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BecomingaSuccessfulResearcher1. Energy,Time,Smarts,Ambition,Perseverance,
Instructors,FellowStudents,…2. ToolsandSkills
– Mathtools(e.g.,dynamicoptimization)– Numericalanalysistechniques– Constructingandanalyzingeconomicmodels– StatisticsandEconometrics– theoryandmethods– **Datasources,datahandlingskills,measurement– **Empiricaldesigns– *Usingstatisticsandeconometricsinresearch– **Communicationskills– oralandwritten 1
Professor Steven Davis, University of Chicago, January 2018
3. **Knowledgeofthefieldincourse-relatedtopics:– Tobuildonwhatcamebefore– Toadvancethefrontierratherthanreinventthewheel– Forinspiration,andforexamplesofhow(not)todoresearch– Toconnectwithotherresearchers,andtopersuadethenthat
yourworkisinterestingandworthyoftheirattention
4. **Howtodevelopgoodideasfor(empirical)research,andhowtodeviseaneffectiveresearchstrategy:new,feasibleandwithhighpotentialtocastlightonimportanttopics,questionsandmodels
5. **CriticalJudgment– Assessingstrengthsandweaknessesinworkbyothersand
yourself– Identifying&droppingweakorimpracticalresearchideas– Refiningandpursuingproming/goodresearchideas 2
Forthemostpart,thiscoursefocusesonapplyingtoolsandtechniquesyou’velearnedelsewhereinyourstudies.Twopartialexceptions:1. Youwilllearnsomebasictext-basedmethods,apply(someof)
theminthehomeworkassignmentsandseeseveralexamplesoftext-basedmethodsineconomicsresearch.Examplesinclude:
– MarcoSammon’s tutorial,AnIntroductiontoPythonforTextAnalysis.– HomeworkAssignments1and2,whichaskyoutoconstructtext-based
measuresofriskexposuresfrom10-Kfilingsandusetheminresearchthatcastslightonfirm-levelequityreturnsandotheroutcomes.
– “MeasuringEconomicPolicyUncertainty,”mypaperwithBakerandBloomthatusesscaledfrequencycountsofnewspaperarticlestoquantifypolicy-relateduncertaintyandassessitseffectsonfirm-levelandaggregateperformance.
– SpeciallecturebyTarekHassan:Heappliesmachine-learningmethodstotranscriptsofconferencecallsaboutquarterlyearningsreportsforpubliclylistedfirms.Heusesthesemethodstoquantifyfirm-levelpolicyrisksandassesseffectsoninvestment,hiring,lobbyingandmore. 3
Theuseoftextasdataandtext-basedmethodsineconomicsresearchisexplodingrapidly.Seetherecentandexcellentsurveyof“TextasData”ineconomicsresearchbyGentzkow,KellyandTaddy (2017).Formoreonmachine-learningmethodsappliedtotextualdata,youmightstartbyperusingthematerialsandreferencesonMattTaddy’s website:http://taddylab.com.2. Youwillhaveampleopportunitytopracticeyouroralandwritten
communicationskillsinthiscourse.– MostPhDstudentsinEconomicsinvesttoolittleindevelopingand
polishingtheircommunicationskills,inmyview.– Ifyoucan’texpressyourideasclearly,it’sasignyouhaven’tthoughthard
enoughaboutyourideasandhowtoexplainthemtoothers.– Poorcommunicationskillsslowyourintellectualdevelopment.Ifyoucan’t
expressyourideasclearly,it’shardforotherstoprovideusefulfeedback.– Forthesamereason,youwillmakefasterprogressonyourdissertation
andyourjobmarketpaperifyouwriteandspeakclearly.– Seethecoursesyllabusformoreontheimportanceofcommunication.– GetStrunk&White’sTheElementsofStyle.Readit.Practiceitsprinciples
andrules.– Read“AnEconomist’sGuidetoVisualizingData,”bySchwabish intheJ.of
EconomicPerspectives,28,no1.Applyitslessonsandprinciples.4
• Governments routinely collect data on the employment, wages, revenues, industry and location of nearly all businesses, often at both the establishment and firm levels.– Data include taxpayer IDs and other identifiers
• Governments use these data for tax compliance purposes, as inputs to NIPA tables, as sampling frames for business surveys, and as raw material in the creation of widely used economic statistics.
