address these concerns we evaluate our EPU index inseveral ways First we show a strong relationship between ourmeasure of EPU and other measures of economic uncertainty forexample implied stock market volatility Second we also show astrong relationship between our index and other measures of policyuncertainty for example the frequency with which the FederalReserve Systemrsquos Beige Books mention policy uncertainty Thirdwe find very similar movements in EPU indexes based on right-leaning and left-leaning newspapers suggesting that politicalslant does not seriously distort our overall EPU index
Fourth we conducted an extensive audit study of 12000 ran-domly selected articles drawn from major US newspapers Workingunder close supervision teams of University of Chicago studentsunderwent a training process and then carefully read overlappingsets of randomly selected articles guided by a 65-page referencemanual and weekly team meetings The auditors assessed whethera given article discusses economic policy uncertainty based on ourcriteria We use the audit results to select our policy term set eval-uate the performance of our computer-automated methods and con-struct additional data There is a high correlation between ourhuman- and computer-generated indexes (086 in quarterly datafrom 1985 to 2012 and 093 in annual data from 1900 to 2010) Thediscrepancy between the human and computer-generated indexes isuncorrelated with GDP growth rates and with the level of EPU
Finally our indexes have a market use validation commer-cial data providers that include Bloomberg FRED Haver andReuters carry our indexes to meet demands from banks hedgefunds corporations and policy makers This pattern of marketadoption suggests that our indexes contain useful information fora range of decision makers
In Section IV we provide evidence of how firm-level and ag-gregate outcomes evolve in the wake of policy uncertainty
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movements Causal inference is challenging because policy re-sponds to economic conditions and is likely to be forward lookingTo make progress we follow a micro and a macro estimation ap-proach First the micro approach exploits firm-level differences inexposure to certain aspects of policy mainly government pur-chases of goods and services We use micro data from the FederalRegistry of Contracts and data on government health care spend-ing to calculate the share of firm and industry revenues derivedfrom sales to the government Next in firm-level regressions thatinclude time and firm fixed effects and other controls we show thatfirms with greater exposure to government purchases experiencegreater stock price volatility when policy uncertainty is high andreduced investment rates and employment growth when policyuncertainty rises Adding the VIX as an explanatory variable (in-teracted with firm-level exposure to government purchases) westill find greater stock price volatility and falls in investment andemployment with heightened policy uncertainty which points to apolicy uncertainty channel at work rather than a broader uncer-tainty effect We also find that firms in the defense health careand financial sectors are especially responsive to their own cate-gory-specific EPU measures confirming their information value
These firm-level results are suggestive of a causal impact ofpolicy uncertainty on investment and employment in sectors thatrely heavily on government spending and in sectors like healthcare and finance with strong exposure to major shifts in regula-tory policy However the firm-level results offer limited guidanceabout the magnitude of aggregate effects in part because theycapture only a limited set of potential policy uncertainty channels
Our second approach fits vector autoregressive (VAR) modelsto US data and to an international panel VAR that exploits ourEPU indexes for 12 countries The US VAR results indicate thata policy uncertainty innovation equivalent to the actual EPU in-crease from 2005ndash2006 to 2011ndash2012 foreshadows declines ofabout 6 in gross investment 11 in industrial productionand 035 in employment The 12-country panel VAR yields sim-ilar results3 Although our results are not necessarily causal oneplausible interpretation of our micro and macro evidence is that
3 Stock and Watson (2012) use our EPU index to investigate the factorsbehind the 2007ndash2009 recession and slow recovery and come to a similar conclu-sionmdashnamely that policy uncertainty is a strong candidate to partly explain thepoor economic performance but causal identification is hard
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policy uncertainty retards investment hiring and growth in pol-icy-sensitive sectors like defense finance healthcare and con-struction and these sectors are important enough for policyuncertainty to matter at the aggregate level
This article relates to at least three strands of literature Thefirst is research on the impact of uncertainty on growth and in-vestment Theoretical work on this topic dates at least toBernanke (1983) who points out that high uncertainty givesfirms an incentive to delay investment and hiring wheninvestment projects are costly to undo or workers are costly tohire and fire4 Of course once uncertainty recedes firms increasehiring and investment to meet pent-up demand Other reasons fora depressive effect of uncertainty include precautionary spendingcutbacks by households upward pressure on the cost of finance(eg Pastor and Veronesi 2013 Gilchrist Sim and Zakrajsek2014) managerial risk aversion (eg Panousi and Papanikolaou2012) and interactions between nominal rigidities and searchfrictions (Basu and Bundick 2012 Leduc and Liu 2015)
Second there is a literature focused explicitly on policy un-certainty Friedman (1968) Rodrik (1991) Higgs (1997) andHassett and Metcalf (1999) among others consider the detrimen-tal economic effects of monetary fiscal and regulatory policy un-certainty More recently Born and Pfeifer (2014) and Fernandez-Villaverde at al (2015) study policy uncertainty in DSGE modelsfinding moderately negative effects while Pastor and Veronesi(2012 2013) model the theoretical links among fluctuationspolicy uncertainty and stock market volatility5
4 Dixit and Pindyck (1994) offer a review of the early theoretical literatureincluding papers by Oi (1961) Hartman (1972) and Abel (1983) that highlightpotentially positive effects of uncertainty Recent empirical papers include Bloom(2009) Bachman Elstener and Sims (2013) Bloom et al (2014) and Scotti (2016)with a review in Bloom (2014)
5 In other related work Julio and Yook (2012) find that investment fallsaround national elections Durnev (2010) finds that corporate investment becomesless responsive to stock prices in election years Brogaard and Detzel (2015) findthat policy uncertainty reduces asset returns Handley and Limao (2015) find thattrade policy uncertainty delays firm entry Gulen and Ion (2016) find negative re-sponses of corporate investment to our EPU index Koijen Philipson and Uhlig(2016) develop evidence that government-induced uncertainty about profitabilitygenerates a large equity risk premium for firms in the health care sector and redu-ces their medical RampD and Giavazzi and McMahon (2012) find that policy uncer-tainty led German households to increase savings in the run-up to the close andconsequential general elections in 1998
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1605
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
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Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
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I Introduction
Concerns about policy uncertainty have intensified in thewake of the global financial crisis serial crises in the Eurozoneand partisan policy disputes in the United States For examplethe Federal Open Market Committee (2009) and theInternational Monetary Fund (IMF) (2012 2013) suggest thatuncertainty about US and European fiscal regulatory and mon-etary policies contributed to a steep economic decline in 2008ndash2009 and slow recoveries afterward1
To investigate the role of policy uncertainty we first developan index of economic policy uncertainty (EPU) for the UnitedStates and examine its evolution since 19852 Our index reflectsthe frequency of articles in 10 leading US newspapers that con-tain the following trio of terms lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo lsquolsquouncer-tainrsquorsquo or lsquolsquouncertaintyrsquorsquo and one or more of lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquolsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquo lsquolsquoregulationrsquorsquo or lsquolsquoWhite HousersquorsquoThe index spikes near tight presidential elections Gulf Wars I andII the 911 attacks the 2011 debt ceiling dispute and other majorbattles over fiscal policy We extend our newspaper-based ap-proach to measuring policy uncertainty along three dimensionsback in time across countries and to specific policy categories
To push back to 1900 we rely on archives for six major USnewspapers published throughout the past century Thislong-span EPU index highlights prendashWorld War II political de-velopments and shocks like the Gold Standard Act of 1900 theoutbreak of World War I the Versailles conference in 1919 and asustained surge in policy uncertainty from late 1931 whenPresident Herbert Hoover and then President FranklinRoosevelt introduced a rash of major new policies The indexalso shows an upward drift since the 1960s perhaps due torising political polarization or the growing economic role for gov-ernment (Baker et al 2014)Using similar methods we constructEPU indexes for 11 other countries including all G10 economiesThese indexes are particularly helpful in countries with feweralternative uncertainty measures We develop category-specific
1 lsquolsquoWidespread reports from business contacts noted that uncertainties abouthealth-care tax and environmental policies were adding to businessesrsquo reluctanceto commit to higher capital spendingrsquorsquo (Federal Open Market Committee 2009) Seealso IMF (2012 pp xvndashxvi and 49ndash53 and 2013 pp 70ndash76)
2 Our data are available at monthly and daily frequencies at httpwwwpolicyuncertaintycom and are carried by Bloomberg Haver FRED and Reuters
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policy uncertainty indexes for the United States by specifyingmore restrictive criteria for those articles that contain termsabout the economy policy and uncertainty For example wedevelop indexes of health care policy uncertainty and nationalsecurity policy uncertainty based on the presence of additionalterms like lsquolsquohealth carersquorsquo lsquolsquohospitalrsquorsquo or lsquolsquohealth insurancersquorsquo andlsquolsquowarrsquorsquo lsquolsquoterrorismrsquorsquo or lsquolsquodepartment of defensersquorsquo respectivelyCategory-specific shocks and policy initiatives are clearly visible
Our approach to measuring policy uncertainty raises potentialconcerns about newspaper reliability accuracy bias and consis-tency To address these concerns we evaluate our EPU index inseveral ways First we show a strong relationship between ourmeasure of EPU and other measures of economic uncertainty forexample implied stock market volatility Second we also show astrong relationship between our index and other measures of policyuncertainty for example the frequency with which the FederalReserve Systemrsquos Beige Books mention policy uncertainty Thirdwe find very similar movements in EPU indexes based on right-leaning and left-leaning newspapers suggesting that politicalslant does not seriously distort our overall EPU index
Fourth we conducted an extensive audit study of 12000 ran-domly selected articles drawn from major US newspapers Workingunder close supervision teams of University of Chicago studentsunderwent a training process and then carefully read overlappingsets of randomly selected articles guided by a 65-page referencemanual and weekly team meetings The auditors assessed whethera given article discusses economic policy uncertainty based on ourcriteria We use the audit results to select our policy term set eval-uate the performance of our computer-automated methods and con-struct additional data There is a high correlation between ourhuman- and computer-generated indexes (086 in quarterly datafrom 1985 to 2012 and 093 in annual data from 1900 to 2010) Thediscrepancy between the human and computer-generated indexes isuncorrelated with GDP growth rates and with the level of EPU
Finally our indexes have a market use validation commer-cial data providers that include Bloomberg FRED Haver andReuters carry our indexes to meet demands from banks hedgefunds corporations and policy makers This pattern of marketadoption suggests that our indexes contain useful information fora range of decision makers
In Section IV we provide evidence of how firm-level and ag-gregate outcomes evolve in the wake of policy uncertainty
ECONOMIC POLICY UNCERTAINTY 1595
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movements Causal inference is challenging because policy re-sponds to economic conditions and is likely to be forward lookingTo make progress we follow a micro and a macro estimation ap-proach First the micro approach exploits firm-level differences inexposure to certain aspects of policy mainly government pur-chases of goods and services We use micro data from the FederalRegistry of Contracts and data on government health care spend-ing to calculate the share of firm and industry revenues derivedfrom sales to the government Next in firm-level regressions thatinclude time and firm fixed effects and other controls we show thatfirms with greater exposure to government purchases experiencegreater stock price volatility when policy uncertainty is high andreduced investment rates and employment growth when policyuncertainty rises Adding the VIX as an explanatory variable (in-teracted with firm-level exposure to government purchases) westill find greater stock price volatility and falls in investment andemployment with heightened policy uncertainty which points to apolicy uncertainty channel at work rather than a broader uncer-tainty effect We also find that firms in the defense health careand financial sectors are especially responsive to their own cate-gory-specific EPU measures confirming their information value
These firm-level results are suggestive of a causal impact ofpolicy uncertainty on investment and employment in sectors thatrely heavily on government spending and in sectors like healthcare and finance with strong exposure to major shifts in regula-tory policy However the firm-level results offer limited guidanceabout the magnitude of aggregate effects in part because theycapture only a limited set of potential policy uncertainty channels
Our second approach fits vector autoregressive (VAR) modelsto US data and to an international panel VAR that exploits ourEPU indexes for 12 countries The US VAR results indicate thata policy uncertainty innovation equivalent to the actual EPU in-crease from 2005ndash2006 to 2011ndash2012 foreshadows declines ofabout 6 in gross investment 11 in industrial productionand 035 in employment The 12-country panel VAR yields sim-ilar results3 Although our results are not necessarily causal oneplausible interpretation of our micro and macro evidence is that
3 Stock and Watson (2012) use our EPU index to investigate the factorsbehind the 2007ndash2009 recession and slow recovery and come to a similar conclu-sionmdashnamely that policy uncertainty is a strong candidate to partly explain thepoor economic performance but causal identification is hard
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policy uncertainty retards investment hiring and growth in pol-icy-sensitive sectors like defense finance healthcare and con-struction and these sectors are important enough for policyuncertainty to matter at the aggregate level
This article relates to at least three strands of literature Thefirst is research on the impact of uncertainty on growth and in-vestment Theoretical work on this topic dates at least toBernanke (1983) who points out that high uncertainty givesfirms an incentive to delay investment and hiring wheninvestment projects are costly to undo or workers are costly tohire and fire4 Of course once uncertainty recedes firms increasehiring and investment to meet pent-up demand Other reasons fora depressive effect of uncertainty include precautionary spendingcutbacks by households upward pressure on the cost of finance(eg Pastor and Veronesi 2013 Gilchrist Sim and Zakrajsek2014) managerial risk aversion (eg Panousi and Papanikolaou2012) and interactions between nominal rigidities and searchfrictions (Basu and Bundick 2012 Leduc and Liu 2015)