Professor Steven Davis, January 2018
5
Large Business Databases • With (much!) work, these raw data can be turned
into core longitudinal research databases covering the universe of business firms and establishments– Establishments linked to parent firms– Both followed longitudinally (harder for firms)– In some cases, employers are also linked to their
employees, and both are followed longitudinally. – In some cases, businesses are also linked to their
owners, and both are followed longitudinally.6
Large Business Databases, 2 • Data from other sources – one-off and recurring
surveys, other administrative records, and proprietary data – are often merged with these core databases to investigate a specific issue.
• Advances in data storage and computing power greatly increase the scope for building these databases and using them as research tools.
• The next slide lists two core longitudinal U.S. business databases that emerge from different administrative record systems.
7
Two Large U.S. Databases 1. LBD:LongitudinalBusinessDatabase(Census)
– Firms&Establishments,linkedandtrackedovertime– Universalcoverage,annualobservationssince1976– Corevariables:employment,payroll,sales/revenue
(since1994),industry,location,companyname,IDs2. BED:BusinessEmploymentDynamics(BLS)
– Firms(intra-state)&Establishments,linkedandtrackedovertime
– Universalcoverage,quarterlysince1990– Corevariables:employment,industry,location,IDsTheLBDandBEDcoverbusinesseswith1+employees8
Other Business Databases • Other longitudinal business databases
derive from panel surveys conducted by statistical agencies, proprietary sources created for commercial purposes, etc.
• Some of the most exciting research ideas on the margin combine core business micro datasets like the LBD and BED with smaller, targeted sources that contain rich information about particular aspects of business behavior and outcomes. 9
Selected Business Databases3. COMPUSTAT: Rich source of data on firms
listed on major exchanges or traded OTC.– Heavily used in economics and finance research.– Considered in more detail later in this lecture– You will use in Homework Assignments #1 and #2
4. 10-K filings: Text documents that publicly listed firms file with the U.S. Securities & Exchange Commission:– Available for download through EDGAR.– You will use 10-K filings for text-based analysis in
HWs 1&2. 10
5. LRD:Alarge,rotatingpanelofmanufacturingplantswithannualcoveragebackto1972andcoverageinbusinesscensusyearsbeforethen– Large,richsetofmeasures– DrawsonAnnualSurveyofManufacturesandonce-
every-fiveyearsCensusofManufactures– Heavilyexploitedineconomicsresearch,e.g.,my
bookonJobCreationandDestruction.6. ManufacturingEnergyConsumptionSurvey
– Highlydetaileddataonenergy-relatedinputs,expenditures,technologiesandpracticesforroughly15,000manufacturingplants
– Every3or4yearssince1985– SeeDavisetal.(RESTAT,October2013)forinfo.
11
7. AnnualSurveysofBusinessforRetail,WholesaleandManufacturing(ASM).
8. Manufacturing&OrganizationsPracticeSurvey(MOPS)– asupplementtotheASM.– SeveralpapersinSectionsIIandVIIIofthereadinglist.– IamdeeplyinvolvedindesigningthepartofMOPSthat
dealswithelicitingsubjectiveprobabilitydistributionsoverfutureplant-leveloutcomes
9. VariousCensusesofBusiness– Extensivecoverageofestablishmentsandfirmsaboutonce
everyfiveyears,largesetofmeasures– CoverageofConstruction,FIRE,Retail,Wholesale,
Manufacturing,Mining,Servicesandmore.OnlytheManufacturingdatahavebeenheavilyworked.
– Firstavailableyearvariesbyindustrysector12
10.ILBD:LBD+revenue,industryandownershipforbusinesseswithnoemployees.(Davisetal,2009).