Second there is a literature focused explicitly on policy un-certainty Friedman (1968) Rodrik (1991) Higgs (1997) andHassett and Metcalf (1999) among others consider the detrimen-tal economic effects of monetary fiscal and regulatory policy un-certainty More recently Born and Pfeifer (2014) and Fernandez-Villaverde at al (2015) study policy uncertainty in DSGE modelsfinding moderately negative effects while Pastor and Veronesi(2012 2013) model the theoretical links among fluctuationspolicy uncertainty and stock market volatility5
4 Dixit and Pindyck (1994) offer a review of the early theoretical literatureincluding papers by Oi (1961) Hartman (1972) and Abel (1983) that highlightpotentially positive effects of uncertainty Recent empirical papers include Bloom(2009) Bachman Elstener and Sims (2013) Bloom et al (2014) and Scotti (2016)with a review in Bloom (2014)
5 In other related work Julio and Yook (2012) find that investment fallsaround national elections Durnev (2010) finds that corporate investment becomesless responsive to stock prices in election years Brogaard and Detzel (2015) findthat policy uncertainty reduces asset returns Handley and Limao (2015) find thattrade policy uncertainty delays firm entry Gulen and Ion (2016) find negative re-sponses of corporate investment to our EPU index Koijen Philipson and Uhlig(2016) develop evidence that government-induced uncertainty about profitabilitygenerates a large equity risk premium for firms in the health care sector and redu-ces their medical RampD and Giavazzi and McMahon (2012) find that policy uncer-tainty led German households to increase savings in the run-up to the close andconsequential general elections in 1998
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
ECONOMIC POLICY UNCERTAINTY 1599
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1605
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
policy uncertainty indexes for the United States by specifyingmore restrictive criteria for those articles that contain termsabout the economy policy and uncertainty For example wedevelop indexes of health care policy uncertainty and nationalsecurity policy uncertainty based on the presence of additionalterms like lsquolsquohealth carersquorsquo lsquolsquohospitalrsquorsquo or lsquolsquohealth insurancersquorsquo andlsquolsquowarrsquorsquo lsquolsquoterrorismrsquorsquo or lsquolsquodepartment of defensersquorsquo respectivelyCategory-specific shocks and policy initiatives are clearly visible
Our approach to measuring policy uncertainty raises potentialconcerns about newspaper reliability accuracy bias and consis-tency To address these concerns we evaluate our EPU index inseveral ways First we show a strong relationship between ourmeasure of EPU and other measures of economic uncertainty forexample implied stock market volatility Second we also show astrong relationship between our index and other measures of policyuncertainty for example the frequency with which the FederalReserve Systemrsquos Beige Books mention policy uncertainty Thirdwe find very similar movements in EPU indexes based on right-leaning and left-leaning newspapers suggesting that politicalslant does not seriously distort our overall EPU index
Fourth we conducted an extensive audit study of 12000 ran-domly selected articles drawn from major US newspapers Workingunder close supervision teams of University of Chicago studentsunderwent a training process and then carefully read overlappingsets of randomly selected articles guided by a 65-page referencemanual and weekly team meetings The auditors assessed whethera given article discusses economic policy uncertainty based on ourcriteria We use the audit results to select our policy term set eval-uate the performance of our computer-automated methods and con-struct additional data There is a high correlation between ourhuman- and computer-generated indexes (086 in quarterly datafrom 1985 to 2012 and 093 in annual data from 1900 to 2010) Thediscrepancy between the human and computer-generated indexes isuncorrelated with GDP growth rates and with the level of EPU
Finally our indexes have a market use validation commer-cial data providers that include Bloomberg FRED Haver andReuters carry our indexes to meet demands from banks hedgefunds corporations and policy makers This pattern of marketadoption suggests that our indexes contain useful information fora range of decision makers
In Section IV we provide evidence of how firm-level and ag-gregate outcomes evolve in the wake of policy uncertainty
ECONOMIC POLICY UNCERTAINTY 1595
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nloaded from
movements Causal inference is challenging because policy re-sponds to economic conditions and is likely to be forward lookingTo make progress we follow a micro and a macro estimation ap-proach First the micro approach exploits firm-level differences inexposure to certain aspects of policy mainly government pur-chases of goods and services We use micro data from the FederalRegistry of Contracts and data on government health care spend-ing to calculate the share of firm and industry revenues derivedfrom sales to the government Next in firm-level regressions thatinclude time and firm fixed effects and other controls we show thatfirms with greater exposure to government purchases experiencegreater stock price volatility when policy uncertainty is high andreduced investment rates and employment growth when policyuncertainty rises Adding the VIX as an explanatory variable (in-teracted with firm-level exposure to government purchases) westill find greater stock price volatility and falls in investment andemployment with heightened policy uncertainty which points to apolicy uncertainty channel at work rather than a broader uncer-tainty effect We also find that firms in the defense health careand financial sectors are especially responsive to their own cate-gory-specific EPU measures confirming their information value
These firm-level results are suggestive of a causal impact ofpolicy uncertainty on investment and employment in sectors thatrely heavily on government spending and in sectors like healthcare and finance with strong exposure to major shifts in regula-tory policy However the firm-level results offer limited guidanceabout the magnitude of aggregate effects in part because theycapture only a limited set of potential policy uncertainty channels
Our second approach fits vector autoregressive (VAR) modelsto US data and to an international panel VAR that exploits ourEPU indexes for 12 countries The US VAR results indicate thata policy uncertainty innovation equivalent to the actual EPU in-crease from 2005ndash2006 to 2011ndash2012 foreshadows declines ofabout 6 in gross investment 11 in industrial productionand 035 in employment The 12-country panel VAR yields sim-ilar results3 Although our results are not necessarily causal oneplausible interpretation of our micro and macro evidence is that
3 Stock and Watson (2012) use our EPU index to investigate the factorsbehind the 2007ndash2009 recession and slow recovery and come to a similar conclu-sionmdashnamely that policy uncertainty is a strong candidate to partly explain thepoor economic performance but causal identification is hard
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policy uncertainty retards investment hiring and growth in pol-icy-sensitive sectors like defense finance healthcare and con-struction and these sectors are important enough for policyuncertainty to matter at the aggregate level
This article relates to at least three strands of literature Thefirst is research on the impact of uncertainty on growth and in-vestment Theoretical work on this topic dates at least toBernanke (1983) who points out that high uncertainty givesfirms an incentive to delay investment and hiring wheninvestment projects are costly to undo or workers are costly tohire and fire4 Of course once uncertainty recedes firms increasehiring and investment to meet pent-up demand Other reasons fora depressive effect of uncertainty include precautionary spendingcutbacks by households upward pressure on the cost of finance(eg Pastor and Veronesi 2013 Gilchrist Sim and Zakrajsek2014) managerial risk aversion (eg Panousi and Papanikolaou2012) and interactions between nominal rigidities and searchfrictions (Basu and Bundick 2012 Leduc and Liu 2015)
Second there is a literature focused explicitly on policy un-certainty Friedman (1968) Rodrik (1991) Higgs (1997) andHassett and Metcalf (1999) among others consider the detrimen-tal economic effects of monetary fiscal and regulatory policy un-certainty More recently Born and Pfeifer (2014) and Fernandez-Villaverde at al (2015) study policy uncertainty in DSGE modelsfinding moderately negative effects while Pastor and Veronesi(2012 2013) model the theoretical links among fluctuationspolicy uncertainty and stock market volatility5
4 Dixit and Pindyck (1994) offer a review of the early theoretical literatureincluding papers by Oi (1961) Hartman (1972) and Abel (1983) that highlightpotentially positive effects of uncertainty Recent empirical papers include Bloom(2009) Bachman Elstener and Sims (2013) Bloom et al (2014) and Scotti (2016)with a review in Bloom (2014)
5 In other related work Julio and Yook (2012) find that investment fallsaround national elections Durnev (2010) finds that corporate investment becomesless responsive to stock prices in election years Brogaard and Detzel (2015) findthat policy uncertainty reduces asset returns Handley and Limao (2015) find thattrade policy uncertainty delays firm entry Gulen and Ion (2016) find negative re-sponses of corporate investment to our EPU index Koijen Philipson and Uhlig(2016) develop evidence that government-induced uncertainty about profitabilitygenerates a large equity risk premium for firms in the health care sector and redu-ces their medical RampD and Giavazzi and McMahon (2012) find that policy uncer-tainty led German households to increase savings in the run-up to the close andconsequential general elections in 1998
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
movements Causal inference is challenging because policy re-sponds to economic conditions and is likely to be forward lookingTo make progress we follow a micro and a macro estimation ap-proach First the micro approach exploits firm-level differences inexposure to certain aspects of policy mainly government pur-chases of goods and services We use micro data from the FederalRegistry of Contracts and data on government health care spend-ing to calculate the share of firm and industry revenues derivedfrom sales to the government Next in firm-level regressions thatinclude time and firm fixed effects and other controls we show thatfirms with greater exposure to government purchases experiencegreater stock price volatility when policy uncertainty is high andreduced investment rates and employment growth when policyuncertainty rises Adding the VIX as an explanatory variable (in-teracted with firm-level exposure to government purchases) westill find greater stock price volatility and falls in investment andemployment with heightened policy uncertainty which points to apolicy uncertainty channel at work rather than a broader uncer-tainty effect We also find that firms in the defense health careand financial sectors are especially responsive to their own cate-gory-specific EPU measures confirming their information value
These firm-level results are suggestive of a causal impact ofpolicy uncertainty on investment and employment in sectors thatrely heavily on government spending and in sectors like healthcare and finance with strong exposure to major shifts in regula-tory policy However the firm-level results offer limited guidanceabout the magnitude of aggregate effects in part because theycapture only a limited set of potential policy uncertainty channels
Our second approach fits vector autoregressive (VAR) modelsto US data and to an international panel VAR that exploits ourEPU indexes for 12 countries The US VAR results indicate thata policy uncertainty innovation equivalent to the actual EPU in-crease from 2005ndash2006 to 2011ndash2012 foreshadows declines ofabout 6 in gross investment 11 in industrial productionand 035 in employment The 12-country panel VAR yields sim-ilar results3 Although our results are not necessarily causal oneplausible interpretation of our micro and macro evidence is that
3 Stock and Watson (2012) use our EPU index to investigate the factorsbehind the 2007ndash2009 recession and slow recovery and come to a similar conclu-sionmdashnamely that policy uncertainty is a strong candidate to partly explain thepoor economic performance but causal identification is hard
QUARTERLY JOURNAL OF ECONOMICS1596
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nloaded from
policy uncertainty retards investment hiring and growth in pol-icy-sensitive sectors like defense finance healthcare and con-struction and these sectors are important enough for policyuncertainty to matter at the aggregate level
This article relates to at least three strands of literature Thefirst is research on the impact of uncertainty on growth and in-vestment Theoretical work on this topic dates at least toBernanke (1983) who points out that high uncertainty givesfirms an incentive to delay investment and hiring wheninvestment projects are costly to undo or workers are costly tohire and fire4 Of course once uncertainty recedes firms increasehiring and investment to meet pent-up demand Other reasons fora depressive effect of uncertainty include precautionary spendingcutbacks by households upward pressure on the cost of finance(eg Pastor and Veronesi 2013 Gilchrist Sim and Zakrajsek2014) managerial risk aversion (eg Panousi and Papanikolaou2012) and interactions between nominal rigidities and searchfrictions (Basu and Bundick 2012 Leduc and Liu 2015)
Second there is a literature focused explicitly on policy un-certainty Friedman (1968) Rodrik (1991) Higgs (1997) andHassett and Metcalf (1999) among others consider the detrimen-tal economic effects of monetary fiscal and regulatory policy un-certainty More recently Born and Pfeifer (2014) and Fernandez-Villaverde at al (2015) study policy uncertainty in DSGE modelsfinding moderately negative effects while Pastor and Veronesi(2012 2013) model the theoretical links among fluctuationspolicy uncertainty and stock market volatility5
4 Dixit and Pindyck (1994) offer a review of the early theoretical literatureincluding papers by Oi (1961) Hartman (1972) and Abel (1983) that highlightpotentially positive effects of uncertainty Recent empirical papers include Bloom(2009) Bachman Elstener and Sims (2013) Bloom et al (2014) and Scotti (2016)with a review in Bloom (2014)
5 In other related work Julio and Yook (2012) find that investment fallsaround national elections Durnev (2010) finds that corporate investment becomesless responsive to stock prices in election years Brogaard and Detzel (2015) findthat policy uncertainty reduces asset returns Handley and Limao (2015) find thattrade policy uncertainty delays firm entry Gulen and Ion (2016) find negative re-sponses of corporate investment to our EPU index Koijen Philipson and Uhlig(2016) develop evidence that government-induced uncertainty about profitabilitygenerates a large equity risk premium for firms in the health care sector and redu-ces their medical RampD and Giavazzi and McMahon (2012) find that policy uncer-tainty led German households to increase savings in the run-up to the close andconsequential general elections in 1998
ECONOMIC POLICY UNCERTAINTY 1597
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
ECONOMIC POLICY UNCERTAINTY 1599
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
QUARTERLY JOURNAL OF ECONOMICS1600
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
QUARTERLY JOURNAL OF ECONOMICS1602
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
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nF
ebru
ary
12
2015
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S
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spap
ers
inA
cces
sW
orld
New
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ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
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qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
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to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
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pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
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hh
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hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