11.JobOpeningsandLaborTurnoverSurvey(JOLTS):BLSestablishment-levelsurveyofjobvacanciesandworkerflows(quits,layoffs,hires,etc.)
12.CapitalIQ:Commercialplatform,extensivedataonfinancialcharacteristicsofbusinessesandtheirsale(changesinownership,capitalstructure,etc.)www.capitaliq.com/Main3/ourproducts_platform.asp
13.Dealogic:Anothercommercialplatformwithdataonfinancialcharacteristicsofbusinesses,businesssales,etc.www.dealogic.com/ 13
• Datasets1and5-10areproductsoftheU.S.CensusBureau.Theysharecommonbusinessidentifiers,makingitrelativelyeasytomergetheestablishment-levelandfirm-leveldataacrossdatabases.TogetafullersenseoftherangeofbusinessmicrodatasetsthatresidewithintheU.S.Censussystem,perusewww.census.gov/ces/dataproducts/economicdata.html
• Likewise,datasets2and11areproductsoftheU.S.BureauofLaborStatistics,anditisreasonablystraightforwardtomergethem.
14
• Whenbusinessdatasetsdonotsharecommonidentifiers,theycanbemergedonthebasisofbusinessnameandaddress,industry,sizeandothercharacteristics.Thistypeofmergingprocesscaninvolveagreatdealofwork,andittypicallyyieldsmatchratesacrossdatasetswellbelow100percent.
• Fortunately,youcanoftenbuildonpreviouseffortsbyothers.Forexample,Davisetal.(2007)buildonMcCueandJarmin (2005)tomatchCompustat datatofirm-levelrecordsintheLBDthrough2005.
15
SelectedLongitudinalEmployer-WorkerDatasets14. LongitudinalEmployerHouseholdDynamics– LEHDmicrodata:quarterlyearningsforworkers,linked
toemployersandbothfollowedlongitudinally.– Datastartin1990forselectedstates,withmorestates
availableinmorerecentyears– Seewww.census.gov/ces/dataproducts/lehddata.html
15. SocialSecurityAdministration(SSA)longitudinalemployerworkerdata
– Annuallongitudinaldataontheearningsofprivatesectoremployeesfrom1974onwards,linkedtoemployerswhoarealsofollowedlongitudinally
16
SelectedLongitudinalEmployer-WorkerDatasets16. GermanSocialSecurityData– SimilartoLEHD
andU.S.SSAdata,butbetterintworespects:First,theyallowforarelativelycleandistinctionbetweenearningsandwages.Second,theyallowforamuchsharperdatingofjob-losseventsandotheremploymenttransitions.Strongrecentapplicationsinclude:
– Jarosch (2015)inananalysisoftheearningslossesofdisplacedworkers.
• Jarosch graduatedfromChicago,andthiswashisjobmarketpaper.It’snowR&RatEconometrica!
• Hispaperhasitsrootsinworkhedidforthiscourse! 17
18
A.Newmeasurementthatinformsourthinkingaboutfundamentalaspectsofeconomicbehavior.Examples:1. DavisandHaltiwanger(1992)andthebook byDavis,
HaltiwangerandSchuh(1996)developempiricalunderpinningsfortheflowapproachtolabormarkets.Slide20andSectionIIIinthecoursereadinglist.
2. In“VolatilityandDispersioninBusinessGrowthRates,”Davisetal.(2007)showthattrendsinbusinessvolatilitydiffereddrasticallybetweenpubliclylistedandprivatelyheldfirmsinthedecadesleadingupto2001.Coveredlaterinthislecture.
19
3. DavisandHaltiwanger(2014)documentasecularfallinthe“fluidity” ofU.S.labormarketsanddevelopevidenceonitsimplicationsforemployment.Laterinthecourse.
4. “FirmingupInequality”bySongetal.(2016)exploitslongitudinalworker-employerlinkeddatafromtheSSAtocastnewlightontheevolutionofU.S.earningsinequality.
5. “Capitalistsinthe21st Century”bySmith,YaganandZidar (2017)exploitslongitudinalfirm-ownerlinkeddatatodebunkthenotionthatpassiverentiershavereplacedtheworkingrichatthetopoftheU.S.incomedistribution.
Empirical Regularities Uncovered by the Flow Approach to Labor Markets
• Grossjobflowsacrossemployersareremarkablylarge– ingoodeconomictimesandbad,ineverymarketeconomy,invirtuallyeveryindustryandsector
• Workerflowsarelargeryet• Between-sectoremploymentshiftsaccountforasmallshareofjobflows.Idiosyncraticfactorspredominate.
• Jobandworkerflowsexhibitpronouncedcyclicalmovements.
• Workerandjobflowsaretightlylinkedinthecrosssectionandovertime.(Davis,Faberman andHaltiwanger,2012,J.ofMonetaryEconomics).
• AllstudentsofthiscourseshouldreadDavis,FabermanandHaltiwanger(2006).Thatwillgiveyoutheminimalbackgroundyouneed.Ifyouwanttodigdeeper,seethereadingsinSectionIII. 20
21
B.Uncoveringempiricalregularitiesthatchallengestandardtheoryandguidenewresearch.Example:Davis,Faberman andHaltiwanger(2013)useJOLTSmicrodatatostudyvacancies,hiresandvacancyyieldsinalargesampleofU.S.employers.• TheyshowthattheC-Sbehaviorofvacancyyieldsisinconsistentwithstandardequilibriumsearchtheories.
• Theydevelopcompellingevidencethatemployersuseotherinstruments,inadditiontovacancynumbers,tovarytheirpaceofhiring.
• Theirevidenceleadsthemtoformulateageneralizedmatchingfunctionthataccommodatesaroleforrecruitingintensity(pervacancy)andoutperformsthestandardmatchingfunction,H=M(U,V),inmanyways.
22
-5.0
-4.5
-4.0
-3.5
-3.0
-2.5
-2.0
-1.5
-1.0
-5.0 -4.0 -3.0 -2.0 -1.0
Log Daily Fill Rate
Log Hires Rate
Hires-Weighted Least SquaresSlope (s.e.) = 0.820 (0.006)R-squared = 0.993
Data points correspond to growth rate bins
This empirical relation should be flat according to Mortensen-Pissarides models!
• Wewillcoverthispaperon“TheEstablishment-LevelBehavioronVacanciesandHiring”indetaillaterinthecourse.
• See SectionVIinthecoursereadinglistforrelatedempiricalstudiesandandseveralpapersthatadvancetheoreticalexplanationsfortheempiricalrelationshiponthepreviousslideandotherevidenceinthepaper.
• Wewillalsoconsidersomework-in-progressthatexploitsproprietarydatathatlinksmillionsofjobseekers,applicationsandvacancypostings.
C.Identifyingandcorrectingsampledesignflawstoproducebetteraggregatestatistics.Subtleflawsinsampledesigncanleadtomajorerrorsinmeasurementandinference.•Remark:Asampledesignsuitableforestimatinglevelscanbeunsuitableforestimatingchangesorflows.•Example:TheoriginalsampledesignfortheJobOpeningsandLaborTurnoverSurvey(JOLTS)underweightedtailmassinthecross-sectionalgrowthratedensity.Asaresult,publishedJOLTSstatisticsunderstatedworkerflowsandjobopenings,andmisstatedtherelativecyclicalityofhiresvs.separationsandquitsvs.layoffs•Solution:ReweightthesampletoensurethatitreplicatestheemploymentgrowthratedensityintheBED. 24
Rates of Hires, Quits, Layoffs and Job Openings in the Cross Sectionof Establishment Growth Rates, JOLTS Micro Data, 2001-2006
Reproduced from Davis, Faberman, Haltiwanger and Rucker, 2010.
25
Reproduced from Davis, Faberman, Haltiwanger and Rucker, “Adjusted Estimates of Worker Flows and Job Openings In JOLTS,” NBER volume, 2010.