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omyrsquorsquo
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an
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nce
rtain
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eria
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rto
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eth
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y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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policy uncertainty retards investment hiring and growth in pol-icy-sensitive sectors like defense finance healthcare and con-struction and these sectors are important enough for policyuncertainty to matter at the aggregate level
This article relates to at least three strands of literature Thefirst is research on the impact of uncertainty on growth and in-vestment Theoretical work on this topic dates at least toBernanke (1983) who points out that high uncertainty givesfirms an incentive to delay investment and hiring wheninvestment projects are costly to undo or workers are costly tohire and fire4 Of course once uncertainty recedes firms increasehiring and investment to meet pent-up demand Other reasons fora depressive effect of uncertainty include precautionary spendingcutbacks by households upward pressure on the cost of finance(eg Pastor and Veronesi 2013 Gilchrist Sim and Zakrajsek2014) managerial risk aversion (eg Panousi and Papanikolaou2012) and interactions between nominal rigidities and searchfrictions (Basu and Bundick 2012 Leduc and Liu 2015)
Second there is a literature focused explicitly on policy un-certainty Friedman (1968) Rodrik (1991) Higgs (1997) andHassett and Metcalf (1999) among others consider the detrimen-tal economic effects of monetary fiscal and regulatory policy un-certainty More recently Born and Pfeifer (2014) and Fernandez-Villaverde at al (2015) study policy uncertainty in DSGE modelsfinding moderately negative effects while Pastor and Veronesi(2012 2013) model the theoretical links among fluctuationspolicy uncertainty and stock market volatility5
4 Dixit and Pindyck (1994) offer a review of the early theoretical literatureincluding papers by Oi (1961) Hartman (1972) and Abel (1983) that highlightpotentially positive effects of uncertainty Recent empirical papers include Bloom(2009) Bachman Elstener and Sims (2013) Bloom et al (2014) and Scotti (2016)with a review in Bloom (2014)
5 In other related work Julio and Yook (2012) find that investment fallsaround national elections Durnev (2010) finds that corporate investment becomesless responsive to stock prices in election years Brogaard and Detzel (2015) findthat policy uncertainty reduces asset returns Handley and Limao (2015) find thattrade policy uncertainty delays firm entry Gulen and Ion (2016) find negative re-sponses of corporate investment to our EPU index Koijen Philipson and Uhlig(2016) develop evidence that government-induced uncertainty about profitabilitygenerates a large equity risk premium for firms in the health care sector and redu-ces their medical RampD and Giavazzi and McMahon (2012) find that policy uncer-tainty led German households to increase savings in the run-up to the close andconsequential general elections in 1998
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
ECONOMIC POLICY UNCERTAINTY 1599
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
QUARTERLY JOURNAL OF ECONOMICS1600
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
QUARTERLY JOURNAL OF ECONOMICS1602
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
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Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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Finally there is a rapidly growing literature on text searchmethodsmdashusing newspaper archives in particularmdashto measure avariety of outcomes Examples include Gentzkow and Shapiro(2010) Hoberg and Phillips (2010) Boudoukh et al (2013) andAlexopoulos and Cohen (2015) Our work suggests that newspa-per text search can yield useful proxies for economic and policyconditions stretching back several decades which could be espe-cially valuable in earlier eras and in countries with fewer datasources
Section II describes the data we use to construct our policyuncertainty indexes Section III evaluates our EPU measures inseveral ways and develops additional evidence about movementsin policy-related uncertainty over time Section IV investigateshow firm-level outcomes covary with policy uncertainty and thedynamic responses of aggregate outcomes to policy uncertaintyinnovations Section V concludes and offers some thoughts aboutdirections for future research
II Measuring EPU
We build indexes of policy-related economic uncertaintybased on newspaper coverage frequency6 We aim to capture un-certainty about who will make economic policy decisions whateconomic policy actions will be undertaken and when and theeconomic effects of policy actions (or inaction)mdashincluding uncer-tainties related to the economic ramifications of lsquolsquononeconomicrsquorsquopolicy matters for example military actions Our measures cap-ture both near-term concerns (eg when will the Fed adjust itspolicy rate) and longer term concerns (eg how to fund entitle-ment programs) as reflected in newspaper articles We first de-scribe the construction of our monthly and daily EPU indexes forthe United States from 1985 onward and then turn to indexes forspecific policy categories indexes for other countries and histor-ical indexes for the United States and United Kingdom
6 Earlier drafts of this article include index components based on (i) the pre-sent value of future scheduled tax code expirations and (ii) disagreement amongprofessional forecasters over future government purchases and consumer pricesHowever to extend our EPU measures over time and across countries we focushere on the newspaper approach while continuing to report the other componentsat httpwwwpolicyuncertaintycom
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IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
ECONOMIC POLICY UNCERTAINTY 1599
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cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1605
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
IIA US Economic Policy Uncertainty Indexes from 1985
Our modern monthly EPU index for the United States relieson 10 leading newspapers USA Today Miami Herald ChicagoTribune Washington Post Los Angeles Times Boston Globe SanFrancisco Chronicle Dallas Morning News New York Timesand Wall Street Journal We search the digital archives of eachpaper from January 1985 to obtain a monthly count of articlesthat contain the following trio of terms lsquolsquouncertaintyrsquorsquo or lsquolsquouncer-tainrsquorsquo lsquolsquoeconomicrsquorsquo or lsquolsquoeconomyrsquorsquo and one of the following policyterms lsquolsquoCongressrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederal Reserversquorsquo lsquolsquolegislationrsquorsquolsquolsquoregulationrsquorsquo or lsquolsquoWhite Housersquorsquo (including variants likelsquolsquouncertaintiesrsquorsquo lsquolsquoregulatoryrsquorsquo or lsquolsquothe Fedrsquorsquo) In other words tomeet our criteria an article must contain terms in all three cat-egories pertaining to uncertainty the economy and policy Weuse our audit study to select the policy terms as explained inSection IIIA
An obvious difficulty with these raw counts is that the over-all volume of articles varies across newspapers and time Thuswe scale the raw counts by the total number of articles in thesame newspaper and month We standardize each monthlynewspaper-level series to unit standard deviation from 1985 to2009 and then average across the 10 papers by month Finallywe normalize the 10-paper series to a mean of 100 from 1985 to2009 To be precise let Xit denote the scaled EPU frequencycounts for newspaper i = 1 2 10 in month t and let T1 andT2 denote the time intervals used in the standardization andnormalization calculations We proceed in the following steps(i) Compute the times-series variance 2
i in the interval T1 foreach paper i (ii) Standardize Xit by dividing through by thestandard deviation i for all t This operation yields for eachpaper a series Yit with unit standard deviation in the intervalT1 (iii) Compute the mean over newspapers of Yit in each monthto obtain the series Zt (iv) Compute M the mean value of Zt inthe interval T2 (v) Multiply Zt by (100M) for all t to obtain thenormalized EPU time-series index We use the same approachfor other countries and indexes
Figure I plots the resulting index which shows clear spikesaround the Gulf Wars close presidential elections the 911 ter-rorist attack the stimulus debate in early 2008 the LehmanBrothers bankruptcy and TARP legislation in late 2008 thesummer 2011 debt ceiling dispute and the battle over the lsquolsquofiscal
ECONOMIC POLICY UNCERTAINTY 1599
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nloaded from
cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
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index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
ECONOMIC POLICY UNCERTAINTY 1607
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
ECONOMIC POLICY UNCERTAINTY 1609
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
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an
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rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
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art
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art
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art
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Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
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esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
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ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
cliffrsquorsquo in late 2012 among other events and developments Somenotable political events do not generate high EPU according toour index For instance our EPU index shows no large spike inconnection with the partial federal government shutdowns fromNovember 1995 to January 1996 although those shutdowns re-ceived quite a lot of press coverage7
In addition to our monthly index we produce a daily EPUindex using the Newsbank news aggregator which coversaround 1500 US newspapers Newsbankrsquos extensive coverageyields enough articles to generate a meaningful daily countTaking monthly averages of our daily index it correlates at 085with our 10-paper monthly index indicating a high degree of sim-ilarity Because papers enter and leave the Newsbank archive andits count of newspapers expands greatly over time compositionalshifts potentially distort the longer term behavior of the daily EPU
FIGURE I
EPU Index for the United States
7 We find more than 8000 articles about these shutdowns in Newsbank ar-chives but less than 25 also mention the economy less than 2 mention uncer-tainty and only 1 mentions both Thus politically tumultuous episodes do notnecessarily raise EPU by our measure
QUARTERLY JOURNAL OF ECONOMICS1600
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nloaded from
index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
index Hence we focus on our 10-paper monthly EPU index butthe daily index provides a useful high-frequency alternative8
IIB EPU Indexes for Policy Categories
To create indexes for policy categories we apply additionalcriteria to those articles that contain our trio of terms about theeconomy policy and uncertainty The additional criteria involvethe presence of one or more category-relevant terms lsquolsquothe Fedrsquorsquolsquolsquocentral bankrsquorsquo lsquolsquointerest ratersquorsquo lsquolsquoinflationrsquorsquo and so on for the mon-etary policy category for example Online Appendix B reports thefull set of terms that define our 11 policy categories and subcat-egories We use Newsbank for the category indexes because itshigh text density facilitates measurement by time period andpolicy category As seen in Figure II the national security EPUindex spiked sharply in connection with the 911 attacks GulfWar I and the onset of Gulf War II The health care EPU index
FIGURE II
National Security and Health Care EPU Indexes
8 We update the daily EPU index at approximately 9 am EST each day andpost it at httpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1601
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nloaded from
rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
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TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
QUARTERLY JOURNAL OF ECONOMICS1604
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
ECONOMIC POLICY UNCERTAINTY 1605
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
ECONOMIC POLICY UNCERTAINTY 1615
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
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ge
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der
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ase
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DP
)fr
omth
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erve
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elp
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ers
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Ad
ata
for
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ent
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esan
dfo
reca
std
ata
for
the
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revalu
es
See
the
not
esto
Table
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rad
dit
ion
al
vari
able
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nit
ion
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rsbase
don
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ster
ing
at
the
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plt
00
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00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
rose sharply during the Clinton health care reform initiative in1993ndash1994 and has fluctuated at high levels from 2009 to 2014
Table I reports all 11 category-specific EPU indexes9 It alsoreports an overall economic uncertainty (EU) index that drops thepolicy requirement in the EPU index The first two rows reportaverage EU and EPU values for the indicated periods expressedrelative to the average EPU value from 1985 to 2014 For exam-ple the EU value of 2182 says the (scaled) frequency of EU arti-cles from 19851 to 19906 is somewhat more than twice theaverage frequency of EPU articles from 1985 to 2014 The next11 rows report relative frequency values for specific policy cate-gories and time periods For example the 548 value for lsquolsquoNationalSecurityrsquorsquo says the frequency of EPU articles during 20019 to200212 that mention national security matters is 548 of the1985ndash2014 average EPU frequency and 43 548
1285
of the EPU
frequency from 20019 to 200212Fiscal matters especially tax policy stand out in Table I as the
largest source of policy uncertainty especially in recent years Thefiscal policy EPU index rose from values near 33 in the precrisisyears to 615 in 20089 to 200912 and 783 from 2010 to 2013Health care policy is the second largest source of elevated EPU inrecent years Policy uncertainty related to financial regulations andentitlement programs also rose sharply after 2008 but from initiallylower levels Concerns related to sovereign debt and currency crisesare up by an order of magnitude during 2010 to 2013 but from sucha low base as to have little impact on the overall EPU index EPUconcerns related to monetary policy are important throughout the1985ndash2014 period but perhaps surprisingly they are not elevatedin recent years by our measure We interpret this result as a reflec-tion of low and stable inflation rates in recent years which appar-ently drive newspaper coverage more than disputes amongprofessional economists about unconventional monetary policies10
Several other researchers develop measures related to uncer-tainty about government behavior Marina Azzimonti (2015) con-structs a newspaper index of partisan conflict at the federal levelthat shows similarities to our EPU index but also notable
9 In contrast to Figure III which normalizes each category-specific EPUseries to 100 Table I expresses each category-specific EPU series as a percentageof the overall EPU frequency from 1985 to 2014
10 Other evidence also points to subdued levels of inflation uncertainty inrecent years See Nalewaik (2015) for a presentation and discussion of evidencebased on time-series models surveys and financial markets data
QUARTERLY JOURNAL OF ECONOMICS1602
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
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omyrsquorsquo
an
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nce
rtain
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requ
irem
ents
(ie
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rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
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ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
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icy
cate
gor
y
For
exam
ple
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edic
aid
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pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
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omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
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nce
rtain
tyrsquorsquo
crit
eria
can
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rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EI
EC
ON
OM
ICP
OL
ICY
UN
CE
RT
AIN
TY
BY
PO
LIC
YC
AT
EG
OR
YA
ND
TIM
EP
ER
IOD
1985ndash2014
Tim
ep
erio
d19851
ndash19906
19907
ndash19911
219921
ndash20018
20019
ndash20021
220031
ndash20076
20077
ndash20088
20089
ndash20091
220101
ndash20131
019851
ndash20141
2
Mid
-80s
toG
ulf
War
IG
ulf
War
I1990s
boo
mto
91
191
1att
ack
s2000s
boo
m
Earl
ycr
edit
cru
nch
Leh
man
coll
ap
seamp
rece
ssio
n
Fis
cal
pol
icy
batt
les
Over
all
aver
age
Over
all
econ
omic
un
cert
ain
ty2182
3498
1859
3269
1598
1848
3709
2521
2193
Eco
nom
icp
olic
yu
nce
rtain
ty1096
1419
881
1285
714
834
1321
1275
1000
Fis
cal
pol
icy
496
596
359
554
323
331
615
783
461
Taxes
399
484
319
512
302
314
569
681
403
Gov
ern
men
tsp
end
ing
ampot
her
227
268
121
173
85
66
171
332
171
Mon
etary
pol
icy
327
418
261
452
222
316
278
261
281
Hea
lth
care
70
154
149
184
131
134
293
393
173
Nati
onal
secu
rity
250
536
180
548
254
159
213
198
238
Reg
ula
tion
157
230
145
196
112
155
292
281
174
Fin
an
cial
regu
lati
on33
70
13
53
17
36
102
61
33
Sov
erei
gn
deb
tamp
curr
ency
cris
es14
06
23
05
04
03
04
39
16
En
titl
emen
tp
rogra
ms
73
126
115
187
88
82
153
247
124
Tra
de
pol
icy
38
40
63
26
17
20
14
21
38
Su
mof
pol
icy
cate
gor
ies
1425
2107
1295
2151
1152
1200
1863
2222
1506
Rati
oof
EP
Uto
over
all
EU
05
004
104
703
904
504
503
605
104
7
Not
es
Qu
erie
sru
nF
ebru
ary
12
2015
onU
S
new
spap
ers
inA
cces
sW
orld
New
sN
ewsb
an
k
usi
ng
the
cate
gor
y-s
pec
ific
pol
icy
term
sets
list
edin
On
lin
eA
pp
end
ixB
E
xce
pt
for
the
last
row
all
entr
ies
are
exp
ress
edre
lati
ve
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
lsquolsquoOver
all
econ
omic
un
cert
ain
tyrsquorsquo
qu
an
tifi
esth
efr
equ
ency
ofart
icle
sth
at
mee
tou
rlsquolsquoe
con
omyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
requ
irem
ents
(ie
d
rop
pin
gth
elsquolsquop
olic
yrsquorsquo
requ
irem
ent)
an
dis
als
oex
pre
ssed
rela
tive
toth
eaver
age
EP
Ufr
equ
ency
from
1985
to2014
Th
eca
tegor
y-
spec
ific
ind
exvalu
essu
mto
mor
eth
an
100
for
two
reaso
ns
firs
tw
eu
sea
few
pol
icy
term
sin
mor
eth
an
one
pol
icy
cate
gor
y
For
exam
ple
lsquolsquoM
edic
aid
rsquorsquoap
pea
rsin
the
term
sets
for
bot
hh
ealt
hca
rean
den
titl
emen
tp
rogra
ms
Sec
ond
a
new
spap
erart
icle
that
mee
tsth
elsquolsquoe
con
omyrsquorsquo
lsquolsquopol
icyrsquorsquo
an
dlsquolsquou
nce
rtain
tyrsquorsquo
crit
eria
can
refe
rto
mor
eth
an
one
pol
icy
cate
gor
y
ECONOMIC POLICY UNCERTAINTY 1603
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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departuresmdashfor example war and national security threats pro-duce declines in partisan conflict but increases in policy uncer-tainty Shoag and Veuger (2015) develop policy uncertaintyindexes for US states based on newspapers and other local indi-cators finding a strong negative link to state-level economic per-formance Fernandez-Villaverde et al (2015) estimate stochasticvolatility processes for US capital taxes labor taxes and govern-ment expenditures in a DSGE model finding correlations with ourEPU index of 044 031 and 067 respectively Jurado Ludvigsonand Ng (2015) derive uncertainty measures from common variationin the unforecastable components of macroeconomic indicatorswith their main measure correlating at 042 with our EPU index
IIC EPU Indexes for Other Countries
We also construct EPU indexes for 11 other major econo-mies11 As with our US index we first obtain a monthly countof articles that contain a trio of terms about the economy (E) policy(P) and uncertainty (U) We then scale the raw counts standard-ize each newspaperrsquos variation average across papers in a countryby month and normalize12 To help develop suitable E P and Uterm sets we consulted persons with native-level fluency and eco-nomics expertise in the relevant language and country Our P termset differs across countries for reasons both obvious (eg usinglsquolsquoBOJrsquorsquo for Japan) and idiosyncratic (eg inclusion of lsquolsquocustomsdutiesrsquorsquo for India) Online Appendix A lists the term sets and news-papers for each country-level EPU index We perform all searchesin the native language of the newspaper drawing on archives forseven newspapers in India six each in Canada and South Koreatwo each in France Germany Italy Japan Spain and the UnitedKingdom and one each in China and Russia13
Figure III displays the EPU index for Russia and OnlineAppendix Figures A1ndashA10 display the other country-level
11 We have recently developed additional EPU indexes for Australia andBrazil and assisted other researchers in developing EPU indexes for Holland andIreland We are open to proposals to developing indexes for other countries
12 For certain papers outside the United States search platform limitationspreclude us from scaling by the count of all articles In these cases we instead scaleby the count of articles containing the common and neutral term lsquolsquotodayrsquorsquo
13 Censorship and state control of the media present special challenges for Russiaand China For China we use the South China Morning Post the leading English-language newspaper in Hong Kong For Russia we rely on Kommersant which focuseson financial matters and is reportedly fairly free of government pressures
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
ECONOMIC POLICY UNCERTAINTY 1607
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
ECONOMIC POLICY UNCERTAINTY 1609
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
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indexes14 The Russian index responds to Russian military con-flicts major political developments in Ukraine the Russian finan-cial crisis in 1998 the Lehman Brothers failure in 2008 the 2013lsquolsquotaper tantrumrsquorsquo triggered by a perceived shift in US monetarypolicy and other developments While the Russian index is noisyreflecting our reliance on a single paper it shows that our approachyields useful information even for countries with strong restric-tions on press freedoms Looking at EPU indexes across 12 coun-tries we see that a wide variety of global and domestic factors drivemovements in our newspaper-based measures of policyuncertainty
IID Long-Span EPU Indexes for the United States and UnitedKingdom
We also construct long-span monthly EPU indexes back to 1900for the United States (drawing on digital archives for the Wall StreetJournal New York Times Los Angeles Times Boston Globe Chicago
FIGURE III
Index of EPU for Russia
14 We provide regular monthly updates of the country-level EPU indexes athttpwwwpolicyuncertaintycom
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
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Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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Tribune and Washington Post) and the United Kingdom (Times ofLondon and the Guardian) Based on informal audits and our reviewof word usage patterns in newspapers and other text sources weexpanded the E term set for the historical indexes to include lsquolsquobusi-nessrsquorsquo lsquolsquoindustryrsquorsquo lsquolsquocommercersquorsquo and lsquolsquocommercialrsquorsquo The expanded andnarrower E term sets yield very similar results in recent decades butthe expanded set seems to perform better in the early decades of thetwentieth century Based on results of the audit analysis describedlater we also expanded the P term set for the historical indexes toinclude lsquolsquotariff rsquorsquo and lsquolsquowarrsquorsquo
Figure IV and Online Appendix Figure A1 display the histor-ical EPU indexes for the United States and United KingdomIndexes for these two countries exhibit similarities and notabledifferences For example the elevation of EPU levels in the1930s is dramatic in the United States but modest in the UnitedKingdom which experienced a less severe output fall during theGreat Depression World Wars I and II are more prominent in theUnited Kingdom EPU series Gulf Wars I and II are associatedwith sharp EPU spikes in both countries The mid-1970s stands
FIGURE IV
US Historical Index of EPU
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
ECONOMIC POLICY UNCERTAINTY 1607
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
ECONOMIC POLICY UNCERTAINTY 1609
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nloaded from
We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
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out as a period of unusually high EPU in the United Kingdom(which suffered severe economic turmoil over this period includingthe IMF bailout and resignation of Prime Minister Harold Wilson)but not in the United States The post-1960s upward drift of EPUevident for the United States is absent for the United KingdomThis long-term US-UK comparison reinforces our earlier infer-ence that a broad mix of domestic and international developmentsinfluences the extent of policy uncertainty in any given country
III Evaluating Our Policy Uncertainty Measures
As remarked in Section I using newspaper-based measuresof EPU raises several issues about accuracy and potential biasThis section explains how we sought to address those issues Westart with a discussion of our audit study which relies on humanreadings of newspaper articles We use the audit study to selectour P term set compare the time-series behavior of human andcomputer-generated EPU indexes and collect other informationabout the nature of policy uncertainty Next we consider the roleof political slant in our EPU index Last we compare our news-paper-based index to other measures of uncertainty stock marketvolatility the frequency of uncertainty and policy uncertaintydiscussions in the Beige Books the share of the lsquolsquoRisk Factorsrsquorsquosection in firmsrsquo 10-K filings devoted to government policies andregulations and the frequency of large daily stock market movestriggered by news about government policy
IIIA Audit Study Based on Human Readings
We spent six months developing an audit process designed toevaluate and refine our US EPU indexes and another 18 monthsrunning a large-scale human audit study During the latterphase student teams working under our close supervision readand coded articles drawn from eight newspapers from 1900 to201215 We now describe the audit process and results
1 Audit Process We began by reading a few hundred news-paper articles typically in batches of 50 and comparing notes to
15 To construct our EPU index it suffices to recover counts of articles thatcontain certain terms In contrast we need full-text articles (machine-readablefiles or images) to carry out the audit study We could not access full-text articlesfor the Boston Globe or USA Today but we did so for the other eight newspapers
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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develop classification criteria an audit template in the form of anExcel file and the first draft of a guidebook for auditors Early onwe concluded that the largest payoff to an audit study involvedselecting and evaluating the lsquolsquopolicyrsquorsquo or P term set Accordinglythe formal audit study described below samples from the universeof articles that meet our lsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteriawhich concentrates our (expensive) human resources on samplesthat are highly germane for our purposes16
Next we conducted a pilot audit Working with a team ofstudent research assistants we read and coded 2000 randomlyselected newspaper articles To identify coding difficulties andweaknesses in our training materials we held weekly review ses-sions with the auditors and assigned about 20 of articles tomultiple auditors We used the pilot study to develop a trainingprocess and refine our audit guide The resulting 65-page guideserves as a training tool and reference manual in our full-scaleaudit It explains how to assess whether an article meets ourcriteria for economic uncertainty and economic policy uncertaintyand how to code each field in the audit template17 The pilot studyalso led to improvements in the audit process For example toensure that auditor-learning effects are not confounded with dif-ferences across papers or over time the full-scale audit studypresents articles to auditors in a randomized order
To conduct the full-scale audit we recruited and trained newteams of research assistants Each new auditor underwent atraining process that included a review of the audit guide andtemplate trial codings of at least 100 articles (not included inthe audit sample) a one-on-one meeting to review the trial cod-ings and additional trial codings and feedback when needed Wemet with the audit teams on a weekly basis to address questionsreview lsquolsquohard callsrsquorsquo and coding differences and maintain esprit de
16 Only 05 of the articles in our 10 leading newspapers satisfy both thelsquolsquoeconomyrsquorsquo and lsquolsquouncertaintyrsquorsquo criteria Thus the vast majority of all articles readby our auditors would be useless for selecting and evaluating our P term set if wewere to sample randomly from all newspaper articles
17 The guide includes coding instructions numerous examples and FAQs Forexample one of the FAQs asks lsquolsquoAre remarks about uncertain tax revenues groundsfor EPU=1rsquorsquo and answers lsquolsquoYes if the article attributes uncertainty about tax rev-enues partly or entirely to uncertainty about policy choices No if the articleattributes uncertainty about tax revenues entirely to uncertainty about economicconditions rsquorsquo The audit guide is available at httpwwwpolicyuncertaintycomAudit_Guidepptx
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corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
ECONOMIC POLICY UNCERTAINTY 1609
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
ECONOMIC POLICY UNCERTAINTY 1613
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
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Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
corps The auditors reviewed 12009 articles from 1900 to 2012that we selected using a two-stage approach18 First we specifieda target sample size (higher in 1985ndash2011 and certain key earlieryears) and then we randomly sampled a number of articles foreach newspaper and month To monitor audit quality and shar-pen incentives for careful work we randomly assigned about onequarter of the articles to multiple auditors
2 Selecting a P Term Set When an auditor codes an article asEPU = 1 he or she also records the policy terms contained in thepassages about EPU Using these records we identified 15 termsthat appear often in newspaper discussions of EPU from 1985 to2012 lsquolsquoregulationrsquorsquo lsquolsquobudgetrsquorsquo lsquolsquospendingrsquorsquo lsquolsquopolicyrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquotaxrsquorsquolsquolsquofederal reserversquorsquo lsquolsquowarrsquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoHouse ofRepresentativesrsquorsquo lsquolsquogovernmentrsquorsquo lsquolsquoCongressrsquorsquo lsquolsquoSenatersquorsquo lsquolsquopresi-dentrsquorsquo and lsquolsquolegislationrsquorsquo (and variants like lsquolsquoregulatoryrsquorsquo lsquolsquotaxationrsquorsquoetc) We then considered the approximately 32000 term set per-mutations with four or more of these policy terms For each per-mutation we generated computer assignments of EPUC = 0 or 1for each article in the sample By comparing these computer as-signments to the human codings we obtain sets of false negatives(EPUC = 0 EPUH = 1) and false positives (EPUC = 1 EPUH = 0) foreach permutation We chose the P term set that minimizes thegross error ratemdashthat is the sum of false positive and false nega-tive error rates This process yields our baseline policy term set forthe EPU index in Figure I lsquolsquoregulationrsquorsquo lsquolsquodeficitrsquorsquo lsquolsquoFederalReserversquorsquo lsquolsquoWhite Housersquorsquo lsquolsquoCongressrsquorsquo and lsquolsquolegislationrsquorsquo