26
• Ouradjustedstatisticsforhiresandseparationsexceedthepublishedstatisticsbyaboutonethird.
• Ouradjustedlayoffrateismorethan60percentgreaterthanthepublishedlayoffrate.
• Ouradjustmentssignificantlyaltertime-seriespropertiesaswell.– Aggregatehiresare50percentmorevariablethanseparationsin(old)publishedJOLTSstatistics,asmeasuredbythevarianceofquarterlyrates,but20percentlessvariableaccordingtoouradjustedstatistics.
– Quarterlyquitratesaremorethantwiceasvariableaslayoffsinthe(old)publishedstatisticsbutequallyvariableaccordingtoouradjustedstatistics.
27
• BLSreviseditspublishedJOLTSstatisticsafterwecirculatedourpaper.Theirnewstatisticsaremuchclosertoour“adjustedestimates”.
• Dootherimportanteconomicstatisticssufferfromsimilarmeasurementproblemsthatdistortestimatedlevelsandtime-seriesproperties?– Investmentexpenditures,businessborrowing?
• Perhaps,butit’sunclear(tome).– Theerrorsintroducedbyflawedandill-suitedsampledesignscanbecorrectedexpostbyadjustingsurvey-basedestimatestomatchbenchmarkquantities.
– However,suitablebenchmarksarenotalwaysavailableor,whenavailable,notalwaysusedwell. 28
• Foraveryshortandeasy-to-digestdiscussionoftheweaknessesintheJOLTSsampledesign,whytheyledtoseriousproblemsinthepublishedJOLTSstatistics,andhowtocorrectforthesampledesignproblems,readDavis(2010).
• Whilethebasicideabehindthecorrectionisquitesimple,variouschallengescomplicatetheactualimplementation.Forthegorydetails,seeDavis,Faberman,HaltiwangerandRucker(2010).
29
D.ExploitingCross-SectionalRelationstoConstructSyntheticTime-SeriesData¨Statisticalmodelofcross-sectionalrelations+completequarterlydataonthecross-sectionaldistributionofestablishment-levelgrowthratesà syntheticdataforaggregateworkerflows
¤BEDdataonf+model-basedŵ for1990-2001¤BEDdataonf+JOLTS-basedwfor2001-2010.Thismethodrequiresagoodstatisticalmodelofhoww(workerflowrate)relatestog(employmentgrowthrate)inthecrosssection!SeeDavis,Faberman andHaltiwanger(J.ofMonetaryEconomics,2012)fordetails.30
Wt = ft (g)wt (g)g∑
¨ Layoffsmovewithjobdestruction.Quitsmoveoppositetoboth.
4.0
5.0
6.0
7.0
8.0
9.0
10.0
4.0
5.0
6.0
7.0
8.0
9.0
10.01990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
PercentofEmployment
JobDestruction
Layoffs
Quits
Synthetic JOLTS EstimatesActual JOLTS Estimates
This chart is reproduced from “Labor Market Flows in the CrossSection and Over Time,” by Davis, Faberman and Haltiwanger, Journal of MonetaryEconomics, January 2012.
E.Combiningquasi-experimentalvariationandlongitudinalbusinessdatatotesttheoreticalpredictionsandestimateresponsemagnitudes.Thereareincreasinglymanyexamplesofthissort,andtheydifferwidelyincharacter.Thisapproachrequiressomeingenuityinrecognizingausefulquasiexperimentandingatheringorconstructingdatatomergeintoexistinglongitudinalbusinessdatabases.Ifestimationofcausaleffectsisthegoal,thensuccessalsoturnspartlyonwhethertheidentificationstrategyispersuasiveandyieldsenoughpowertorecoverreasonablypreciseestimates oftheeffectsofinterest. 32
Examples• Estimatingtheeffectsofprivateequitybuyoutsonemployment,jobreallocation,productivityandcompensationperworker.– Davisetal.(2014)advancetheliteratureontheeffectsofPEbuyoutsinseveralrespects,butperhapstheirmostimportantinnovationinvolvesthesimultaneoususeoffirmandestablishmentdatatoestimatewithin-firmreallocationeffects.Coveredinnextlecture.