Online Appendix Figures B1 to B6 display alternative EPUindexes constructed by dropping the six baseline terms one at atime Inspecting these figures it is apparent that the time-seriesbehavior of our EPU index is not particularly sensitive to anysingle policy term We also experimented with compound textfilters for example adding government AND tax to the baselineterm set Somewhat to our surprise we were unable to developsimple compound text filters that achieved a materially lowergross error rate than our baseline term set19
18 We reviewed more than 15000 articles across the preaudit phase pilotaudit auditor training exercises and full-scale audit but we draw only on the12009 articles in the full-scale audit for our analysis here
19 Our consideration of compound text filters focused on terms that materiallylowered the false negative rate when added to the baseline term setmdashat the cost of
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We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
We repeated this process to obtain the P term set for thehistorical EPU index in Figure IV which makes use of all sixterms in the P set for the modern index plus lsquolsquotariffrsquorsquo and lsquolsquowarrsquorsquoAdding these two policy terms accords well with the prominentrole of tariffs and tariff revenues in the first half of the twentiethcentury and with US participation in World Wars I and II theKorean War and the Vietnam War all of which involved muchgreater per capita rates of US military deployments and casual-ties than more recent military conflicts
3 Time-Series Comparison We chose the P term set for ourcomputer-automated EPU index to minimize the gross error raterelative to the human benchmark provided by our audit study Toassess the time-series performance implied by our automatedclassifications we now compare movements over time in humanand computer-generated EPU indexes To do so we compute thefraction of audit sample articles with EPUH = 1 in each quarterfrom 1985 to 2012 multiply by the EU rate for our 10 newspapersand normalize the resulting human EPU index to 100 over theperiod To obtain the corresponding computer EPU index we in-stead use the fraction of audit-sample articles with EPUC = 1Figure V compares these human and computer EPU indexesThere are differences between the two seriesmdashfor example alarger spike for the summer 2011 debt ceiling dispute in thehuman EPU indexmdashbut they are quite similar with a correlationof 086 Repeating the same type of comparison using annual datafrom 1900 to 2010 in Online Appendix Figure C1 we find a cor-relation of 093 between the human and computer EPU indexes
Figures V and C1 provide some assurance that our computer-automated EPU classifications track the actual time-series vari-ation in the intensity of concerns about EPU as judged by intel-ligent humans In this regard itrsquos worth stressing that our term-set selection criterion makes no use of time-series variation SoFigures V and C1 offer something of an independent check on the
even greater increases in the false positive rate Otherwise the term in questionwould be part of the baseline set lsquolsquoTaxrsquorsquo is the leading example in this regard As anexample of how adding lsquolsquotaxrsquorsquo to the policy term set yields a false positive see lsquolsquoCreditMarkets Little Change in Treasury Pricesrsquorsquo by Kenneth N Gilpin New York TimesFebruary 14 1991 The article discusses economic uncertainty and includes re-marks about taxable and tax-exempt securities but it contains no discussion ofpolicy matters
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
ECONOMIC POLICY UNCERTAINTY 1611
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
ECONOMIC POLICY UNCERTAINTY 1613
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
QUARTERLY JOURNAL OF ECONOMICS1614
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
ECONOMIC POLICY UNCERTAINTY 1615
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
QUARTERLY JOURNAL OF ECONOMICS1616
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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performance of our automated classification criteria Howeveritrsquos important to understand the limitations of these comparisonsThey incorporate our computer-automated EU assignments andmore fundamentally they rely on the content of newspaper arti-cles We use other methods as discussed later to assess the reli-ability of newspaper content for the purposes of constructing anEPU index
For downstream econometric applications we also care aboutthe time-series properties of the net error rate given by the dif-ference between the computer and human EPU index valuesCalculating this net error rate from the series in Figure V wefind that it is essentially uncorrelated with quarterly real GDPgrowth rates (correlation of002) and with the lsquolsquotruersquorsquo (iehuman) EPU rate in the audit sample (correlation of 0004)
4 Other Audit Results Our audit study also speaks to severalother questions related to our EPU index First only 5 of audit-sample articles with EPUH = 1 mainly discuss actual or prospec-tive declines in policy uncertainty Apparently reporters and
FIGURE V
Human and Computer EPU Indexes
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
ECONOMIC POLICY UNCERTAINTY 1613
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
ECONOMIC POLICY UNCERTAINTY 1615
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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Dow
nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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nloaded from
employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
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Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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editors do not regard falling uncertainty as particularly newswor-thy Second 10 of EPUH = 1 articles discuss uncertainty aboutwho will make future economic policy decisions 68 discuss un-certainty about what economic policies will be undertaken (orwhen) and 47 discuss uncertainty about the economic effectsof past present or future policy actions Third the share of EPUH
= 1 articles that discuss who will make future economic policydecisions triples in presidential election years compared withother years indicating that the nature of policy uncertaintyshifts substantially over the election cycle20 Fourth 32 ofEPUH = 1 articles mention policy matters in other countriesoften alongside domestic policy concerns
IIIB Political Slant in Newspaper Coverage of EPU
Our audit study does not address the potential for politicalslant to skew newspaper coverage of EPU If right-leaning (left-leaning) newspapers seriously overplay EPU when Democrats(Republicans) are in power political slant could distort measuredchanges in our index To investigate this issue we split our 10newspapers into the 5 most Republican and 5 most Democraticpapers using the media slant index of Gentzkow and Shapiro(2010) They assign slant values based on how frequently news-papers use words preferred by one party or the other in congres-sional speech For example a newspaper that frequently useslsquolsquodeath taxrsquorsquo lsquolsquopersonal accountsrsquorsquo and lsquolsquowar on terrorrsquorsquo (terms pre-ferred by Republicans) falls on the right side of their slant indexand a newspaper that frequently uses lsquolsquoestate taxrsquorsquo lsquolsquoprivate ac-countsrsquorsquo and lsquolsquowar in Iraqrsquorsquo (terms preferred by Democrats) falls onthe left side Online Appendix Figure C3 plots the lsquolsquoleftrsquorsquo andlsquolsquorightrsquorsquo versions of our EPU index They move together closely
20 We also find electoral cycle effects on the level of policy uncertainty in amulticountry setting In particular we merge our country-level EPU indexeswith data on the timing and closeness of democratic national elections from Julioand Yook (2012 2016) updating their data to cover recent elections This effortyields an unbalanced panel with 12 countries 62 national elections (none forChina) and 3263 monthly observations Using country fixed effects and an electiontiming indicator as explanatory variables EPU is on average 16 log points higherduring the month of national elections (t-statistic of 53 clustering errors at thecountry level) Including ln(1 + jpercentage voting gap between first- and second-place finishersj) as an additional regressor we find statistically significant evidencethat close elections yield a further elevation of policy uncertaintymdashbut the close-ness effect is small
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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Dow
nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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with a correlation of 092 This finding suggests that politicalslant does not seriously distort variation over time in newspapercoverage of EPU and is not a major concern for our index
IIIC Comparisons to Other Measures of Uncertainty and PolicyUncertainty
Another way to evaluate our EPU index is by comparisonwith other measures of uncertainty and policy uncertainty Themost obvious comparator is the VIX an index of 30-day option-implied volatility in the SampP500 index available since 1990 Asseen in Figure VI the VIX and the EPU index often move to-gether (correlation of 058) but they also show distinct variationFor example the VIX reacts more strongly to the Asian financialcrisis the WorldCom fraud and the Lehman Brothers collapsemdashevents with strong financial and stock market connections Incontrast the EPU index shows stronger responses to war in theGulf region the election of a new president and political battlesover taxes and government spendingmdashevents that clearly involvemajor policy concerns but also affect stock market volatility
FIGURE VI
US EPU Compared to 30-Day VIX
ECONOMIC POLICY UNCERTAINTY 1613
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
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ber 3 2016httpqjeoxfordjournalsorg
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Of course the two measures differ conceptually in severalrespects While the VIX reflects implied volatility over a 30-daylook-ahead period our EPU index involves no explicit horizonThe VIX pertains to uncertainty about equity returns while theEPU index reflects policy uncertainty and not just for equity re-turns The VIX covers publicly traded firms only which accountfor about one third of private employment (Davis et al 2007) Tothrow some light on the role of these differences we create anewspaper-based index of equity market uncertaintySpecifically we retain our E and U term sets but replace the Pterm set with lsquolsquostock pricersquorsquo lsquolsquoequity pricersquorsquo or lsquolsquostock marketrsquorsquo Theresulting index shown in Online Appendix Figure C2 correlateswith the VIX at 073 considerably higher than the EPU-VIXcorrelation21
This result tells us two things First it demonstrates that wecan construct a reasonable proxy for an important type of eco-nomic uncertainty using frequency counts of newspaper arti-clesmdasha proof-of-concept for our basic approach Second thestronger correlation of the newspaper-based equity index withthe VIX confirms that differences in topical scope between theVIX and the EPU index are an important source of distinct var-iation in the two measures
1 Other Text Sources We also consider uncertainty indicatorsbased on the Beige Book releases before each regularly scheduledmeeting of the Federal Open Market Committee (FOMC) TheBeige Book published eight times a year summarizes in roughly15000 words the views and concerns expressed by business andother contacts to the 12 regional Federal Reserve Banks Wecount the frequency of lsquolsquouncertainrsquorsquo in each Beige Book normal-ized to account for variation in word count22 We also read eachpassage that contains lsquolsquouncertainrsquorsquo to judge whether it pertains topolicy matters and if so we record the policy category
21 We make no effort here to develop an optimal term set for the news index ofequity market uncertainty something we are currently pursuing in other workInstead Online Appendix Figure C2 reflects our first attempt and can surely beimproved
22 That is we divide the raw frequency count by the number of words in theBeige Book and rescale to preserve the average frequency count per Beige Book overthe sample period
QUARTERLY JOURNAL OF ECONOMICS1614
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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Figure VII shows the resulting quarterly frequency countsper Beige Book (BB) It highlights many of the same shocks andpolicy developments as the EPU index in Figure I The quarterlytime-series correlation between the EPU index and the BB policyuncertainty indicator is 054 The BB policy uncertainty indicatorshows little immediate response to the financial crisis but beginsto rise in the second half of 2009 and is at highly elevatedlevels from 2010 to 2013 In a categorical breakdown analogousto Table I (not shown) the BBs also point to fiscal policy as themost important source by far of elevated policy uncertainty inrecent years Financial regulation and sovereign debt concernsfigure more prominently in the BBs than in newspapers In con-trast to newspapers (but rather unsurprisingly) the BBs almostnever mention monetary policy uncertainty
Figure VII also shows a policy uncertainty indicator based ontextual analysis of 10-K filings For each 10-K filing we countsentences in the Risk Factors section (mandatory since fiscalyear 2005) that contain one or more of the policy terms listed inOnline Appendix E We then divide by the total number of sen-tences in the Risk Factors section and average over firms by year
FIGURE VII
Policy Uncertainty Measures Based on Textual Analysis of the Fedrsquos BeigeBooks and Part 1A (Risk Factors) of Firmsrsquo 10-K Filings
ECONOMIC POLICY UNCERTAINTY 1615
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to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
to obtain the series in Figure VII23 Although the temporalcoarseness of the 10-K filings precludes fine-grained compari-sons our analysis reveals a strong upward drift after 2009 inthe degree to which firms express concerns about their exposureto policy-related risk factors24
2 Daily Stock Market Jumps Finally following BakerBloom and Davis (2015) we characterize all large daily moves(greater than j25j) in the SampP stock index from 1900 to 2012 Ineach instance we locate and read the next-day New York Timesand Wall Street Journal articles that cover the stock move Werecord the explanation(s) according to the article and classify itas policy-related or not The idea is that higher policy uncertaintyleads to a greater frequency of large equity market moves trig-gered by policy-related news As seen in Online Appendix FigureC6 we find precisely that The correlation of the annual fre-quency count of daily stock market jumps triggered by policynews and the annual version of the EPU index in Figure IV is078 The 1930s and the period during and after the GreatRecession stand out in both series
IIID Summary
In summary our audit study and comparison to other textsources and types of data indicate that our newspaper-based EPUindexes contain useful information about the extent and nature ofeconomic policy uncertainty Compared to other policy uncer-tainty measures newspaper-based indexes offer distinct advan-tages They can be extended to many countries and backward intime sometimes by a century or more For large countries like theUnited States it is feasible to construct useful newspaper-based
23 The average length of the Risk Factors section of 10-K filings has grownsteadily over time perhaps because firms are providing increasingly detailed dis-cussions in this regard For this reason we prefer to scale by the total number ofsentences so as not to overstate the rising importance of policy-related risk factors
24 Online Appendix Figure C5 reports another 10-K policy uncertainty indi-cator based on the fact that firms generally discuss risk factors in order of theirimportance to the firm Thus for each 10-K filing we calculate the percent of theRisk Factors section one must read before encountering a discussion of policy-re-lated risks Averaging across firms by year the mean value of this measure fallsfrom 252 for fiscal year 2005 to 170 for 2013 and the median falls from 152 to87 In other words the average firm perceives policy risks as increasingly impor-tant from 2005 to 2013 relative to other risks
QUARTERLY JOURNAL OF ECONOMICS1616
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
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indexes at a daily frequency and by region Newspaper-based in-dexes are readily disaggregated and parsed to develop category-specific indexes
IV Policy Uncertainty and Economic Activity
To investigate whether policy uncertainty matters for eco-nomic outcomes we take two complementary approaches Thefirst uses firm-level data yielding better causal identificationbut capturing only a limited set of impact channelsmdashgovernmentpurchases of goods and services and certain aspects of regulatorypolicy The second uses macro data in VAR analyses potentiallycapturing many channels but offering little assurance about theidentification of causal effects
IVA Firm-Level Outcomes and Policy Uncertainty
Our firm-level analysis considers option-implied stock pricevolatility as a proxy for firm-level uncertainty and investmentrates and employment growth as real activity measures Weuse US panel data on publicly listed firms and an identificationstrategy that differentiates firms by exposure to uncertaintyabout government purchases of goods and services To measurethis exposure we draw on two sources of information For firms inHealth Services (SIC 80) we use the government share of UShealth care expenditures in 2010 which we calculate as 438 inOnline Appendix F For all other industries we exploit micro datain the Federal Registry of Contracts from 2000 to 2013 as follows
As a first step we match the federal contracts database toCompustat firms using DUNS numbers and the names of theparent firm and their US subsidiaries25 This match yields theparent firmrsquos revenue derived from federal contracts which weallocate to three-digit SIC industries using industry codes andline-of-business data in Compustat We then aggregate revenuesand contract awards to obtain the ratio of federal purchases torevenues in each three-digit industry by year To smooth outhigh-frequency variation from lumpy contract awards we
25 We do so using Dunn amp Bradstreetrsquos US database of all public and privatefirms which includes a firm name DUNS number industry and ownership infor-mation In this way we capture federal contracts of the publicly listed parent firm(eg lsquolsquoGeneral Electricrsquorsquo) and contracts with subsidiaries of the parent firm (eglsquolsquoGeneral Electric Capital Servicesrsquorsquo and lsquolsquoUSA Instrumentsrsquorsquo)
ECONOMIC POLICY UNCERTAINTY 1617
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average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
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TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
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nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
average these ratios from 2000 to 2013 to obtain our exposuremeasure for each three-digit SIC At the top end firms operatingin the guided missiles and space vehicles and parts industry (SIC376) derive 78 of their revenues from sales to the federal gov-ernment The corresponding figure for selected other industrieswith high exposures to federal purchases is 39 for ordnance andaccessories (SIC 348) 27 for search detection navigation guid-ance amp aeronautical systems (SIC 381) 21 for engineering ser-vices (SIC 871) 20 for aircrafts and parts (SIC 372) 15 forship and boat building and repairing (SIC 373) 11 for blankbooks loose leaf binders and bookbinding (SIC 278) and 9 forheavy construction (SIC 160) Direct sales to the federal govern-ment are comparatively small in most other industries
In a second step we measure each firmrsquos exposure to govern-ment purchases as its revenue-weighted mean (across its lines ofbusiness) of the industry-level exposure measures calculated inthe first step If the firm operates in a single three-digit SIC thenits exposure measure equals the corresponding industry exposuremeasure We prefer this two-step approach because it may lessenthe scope for reverse causality and because industry-levelmeasures may better proxy for the firmrsquos ex ante exposure touncertainty about government purchases Our robustness inves-tigations below consider several other firm-level policy exposuremeasures
IVB Implied Stock Price Volatility
Table II displays results from regressing firmsrsquo 30-day im-plied stock price volatility on economic policy uncertainty Weobtain the implied volatility measure from Options Metricswhich calculates the 30-day volatility implied by firm-levelequity options These options have been traded since the mid-1990s on the Chicago Board of Options and Exchange (CBOE2014) and our data begin in 1996 We use this volatility measurein quarterly regressions to match the quarterly company ac-counts averaging implied volatility over all trading days in thequarter We run regressions on a sample that extends from 1996to 2012 and weight by firm sales giving more weight to the largerfirms that also tend to have more actively traded equity options
Column (1) reports a very basic specification that regresseslogged 30-day implied volatility on our EPU index and the ratio offederal government purchases to GDP a control for the first
QUARTERLY JOURNAL OF ECONOMICS1618
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
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Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EII
OP
TIO
N-I
MP
LIE
DS
TO
CK
PR
ICE
VO
LA
TIL
ITY
AN
DP
OL
ICY
UN
CE
RT
AIN
TY
Dep
var
log(3
0-d
ay
imp
lied
vol
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Log
(EP
U)
04
32
00
44
07
52
(00
10)
(00
13)
(00
27)
Log
(EP
U)
inte
nsi
ty02
15
02
28
05
45
00
82
(00
69)
(01
00)
(02
02)
(01
17)
Log
(VIX
)07
34
(00
16)
Log
(VIX
)
inte
nsi
ty
00
20
(01
17)
Log
(EU
)10
80
(00
27)
Log
(EU
)
inte
nsi
ty
03
01
(01
77)
Fed
eral
pu
rch
ase
sG
DP
193
0
77
5
174
0
(15
0)
(14
9)
(14
9)
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
294
5
297
0
299
3
310
8(1
27
2)
(123
6)
(126
6)
(132
4)
Nati
onal
secu
rity
EP
U
def
ense
00
48
(00
12)
Hea
lth
care
EP
U
hea
lth
00
71
(00
43)
Fin
an
cial
regu
lati
onE
PU
fin
an
ce01
44
(00
30)
Fir
man
dti
me
effe
cts
No
Yes
No
Yes
No
Yes
Yes
Not
es
Th
esa
mp
leco
nta
ins
1365
78
obse
rvati
ons
on54
60
firm
sfr
om1996
to2012
Th
ed
epen
den
tvari
able
isth
en
atu
ral
log
ofth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
Inte
nsi
tyis
the
firm
rsquosex
pos
ure
tofe
der
al
pu
rch
ase
sof
goo
ds
an
dse
rvic
esco
mp
ute
dby
the
two-
step
met
hod
des
crib
edin
Sec
tion
IV
Fed
eral
pu
rch
ase
sG
DP
isfr
omN
IPA
table
sL
og(E
U)
isth
elo
gof
the
new
spap
er-b
ase
dec
onom
icu
nce
rtain
tyin
dex
N
ati
onal
secu
rity
EP
U
def
ense
isth
en
ati
onal
secu
rity
EP
Uin
dex
from
Table
Im
ult
ipli
edby
1fo
rfi
rms
ind
efen
sein
du
stri
es(S
ICs
348
372
376
379
381
871)
an
d0
oth
erw
ise
an
dan
alo
gou
sly
for
hea
lth
care
EP
U
hea
lth
(SIC
s800
to809)
an
dfi
nan
cial
regu
lati
onE
PU
fin
an
ce(S
ICs
600ndash699)
All
regre
ssio
ns
wei
gh
ted
by
the
firm
rsquosaver
age
sale
sin
the
sam
ple
per
iod
S
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1619
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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Dow
nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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Dow
nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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moment of policy Log(EPU) is highly statistically significantwith the coefficient of 0432 indicating that a 1 EPU increaseis associated with a roughly 043 increase in firm-level impliedvolatility To put this magnitude in perspective our EPU indexrose by 856 log points (135) from 2006 to 2012 which implies anestimated upward shift of 37 log points (45) in average firm-level implied volatility The negative coefficient on the controlvariable in column (1) says that conditional on log(EPU) averagefirm-level implied volatility is lower when the ratio of federalpurchases to GDP is higher
Column (2) contains the key result We add a full set of firmand time fixed effects to control for unobserved factors that differacross firms and unobserved common factors that vary over timeThe log(EPU) and federal purchasesGDP terms drop out as theyare collinear with the time effects But we now interact thesemeasures with our firm-level measures of exposure to govern-ment purchases This specification tests whether implied volatil-ity at firms with greater exposure to government purchasescovaries more strongly with policy uncertainty We find verystrong evidence for this The coefficient of 0215 on thelog(EPU) intensity measure suggests that for every 1 increasein our policy uncertainty index a firm with say a 50 govern-ment revenue share would see its stock volatility rise by 01126
Column (3) evaluates to what extent our EPU measure tellsus anything different from the VIX index the most commonlyused proxy for overall economic uncertainty As noted inSection IIIC our EPU index and the VIX have a correlation co-efficient of 058 Adding the VIX in a specification without firm ortime effects reverses the sign of the EPU term while the coeffi-cient on the VIX is large (at 0734) and highly significant Thisresult is unsurprising since the VIX is the 30-day implied volatil-ity on the SampP500 index and it should be highly correlated withthe average 30-day implied volatility for publicly listed USfirms
Column (4) again adds time and firm fixed effects and wenow interact the EPU federal purchasesGDP and VIX measureswith the intensity of the firmrsquos exposure to government pur-chases Strikingly we now find that the EPU index has a large
26 Using a quite different empirical design and source of variation KellyPastor and Veronesi (2016) find evidence that policy uncertainty related to electionoutcomes also raises option-implied stock market volatility
QUARTERLY JOURNAL OF ECONOMICS1620
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and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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nloaded from
and significant coefficient while the VIX drops out entirelyCombining columns (3) and (4) reveals that the 30-day impliedvolatility is best explained by the VIX index for the average firmbut the EPU index provides additional explanatory power for theimplied volatility of firms in sectors with high government expo-suremdashlike defense health care engineering services and heavyconstruction
Columns (5) and (6) run a similar evaluation for the EUindex yielding similar results In column (5) we run a regressionwith the EPU EU and federal purchasesGDP measures but notime or firm fixed effects The EU index dominates with a largeand highly significant coefficient Again this result is not surpris-ingmdashthe EU index reflects the overall frequency of newspaperarticles about economic uncertainty without any stipulationthat these articles also discuss policy Column (6) adds time andfirm fixed effects and we again interact the key measures witheach firmrsquos exposure to government purchases As before theEPU measure dominates the general uncertainty measure inthe interacted specification with controls for firm and time effectsIndeed the EU measure now takes on the opposite sign In sum-mary while the EU index is more closely related to the averagefirm-level implied volatility in the specification (5) that excludesfirm and time effects the EPU index outperforms the EU index inexplaining firm-specific movements in option-implied volatility
Finally in column (7) we add category-specific EPU mea-sures from Section IIB for firms in the defense finance andhealth care sectors These category-specific measures potentiallycapture a broad range of impact channels including ones thatinvolve regulatory policy Reassuringly all three measuresyield positive statistically significant coefficients at the 1ndash10level For example implied volatility for defense firms respondsto the national security EPU index which jumped up in GulfWars I and II and after the 911 terrorist attacks (Figure II)Similarly implied volatility for firms in the health care sectorresponds to the health care EPU index which rose during theClinton health care reform initiative and in response to uncer-tainties surrounding the Affordable Care Act The large highlysignificant coefficient on the financial regulation EPU index isespecially noteworthy because direct federal purchases of goodsand services are minuscule in the finance sector Thus we seethis result as evidence that regulatory policy uncertainty drivesfirm-level stock price volatility
ECONOMIC POLICY UNCERTAINTY 1621
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nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
QUARTERLY JOURNAL OF ECONOMICS1626
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ber 3 2016httpqjeoxfordjournalsorg
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nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
These results imply that policy uncertainty accounts for sig-nificant variation in the cross-sectional structure of stock pricevolatilities To see this point consider the estimated changes infirm-level volatilities associated with the change in policy uncer-tainty from 2006 to 2012 Using the results in Table II column(7) we calculate these changes as (0082) (firmrsquos exposure togovernment purchases) (change in overall log EPU) plus (coef-ficient on category-specific log EPU) (change in category-specificlog EPU) Online Appendix Table A1 implements this calculationfor firms in selected industries yielding increases of up to 238 logpoints for financial firms and 139 log points for health care firmsmainly due to the run-up in their respective category-specificEPU indexes and 33 to 46 log points for firms in the ordnanceaircraft and engineering services industries mainly due to theirstrong exposures to government purchases and the rise in overallpolicy uncertainty Comparing JulyndashAugust 2001 to SeptemberndashOctober 2001 (before and after 911) and carrying out the sametype of calculations we find stock price volatility increases of 14ndash15 log points for firms in ordnance aircraft and engineeringServices 112 log points in the finance sector 75 log points inhealth care and tiny responses for firms in most other industriesHence the implied magnitudes are sizable for firms in industrieswith large policy exposures
Table III presents a wide range of additional robustness re-sults for specifications that include firm and year fixed effectsColumns (1) and (2) consider realized volatility and 182-day im-plied volatility to look at longer and shorter uncertainty horizonsyielding very similar results Column (3) adds forecasts from theSurvey of Professional Forecasters of government purchases rel-ative to GDP (interacted with firm-level exposure) as a controland column (4) uses actual future government purchases relativeto GDP (again interacted) as a control Column (5) replaces ourpreferred firm-level exposure measure (calculated by the two-step method described above) with a one-step measure calculateddirectly from the firmrsquos own sales to the federal governmentColumn (6) uses the Belo Gala and Li (2013) measure of indus-try-level exposure to government purchases which exploits theinput-output matrix to capture direct and indirect effects of gov-ernment purchases
Columns (7) and (8) in Table III consider two entirely differ-ent approaches to measuring firm-level exposure to governmentpolicy risks In column (7) we measure exposure by the slope
QUARTERLY JOURNAL OF ECONOMICS1622
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
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Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
TA
BL
EII
I
RO
BU
ST
NE
SS
CH
EC
KS
FO
RO
PT
ION
-IM
PL
IED
ST
OC
KP
RIC
EV
OL
AT
ILIT
YA
ND
PO
LIC
YU
NC
ER
TA
INT
Y
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Sp
ecifi
cati
onR
eali
zed
vol
ati
lity
182-d
ay
imp
lied
vol
ati
lity
Ad
dp
urc
hase
fore
cast
Ad
d12
qtr
sfu
ture
pu
rch
ase
sF
irm
-lev
elin
ten
sity
Bel
oet
al
(2013)
inte
nsi
tyB
eta
inte
nsi
ty10-K
risk
mea
sure
$500m
+sa
les
firm
s
Log
(EP
U)
inte
nsi
ty03
46
01
78
01
75
02
58
01
92
04
56
02
83
03
78
02
37
(00
89)
(00
73)
(00
70)
(00
86)
(00
45)
(01
01)
(01
18)
(02
17)
(00
71)
(fed
eral
pu
rch
ase
sG
DP
)
inte
nsi
ty
237
2
274
7
582
8
70
5
142
0
136
061
57
271
6
310
3(1
47
1)
(117
7)
(153
5)
(167
4)
(100
3)
(276
4)
(149
7)
(641
7)
(124
0)
(For
ecast
edfe
der
al
pu
rch
ase
sG
DP
)
inte
nsi
ty326
1
(62
7)
Fir
man
dti
me