• Identifyingandquantifyingspatialspilloversontheproductivityofmanufacturingplants.– Greenstoneetal.(2010)compareoutcomesatincumbent
plantsincountiesthatwoncontestsfortheopeningofmajornewplantstooutcomesincountiesthatlost. 33
• Greenstone,ListandSyverson (2012)estimatetheeffectsofairqualityregulationsonTFPatU.S.manufacturingplants.Theirquasi-experimentalvariation:airqualityregulationsaremorestringentinsomecounties(andforsometypesofplants) than others.
• How large were the employment effects of credit market disruptions in the wake of the Lehman bankruptcy?– Chodorow-Reich (2014) investigates this question
using differences in lender health after the Lehman crisis as a source of variation in the availability of credit to borrowers. Another very successful JMP!34
• Howdoshockstoinvestmentopportunitiesatoneplantaffectinvestmentoutcomesatother plantsinthesamefirm?Doestheanswerturnonwhetherthefirmisfinanciallyconstrained?– GiroudandMueller(2015)addressthesequestions.TheyusetheLRDtomeasureproductivity,ownership,plantlocation,etc.
– Theyusenewairlineroutesthatreducetraveltimebetweenthefirm’sHQandcertainofitsplantstoobtainexogenousvariationinplant-levelinvestmentopportunities.“[A]reductionintraveltimemakesiteasierforHQtomonitoraplant,giveadvice,shareknowledge,etc.,raisingtheplant’smarginalproductivitythusmakinginvestmentinthe(treated)plantmoreappealing.”
– Cleverrecognitionofaninteresting,non-obviousquasi-experimentinthedata.
35
• Howsensitiveisstate-levelemploymenttostate-leveltaxesonbusinessincome?Howmuchoftheemploymentlossinonestate(inresponsetoaahikeinitstaxrateonbusinessincome)isoffsetbyemploymentgainsinotherstates?
• Giroud andRauh (2017)addressthesequestionsIn“StateTaxationandtheReallocationofBusinessActivity:EvidencefromEstablishment-LevelData,”usingtheLBDandfocusingonfirmsthatoperateinmultiplestates.
• Theymarshalandexploitmuchknowledgeofbusinesstaxationandthenatureofvariationinbusinesstaxratesacrossstates,timeandformofbusinessorganizations.Theyusethisknowledgetoisolateseveralsourcesofstate-specifictimevariationintheeffectivetaxrateoncorporateincome(forbusinessesorganizedas“C” corporations)andinthepersonaltaxrate(forunincorporatedenterprisesandbusinessesorganizedaspass-throughentities).36
F.Applicationsofemployer-workerandbusiness-ownerlinkedlongitudinaldatasets.Examplesincludeitems4and5onSlide19above.Perhapstheleadingapplicationsoflongitudinalemployer-workerlinkeddatasetsinvolvethestudyofearningslossesassociatedwithjobdisplacement.Jacobson,LalondeandSullivan(1993)provideanearlyandinfluentialstudythatexploitedemployer-workerdatasetsandappliedempiricaltechniquespreviouslydevelopedinresearchonprogramevaluation.MorerecentstudiesinthesamemoldincludeCouchandPlaczek (2010),VonWachter,SongandManchester(2009),andDavisandvonWachter (2011). 37
DavisandvonWachter (DvW)alsoshowthatworkhorsesearchandmatchingmodelsfailtoexplainthesizeandcyclicalityofthepresentvalueearningslossesassociatedwithjobloss.Observedlossesareseveraltimeslargerthanpredictedbystandardtheory.RecentpapersbyDopelt (2016),JungandKuhn(2016),Krolikowski(2017),Huckfeldt (2016),andJarosch (2015)considervariousmodificationstosearchandmatchingmodelstoaddressthisissue.WewillcovertheDvW papercarefullyanddiscusssomeoftherelatedliteraturelaterinthecourse.