effe
cts
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Obse
rvati
ons
1365
78
1365
78
1365
78
737
03
1326
28
1343
81
1333
04
1120
23
427
71
Nu
mber
offi
rms
54
60
54
60
54
60
30
70
52
19
53
74
53
28
37
17
10
56
Not
es
Th
esa
mp
lep
erio
dis
1996ndash2012
Th
ed
epen
den
tvari
able
isth
e30-d
ay
imp
lied
vol
ati
lity
for
the
firm
aver
aged
over
all
days
inth
equ
art
er
exce
pt
that
colu
mn
(1)
use
sth
ere
ali
zed
dail
yvol
ati
lity
over
the
qu
art
er
an
dco
lum
n(2
)u
ses
the
aver
age
182-d
ay
imp
lied
vol
ati
lity
S
eeth
en
otes
toT
able
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1623
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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nloaded from
coefficient in a regression of the firmrsquos daily stock returns on ourdaily EPU index from 1985 to 1995 which predates the sampleperiod in Table II Using this beta measure of policy risk expo-sure we again find positive and statistically significant effects ofEPU on firm-level volatility In column (8) we use the policy riskexposure measure derived from 10-K filings and plotted over timein Figure VII but now measured at the firm level (averaging overavailable years) We again find sizable effects of EPU on firm-level volatility but the coefficient on the log(EPU) interactionterm is less statistically significant partly due to a smallersample size27 and perhaps partly because this measure reflectsthe firmrsquos perceived exposure to policy risk factors from 2006 on-ward only whereas the regression sample starts in 1996 Column(9) restricts attention to firms with at least $500 million in annualsales These alternative measures and specifications all yieldhighly significant results similar to column (2) in Table II
Finally Online Appendix Table A2 returns to the baselinespecification in Table II column (2) and replaces the keylog(EPU) interaction term by log(EPUX) where X correspondsto the newspaper-based E (Economy) P (Policy) U (Uncertainty)EP EU or PU index These variants yield slope coefficients onthe key log(EPUX) intensity variable that are statistically in-distinguishable from the point estimate in Table II column (2)This highlights how it is the triple combination of the E P and Uterm sets in newspaper articles that drive our results rather thanthe frequency of the individual E P or U term sets or the precisescaling of the EPU index
IVC Investment Rates and Employment Growth
Table IV investigates the contemporaneous relationship be-tween policy uncertainty and firm-level investment rates and em-ployment growth28 We now have data from 1985 to 2012 and as
27 The sample shrinks for several reasons First the Securities and ExchangeCommission did not mandate a risk factors discussion before 2006 so we cannotobtain this measure for firms that delisted before 2006 Second some publicly listedfirms are exempt from the risk factors disclosure requirement and some may notcomply Third our web-scraping and automated text-reading methods may notcapture all relevant 10-K filings perhaps because some firms present their discus-sion of risk factors in an unusual format Fourth it is not always possible to matchdata from 10-K filings to Compustat Our match rates compare favorably to similarefforts by other researchers eg Campbell et al (2014) See Online Appendix E foradditional discussion
QUARTERLY JOURNAL OF ECONOMICS1624
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TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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Dow
nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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Dow
nloaded from
TA
BL
EIV
PO
LIC
YU
NC
ER
TA
INT
YA
ND
FIR
M-L
EV
EL
INV
ES
TM
EN
T
EM
PL
OY
ME
NT
AN
DS
AL
ES
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
end
ent
vari
able
IK
IK
IK
IK
E
mp
E
mp
E
mp
E
mp
R
ev
L
og(E
PU
)
inte
nsi
ty
00
32
00
32
00
24
00
29
02
13
02
27
02
20
02
20
01
28
(00
10)
(00
10)
(00
11)
(00
10)
(00
84)
(00
89)
(01
18)
(00
94)
(00
96)
F
eder
al
pu
rch
ase
sG
DP
in
ten
sity
82
0
80
4
121
2
88
5
107
9156
0
31
9109
9203
9
(28
6)
(28
6)
(31
8)
(28
7)
(74
1)
(80
4)
(125
6)
(78
8)
(94
3)
F
orec
ast
edF
eder
al
pu
rch
ase
sG
DP
in
ten
sity
10
1
46
5
(08
28)
(28
9)
L
og(d
efen
seE
PU
)
def
ense
firm
00
02
00
18
(00
04)
(00
17)
L
og(h
ealt
hca
reE
PU
)
hea
lth
firm
00
12
00
05
(00
02)
(00
25)
L
og(fi
n
reg
EP
U)
fin
an
cefi
rm
00
02
00
03
(00
01)
(00
05)
Per
iod
icit
yQ
uart
erly
Qu
art
erly
Qu
art
erly
Qu
art
erly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
Yea
rly
3yrs
Fed
pu
rch
ase
lead
sN
oN
oY
esN
oN
oN
oY
esN
oN
oO
bse
rvati
ons
7083
98
7083
98
4112
05
7083
98
1620
06
1620
06
1072
05
1620
06
1514
73
Nu
mber
offi
rms
216
36
216
36
135
63
216
36
171
51
171
51
115
05
171
51
157
49
Not
es
Th
esa
mp
lep
erio
dru
ns
from
1985
to2012
All
colu
mn
sin
clu
de
afu
llse
tof
firm
an
dti
me
effe
cts
IK
isth
ein
ves
tmen
tra
ted
efin
edas
Cap
Ex
t
Net
Pla
nt
Pro
per
tyan
dE
qu
ipm
ent
ethTHORN t
1
E
mp
isth
eem
plo
ym
ent
gro
wth
rate
mea
sure
das
emp
t
emp
t1
05
emp
tthorn
05
emp
t1
an
d
Rev
isth
eco
rres
pon
din
gre
ven
ue
gro
wth
rate
Fed
eral
pu
rch
ase
sG
DP
in
ten
sity
isth
ech
an
ge
infe
der
al
pu
rch
ase
sG
DP
from
NIP
Ata
ble
sin
the
nex
tqu
art
erin
qu
art
erly
spec
ifica
tion
san
din
the
nex
tyea
rin
an
nu
al
spec
ifica
tion
sm
ult
ipli
edby
the
firm
-lev
elp
olic
yex
pos
ure
inte
nsi
tyvari
able
F
orec
ast
edfe
der
al
pu
rch
ase
sG
DP
in
ten
sity
inst
ead
use
sth
em
ean
fore
cast
edch
an
ge
in(fe
der
al
pu
rch
ase
sG
DP
)fr
omth
eF
eder
al
Res
erve
Ban
kof
Ph
ilad
elp
hia
rsquosS
urv
eyof
Pro
fess
ion
al
For
ecast
ers
dra
win
gon
NIP
Ad
ata
for
the
curr
ent
valu
esan
dfo
reca
std
ata
for
the
futu
revalu
es
See
the
not
esto
Table
IIfo
rad
dit
ion
al
vari
able
defi
nit
ion
sS
tan
dard
erro
rsbase
don
clu
ster
ing
at
the
firm
level
plt
00
1plt
00
5plt
01
ECONOMIC POLICY UNCERTAINTY 1625
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nloaded from
before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
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before weight by firm sales We use our preferred measure of thefirmrsquos policy exposure intensity and a full set of time and firmeffects in all Table IV specifications Column (1) reports a regres-sion of the firm-level quarterly investment rate on ethlogethEPUTHORNTHORN Intensity and ethfederal purchases
GDP THORN Intensity The former has a sig-nificant negative coefficient of 0032 and the latter has a sig-nificant positive coefficient These results are in line withstandard predictions of investment-under-uncertainty modelsfor example Bernanke (1983) Dixit and Pindyck (1994) andBloom Bond and Van Reenen (2007)
To assess the magnitude of the estimated policy uncertaintyrelationship recall that the EPU index rose 856 log points from2006 to 2012 For a firm that sells 25 of its output to the federalgovernment this EPU change and the coefficient on log(EPU)Intensity in column (1) imply a one-time investment rate drop of068 percentage point (= 0856 0032 025 100) which isabout one sixth of the median firm-level investment rate of42 Although this calculation rests on a large EPU swingthere were several other large EPU moves during the sampleperiodmdashfor example an 82-point fall from 1992 to 1999 a 72-point rise from 1999 to 2001 and a 79-point fall from 2001 to2006 Hence for firms with high exposures to government pur-chases the estimates imply that swings in policy uncertainty in-volve material changes in investment rates
In column (2) we control for ethForecasted Federal PurchasesGDP THORN
Intensity given the forward-looking nature of investment deci-sions and obtain very similar results on the main coefficient ofinterest Adding controls for cash flow and Tobinrsquos q in column (2)yields a coefficient of 030 (010) on ethlogethEPUTHORNTHORN Intensity again
28 We focus on simple linear specifications that do not allow for rich responsedynamics or interactions between uncertainty and the responsiveness of outcomevariables to first-moment driving forces More sophisticated treatments of invest-ment behavior in these respects using other measures of uncertainty include Abeland Eberly (1996) Guiso and Parigi (1999) and Bloom Bond and Van Reenen(2007) There is value in applying these more sophisticated treatments to ourpolicy uncertainty measures but we leave that task to future research For aricher treatment of dynamics in firm-level investment rate responses to our EPUmeasure see Gulen and Ion (2016)
29 Using Compustat data our cash flow measure is operating income beforedepreciation expressed as a ratio to the book value of plant property and equip-ment The numerator of our Tobinrsquos q measure is the market value of equity(common and preferred shares) plus the book value of debt less the value of
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very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
ECONOMIC POLICY UNCERTAINTY 1627
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employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
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Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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Dow
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
very similar to column (1)29 In column (3) we include the averageethForecasted Federal Purchases
GDP THORN Intensity value in the next 12 quartersas an alternative control for future expectations and again find asignificant negative coefficient In column (4) we add the cate-gory-specific measures and find statistically significant negativeresults for terms involving log changes in the health care EPUindex and the financial regulation EPU index That is the fre-quency of newspaper articles about these types of policy uncer-tainty has additional explanatory power for the investment ratesof firms that operate in sectors most affected by these types ofpolicy
Columns (5) to (8) regress annual firm-level employmentgrowth rates on EPU changes (Compustat lacks quarterly em-ployment data) As with investment rates we find sizable andstatistically significant negative coefficients on policy uncertaintychanges for employment growth rates at firms with high exposureto government policy Consider again an 856 log point increase inthe EPU index and a firm that sells 25 of its output to the fed-eral government Given these values the coefficient of0213 onethlogethEPUTHORNTHORN Intensity in column (5) implies a one-time drop inthe annual employment growth rate of 46 percentage pointswhich is large relative to the mean annual growth rate of 34for firms in the sample The category-specific EPU variables donot have statistically significant effects on employment growthin contrast to the investment results
In column (9) we consider the impact on sales as a placebotest While the real options literature highlights how uncertaintysuppresses demand for input factors with adjustment costs theshort-run impact on output should be smaller according to thisclass of theories Consistent with this prediction the estimatedeffect of ethlogethEPUTHORNTHORN Intensity in column (9) is negative but notstatistically significant while the government purchases variableremains positive and significant Hence our results suggest thatincreases in policy uncertainty are associated with contempora-neous drops in investment rates and employment growth ratesfor firms in policy-exposed sectors but the near-term associationwith their output growth rates is more muted
Finally consider the relationship of policy uncertaintychanges to the cross-sectional structure of investment rates and
inventories and deferred tax credits and the denominator is the book value of plantproperty and equipment
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Dow
nloaded from
employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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Dow
nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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Dow
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policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
employment growth To do so we return to Online AppendixTable A1 and carry out calculations that parallel the earlierones for stock price volatility Working again with the policy un-certainty changes from 2006 to 2012 the implied quarterly in-vestment rate changes are modest except for a 29 drop for firmsin the health care sector while the annual employment changesare large in several sectors Given the change-on-change natureof the underlying regression specifications these results are one-time changes associated with the total change in the policy un-certainty measures from 2006 to 2012
IVD Policy Uncertainty and Aggregate Economic Activity
We now turn to VAR models that exploit time-series varia-tion at the country level Drawing causal inferences from VARs isextremely challengingmdashin part because policy and policy uncer-tainty can respond to current and anticipated future economicconditions Despite the challenges VARs are useful for charac-terizing dynamic relationships At a minimum they let us gaugewhether policy uncertainty innovations foreshadow weaker mac-roeconomic performance conditional on standard macro andpolicy variables
We start by fitting a VAR to monthly US data from January1985 to December 2014 To recover orthogonal shocks we use aCholesky decomposition with the following ordering the EPUindex the log of the SampP500 index the federal funds rate logemployment and log industrial production Our baseline VARspecification includes three lags of all variables Figure VIII de-picts the model-implied responses of industrial production andemployment to a 90-point upward EPU innovation equal insize to the EPU change from its average value in 2005ndash2006(before the financial crisis and recession) to its average value in2011ndash2012 (a period with major fiscal policy battles and high EPUlevels) Figure VIII shows maximum estimated drops of 11 inindustrial production and 035 in employment These responsesare statistically significant and moderate in size being about onethird as large as a typical business cycle fluctuation Since aggre-gate US investment data are not available at a monthly fre-quency we also estimated an analogous VAR model onquarterly data from 1985 to 2014 using the same type ofCholesky decomposition to identify shocks As shown in Online
QUARTERLY JOURNAL OF ECONOMICS1628
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Appendix Figure C7 gross aggregate investment exhibits a peakdecline of about 6 in response to a 90-point EPU innovation
Figure IX shows that the basic character of the impulse re-sponse functions is robust to several modifications of the specifi-cation variable set causal ordering and sample period six lagsinstead of three in the VAR a bivariate VAR (EPU and industrialproduction) a bivariate VAR with reverse ordering including theVIX (after the EPU index) including the EU index (after the EPUindex) dropping the SampP500 index including time trends andusing a sample period that runs from 1920 (when industrial pro-duction data become available) until 1984 These results are inline with the estimated effects of election uncertainty in Julio andYook (2012) and Durnev (2010) despite their distinct empiricalapproaches
A potential concern is whether and to what extent our esti-mated impulse response functions reflect bad news generallyrather than policy uncertainty shocks in particular Includingthe SampP500 stock market index in the VAR somewhat mitigatesthis concern given that stock markets are forward looking and
FIGURE VIII
Industrial Production and Employment Responses to EPU Shock VAR Fit toMonthly US Data
ECONOMIC POLICY UNCERTAINTY 1629
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
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ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
stock prices incorporate many sources of information Our base-line VAR also includes other lsquolsquofirst-momentrsquorsquo variables log em-ployment log industrial production and the fed funds rateStill the EPU index will likely embed first-moment informationnot captured by these variables To investigate this issue we alsoconsidered VARs that include the Michigan Consumer SentimentIndex30 When we place the Michigan index after the EPU indexin the causal ordering the estimated peak effect of a policy un-certainty shock on industrial production falls by about one third
FIGURE IX
US Industrial Production Response to an EPU Shock Alternative SamplesSpecifications and Identification Assumptions
30 The Michigan index reflects phone surveys of consumers and seeks to de-termine how consumers view the short-term economy the long-term economy andtheir own financial situation It takes the difference between the percent answeringpositively and the percent answering negatively for each of five questions thenaverages these differences and normalizes by the base period (December 1968)total The Michigan index has a correlation of0742 with our EPU index Wechose the Michigan index as the more commonly used consumer confidenceindex but other consumer confidence indices are highly correlated with theMichigan indexmdashfor example the Bloomberg confidence index has a correlationof 0943 with the Michigan index and the Conference Board confidence index has acorrelation of 0912 with the Michigan index
QUARTERLY JOURNAL OF ECONOMICS1630
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
(Online Appendix Figure C8) When we place the Michigan indexfirst in the causal ordering the peak effect shrinks by about halfThese results indicate that conditional on the other variables ourEPU index and the Michigan index contain overlapping informa-tion that has value for predicting future output and employmentmovements
Perhaps this result is unsurprising The Michigan index cap-tures a mix of first-moment and second-moment concerns as ex-pressed by households in survey data The relationship betweenlsquolsquoconfidencersquorsquo and uncertainty is murky and the two concepts aretightly linked at a deep level in some theoretical models for ex-ample Ilut and Schneider (2014) In any event the EPU indexhas several important advantages relative to consumer confi-dence indexes EPU indexes can be extended to many countriespushed back in time by a century or more in some countriescomputed in near real time on a daily basis and parsed inmany ways as illustrated by our category-specific EPU indexes
Figure X shows impulse response functions for a panel VARfit to monthly data from 1985 to 2014 on the 12 countries forwhich we have an EPU index The panel VAR specification par-allels the baseline specification that underlies Figure VI exceptthat we use the unemployment rate in place of log(employment)As before we rely on a Cholesky decomposition to identify shocksand display responses to an upward 90-point EPU innovationwhich is well within the range of EPU movements experiencedby the individual countries The 12-country panel VAR yields re-sults that are similar to the US results in Figure VIII In par-ticular the international panel VAR implies that a 90-point EPUinnovation foreshadows a peak drop in industrial production ofabout 1 and a rise in the unemployment rate of about 25 basispoints Online Appendix Figure C9 shows that the basic characterof the panel VAR results is robust to a variety of alternativespecifications variable sets and weighting methods Other re-searchers who use our EPU indexes in multicountry time-seriesanalyses also find that policy uncertainty shocks foreshadow de-teriorations in macroeconomic outcomesmdashexamples include theInternational Monetary Fund (2012) Colombo (2013) Klossnerand Sekkel (2014) and Nodari (2014)
Broadly speaking we see three ways to interpret this VAR-based evidence Under the first interpretation an upward EPUinnovation corresponds to an unforeseen policy uncertainty shockthat causes the worsening of macroeconomic performance
ECONOMIC POLICY UNCERTAINTY 1631
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
through real options effects cost-of-capital effects or other mech-anisms Second an upward EPU innovation captures bad newsabout the economic outlook that is not (fully) captured by theother variables in the VAR system and that bad news triggers arise in EPU that has harmful effects on the economy Under thisinterpretation EPU amplifies and propagates a causal impulsethat originates elsewhere Third EPU has no role as either animpulse or a propagation mechanism instead it simply acts as auseful summary statistic for information missing from the othervariables in our systemmdashlog(output) log(employment) or unem-ployment the policy rate log(SampP500) the VIX and consumersentiment31 This third interpretation is hard to fully reconcilewith our firm-level results which suggests that policy uncer-tainty has negative causal effects Itrsquos also worth noting that
FIGURE X
Responses to an EPU Shock in a Twelve-Country Panel VAR
31 Stock and Watson (2012) consider many more variables in much larger andricher time-series models They still find evidence that EPU innovations precededeteriorations in aggregate performance
QUARTERLY JOURNAL OF ECONOMICS1632
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
our VAR results may understate the importance of policy uncer-tainty shocks as a driving force even under the first interpreta-tion because other variables in the VAR system may respond tonews about future policy uncertainty shocks before they show upin the EPU measure
Clearly there is a need to develop a robust identificationstrategy for assessing the causal role of policy uncertainty in mac-roeconomic performance by for example exploiting close conse-quential democratic elections and exogenous sources of variationin policy uncertainty such as shifts in the outlook for conflict be-tween North and South Korea or events like the UK Brexit voteregarding participation in the European Union In additionlinear VAR systems may be overly restrictive in how theymodel EPU responses to other shocks Perhaps EPU rises in thewake of large negative shocks but responds relatively little tosmall ones Allowing for this type of asymmetry may lead to alarger role for EPU in amplifying and propagating the effects oflarge negative shocks It would also be useful to consider stochas-tic volatility models that allow EPU shocks to directly influencethe future volatility of other shocks including shocks to policyvariables We leave these tasks to future research
At a deeper level the causal role of policy uncertainty is po-tentially quite subtle Sound institutions and policy regimesfoster predictable policy responses even in the face of large neg-ative shocks In this way good institutions and policy regimeslessen the scope for policy to act as a source of uncertainty im-pulses or through uncertain policy responses to amplify andpropagate the effects of other shocks
V CONCLUSION
We develop new measures of economic policy uncertainty forthe United States and 11 other major economies We use thesenew measures to investigate the relationship of policy uncer-tainty to firm-level stock price volatility investment rates andemployment growth and to aggregate investment output andemployment Our findings are broadly consistent with theoriesthat highlight negative economic effects of uncertainty shocksThe results suggest that elevated policy uncertainty in theUnited States and Europe in recent years may have harmed mac-roeconomic performance They also point to sizable effects of
ECONOMIC POLICY UNCERTAINTY 1633
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
policy uncertainty on the cross-sectional structure of stock pricevolatilities investment rates and employment growth
From a methodological perspective we show how to tapnewspaper archives to develop and evaluate new measures of in-terest to macroeconomists financial economists economic histo-rians and other researchers In this regard itrsquos worth stressingthat newspapers are available for countries around the worldand they have circulated in similar form for decades in mostcountries and for centuries in some countries This ubiquity cou-pled with modern databases and computers offers tremendouspossibilities for drawing on newspaper archives to deepen ourunderstanding of broad economic political and historical devel-opments through systematic empirical inquiries
Supplementary Material
An Online Appendix for this article can be found at QJEonline (qjeoxfordjournalsorg)
Kellogg School of Management
Stanford University Center for Economic and Policy
Research Stanford Institute for Economic Policy
Research and National Bureau for Economic Research
Chicago Booth School of Business and National Bureau
for Economic Research
References
Abel Andrew lsquolsquoOptimal Investment under Uncertaintyrsquorsquo American EconomicReview 73 (1983) 228ndash233
Abel Andrew and Janice Eberly lsquolsquoOptimal Investment with CostlyReversibilityrsquorsquo Review of Economic Studies 63 (1996) 581ndash593
Alexopoulos Michelle and Jon Cohen lsquolsquoThe Power of Print Uncertainty ShocksMarkets and the Economyrsquorsquo International Review of Economics and Finance40 (2015) 8ndash28
Azzimonti Marina lsquolsquoPartisan Conflict and Private Investmentrsquorsquo NBER WorkingPaper 21723 2015
Bachmann Rudiger Steffen Elstener and Eric Sims lsquolsquoUncertainty and EconomicActivity Evidence from Business Survey Datarsquorsquo American EconomicJournal Macroeconomics 5 (2013) 217ndash249
Baker Scott Nicholas Bloom Brandice Canes-Wrone Steven J Davis andJonathan Rodden lsquolsquoWhy Has US Policy Uncertainty Risen since 1960rsquorsquoAmerican Economic Review Papers amp Proceedings 104 (2014) 56ndash60
Baker Scott Bloom Nicholas and Steve J Davis lsquolsquoWhat Triggers Stock MarketJumpsrsquorsquo paper presented at the ASSA Meetings January 2015
Basu Susanto and Brent Bundick lsquolsquoUncertainty Shocks in a Model of EffectiveDemandrsquorsquo NBER Working Paper 18420 2012
QUARTERLY JOURNAL OF ECONOMICS1634
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Belo Federico Vito D Gala and Jun Li lsquolsquoGovernment Spending Political Cyclesand the Cross Section of Stock Returnsrsquorsquo Journal of Financial Economics 107(2013) 305ndash324
Bernanke Ben S lsquolsquoIrreversibility Uncertainty and Cyclical InvestmentrsquorsquoQuarterly Journal of Economics 97 (1983) 85ndash106
Bloom Nicholas lsquolsquoThe Impact of Uncertainty Shocksrsquorsquo Econometrica 77 (2009)623ndash685
mdashmdashmdash lsquolsquoFluctuations in Uncertaintyrsquorsquo Journal of Economic Perspectives 28(2014) 153ndash176
Bloom Nicholas Stephen Bond and John van Reenen lsquolsquoUncertainty andInvestment Dynamicsrsquorsquo Review of Economic Studies 74 (2007) 391ndash415
Bloom Nicholas Max Floetotto Nir Jaimovich Itay Saporta and Stephen TerrylsquolsquoReally Uncertain Business Cyclesrsquorsquo working paper Stanford University2014
Born Benjamin and Johannes Pfeifer lsquolsquoPolicy Risk and the Business CyclersquorsquoJournal of Monetary Economics 68 (2014) 68ndash85
Boudoukh Jacob Ronen Feldman Shimon Kogan and Matthew RichardsonlsquolsquoWhich News Moves Stock Prices A Textual Analysisrsquorsquo NBER WorkingPaper 18725 2013
Brogaard Jonathan and Andrew Detzel lsquolsquoThe Asset Pricing Implications ofGovernment Economic Policy Uncertaintyrsquorsquo Management Science 61(2015) 3ndash18
Campbell John L Hsinchun Chen Dan S Dhaliwal Hsin-min Lu and LoganB Steele lsquolsquoThe Information Content of Mandatory Risk Factor Disclosures inCorporate Filingsrsquorsquo Review of Accounting Studies 19 (2014) 396ndash455
Chicago Board Options Exchange lsquolsquoVIX CBOE Volatility Indexrsquorsquo white paper2004
Colombo Valentina lsquolsquoEconomic Policy Uncertainty in the US Does It Matter forthe Euro Arearsquorsquo Economics Letters 121 (2013) 39ndash42
Davis Steven J John Haltiwanger Ron Jarmin and Javier Miranda lsquolsquoVolatilityand Dispersion in Business Growth Rates Publicly Traded versus PrivatelyHeld Firmsrsquorsquo NBER Macroeconomics Annual 21 (2007) 107ndash180
Dixit Avinash K and Robert S Pindyck Investment under Uncertainty(Princeton NJ Princeton University Press 1994)
Durnev Art lsquolsquoThe Real Effects of Political Uncertainty Elections and InvestmentSensitivity to Stock Pricesrsquorsquo working paper McGill University 2010
Federal Open Market Committee Minutes of the December (2009) Meeting avail-able at httpwwwfederalreservegovmonetarypolicyfomcmi-nutes20091216htm
Fernandez-Villaverde Jesus Pablo Guerron-Quintana Keith Kuester andJuan Rubio-Ramirez lsquolsquoFiscal Volatility Shocks and Economic ActivityrsquorsquoAmerican Economic Review 105 (2015) 3352ndash3384
Friedman Milton lsquolsquoThe Role of Monetary Policyrsquorsquo American Economic Review 58(1968) 1ndash17
Gentzkow Matthew and Jesse M Shapiro lsquolsquoWhat Drives Media Slant Evidencefrom US Daily Newspapersrsquorsquo Econometrica 78 (2010) 35ndash71
Giavazzi Francesco and Michael McMahon lsquolsquoPolicy Uncertainty and HouseholdSavingsrsquorsquo Review of Economics amp Statistics 94 (2012) 517ndash531
Gilchrist Simon Jae W Sim and Egon Zakrajsek lsquolsquoUncertainty FinancialFrictions and Investment Dynamicsrsquorsquo NBER Working Paper 20038 2014
Guiso Luigi and Guiso Parigi lsquolsquoInvestment and Demand Uncertaintyrsquorsquo QuarterlyJournal of Economics 114 (1999) 185ndash227
Gulen Huseyin and Mihai Ion lsquolsquoPolicy Uncertainty and Corporate InvestmentrsquorsquoReview of Financial Studies 29 (2016) 523ndash564
Handley Kyle and Nuno Limao lsquolsquoTrade and Investment under PolicyUncertainty Theory and Firm Evidencersquorsquo American Economic JournalPolicy 7 (2015) 189ndash222
Hartman Richard lsquolsquoThe Effects of Price and Cost Uncertainty on InvestmentrsquorsquoJournal of Economic Theory 5 (1972) 258ndash266
Hassett Kevin A and Gilbert E Metcalf lsquolsquoInvestment with Uncertain Tax PolicyDoes Random Tax Policy Discourage Investmentrsquorsquo Economic Journal 109(1999) 372ndash393
ECONOMIC POLICY UNCERTAINTY 1635
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
Higgs Robert lsquolsquoRegime Uncertainty Why the Great Depression Lasted So Longand Why Prosperity Resumed after the Warrsquorsquo Independent Review 1 (1997)561ndash590
Hoberg Gerard and Gordon Phillips lsquolsquoProduct Market Synergies andCompetition in Mergers and Acquisitions A Text-Based Analysisrsquorsquo Reviewof Financial Studies 23 (2010) 3773ndash3811
Ilut Cosmin and Martin Schneider lsquolsquoAmbiguous Business Cyclesrsquorsquo AmericanEconomic Review 104 (2014) 2368ndash2399
International Monetary Fund lsquolsquoWorld Economic Outlook Coping with High Debtand Sluggish Growthrsquorsquo IMF Press October 2012
mdashmdashmdash lsquolsquoWorld Economic Outlook Hopes Realities Risksrsquorsquo IMF Press April2013
Julio Brandon and Youngsuk Yook lsquolsquoPolitical Uncertainty and CorporateInvestment Cyclesrsquorsquo Journal of Finance 67 (2012) 45ndash83
mdashmdashmdash lsquolsquoPolicy Uncertainty Irreversibility and Cross-Border Flows of CapitalrsquorsquoJournal of International Economics 103 (2016) 13ndash26
Jurado Kyle Sydney Ludvigson and Serena Ng lsquolsquoMeasuring UncertaintyrsquorsquoAmerican Economic Review 105 (2015) 1177ndash1216
Kelly Bryan Lubos Pastor and Pietro Veronesi lsquolsquoThe Price of PoliticalUncertainty Theory and Evidence from the Option Marketrsquorsquo Journal ofFinance forthcoming (2016)
Klossner Stefan and Rodrigo Sekkel lsquolsquoInternational Spillovers of PolicyUncertaintyrsquorsquo Economics Letters 124 (2014) 508ndash512
Koijen Ralph S J Tomas J Philipson and Harald Uhlig lsquolsquoFinancial HealthEconomicsrsquorsquo Econometrica 84 (2016) 195ndash242
Leduc Sylvain and Zheng Liu lsquolsquoUncertainty Shocks Are Aggregate DemandShocksrsquorsquo Federal Reserve Bank of San Francisco Working Paper May 2015
Nalewaik Jeremy lsquolsquoRegime-Switching Models for Estimating InflationUncertaintyrsquorsquo Board of Governors of the Federal Reserve System WorkingPaper August 2015
Nodari Gabriela lsquolsquoFinancial Regulation Policy Uncertainty and Credit Spreads inthe United Statesrsquorsquo Journal of Macroeconomics 41 (2014) 122ndash132
Oi Walter lsquolsquoThe Desirability of Price Instability under Perfect CompetitionrsquorsquoEconometrica 29 (1961) 58ndash64
Panousi Vasia and Dimitris Papanikolaou lsquolsquoInvestment Idiosyncratic Risk andOwnershiprsquorsquo Journal of Finance 67 (2012) 1113ndash1148
Pastor Lubos and Pietro Veronesi lsquolsquoUncertainty about Government Policy andStock Pricesrsquorsquo Journal of Finance 67 (2012) 1219ndash1264
mdashmdashmdash lsquolsquoPolitical Uncertainty and Risk Premiarsquorsquo Journal of Financial Economics110 (2013) 520ndash545
Rodrik Dani lsquolsquoPolicy Uncertainty and Private Investmentrsquorsquo Journal ofDevelopment Economics 36 (1991) 229ndash242
Scotti Chiara lsquolsquoSurprise and Uncertainty Indexes Real-Time Aggregation ofReal-Activity Macro Surprisesrsquorsquo Journal of Monetary Economics 82 (2016)1ndash19
Shoag Daniel and Stan Veuger lsquolsquoUncertainty and the Geography of the GreatRecessionrsquorsquo AEI Economic Policy Working Paper 2015-07 2015
Stock James and Mark Watson lsquolsquoDisentangling the Channels of the 2007ndash2009Recessionrsquorsquo Brookings Panel on Economic Activity (Spring 2012) 81ndash135
QUARTERLY JOURNAL OF ECONOMICS1636
by guest on Novem
ber 3 2016httpqjeoxfordjournalsorg
Dow
nloaded from
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