38
NBERMacroeconomicsAnnual,2006
ByStevenJ.Davis,JohnHaltiwanger,RonJarmin andJavierMiranda
39
Startwithsomebackgroundandmotivationtoexplainwhywechosetoworkonthistopic:
• APuzzle:Twoprominentparallelliteratureshadproducedseeminglycontradictoryevidence.
• Thefactsatissuearerelevantforimportanttheoriesofreallocationandgrowth,frictionalunemployment,andeffectsofrisksharing,inputvarietyandcompetitiononproducervolatility.
• Comparedtopreviousworkontrendsinbusinessvolatility,wehadbetterdatasourcesandabetterempiricalstrategyforestablishingthefacts. 40
• Risingvolatilityofgrowthratesinsalesandemploymentamongpubliclytradedfirms– CominandMulani(2003),CominandPhilippon(2005)
• Risingvarianceintheidiosyncraticcomponentoffirm-levelequityreturns– Campbelletal.(2001),FamaandFrench(2004),manyothers
• Decliningexcessjobreallocationratesanddecliningratesofbusinessentryandexit– Davis,FabermanandHaltiwanger(2006),Faberman(2006),Jarmin,KlimekandMiranda(2003,2005),andFoster(2003)
For the full references, see “Volatility and Dispersion in Business Growth Rates.” 41
1
1( ) / 2it it
itit it
x xx x
g -
-
-=
+ 1/ 25
2,
4
1 ( )10it i t itt
t
s g g+=-
é ù= -ê úë ûå
42
Figure 1(a): Volatility, Publicly Traded Firms
Average Volatility of Sales Growth Rates
0.05
0.10
0.15
0.20
0.25
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
Simple Mean Sales-weighted mean
Based on COMPUSTAT Data
43
Average Volatility of Employment Growth Rates
0.06
0.10
0.14
0.18
0.22
0.26
1954
1956
1958
1960
1962
1964
1966
1968
1970
1972
1974
1976
1978
1980
1982
1984
1986
1988
1990
1992
1994
1996
1998
2000
Simple Mean Employment-weighted mean
Based on COMPUSTAT Data
44
7.0
8.0
9.0
10.0
11.0
12.0
13.0
14.0
15.0
1947 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002
Excess Reallocation Rate H-P Trend
LRD data spliced to other sources.45
13.0
13.5
14.0
14.5
15.0
15.5
16.0
16.5
17.0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Based on data from the BLS Business Employment Dynamics 46
• Reallocationandgrowth– Schumpeteriantheories(Aghion-Howitt,1998,Caballero,2006,manyothers)
– Industry-levelproductivitystudies(FHK,2001,etc.)• Theoriesoffrictionalunemployment (MP,etc.)• Effectsofdiversificationopportunitiesonrisktaking,investment,growth(Obstfeld,1994,etc.)
• Decliningaggregatevolatilityanditsconnectiontobusiness-levelvolatility– Financialmarketdevelopment(Thesmar-Thoenig,2004,Acemoglu,2005)
– Greatercompetitioningoodsmarkets(Philippon,2003,CominandPhilippon,2005,CominandMulani,2006)
– Expandinginputvariety(KorenandTenreyo,2006)
47
1, 1,2,... .
K
it ik kt itk
Z i Ng b e=
= + =å Agg. Growth Rate = α it
i=1
n
∑ γ it ,
where α i is firm i's share of activity
A Simple Reason to Anticipate that Aggregate andBusiness-Level Volatility Measures Trend in theSame Direction: Write the growth rate of firm i as a linear function of k mutually uncorrelated common shocks and an idiosyncratic shock:
48
2 2 2
1 1 1Firm Volatility =
n n K
it t it ik kti i k
ea s a b s= = =
é ù+ ê úë û
å å å
2 2 2 2 2 2
1 12
n n K n K
it it it ik kt it jt ik jk kti i k j i ka s a b s a a b b s
= > =
é ù é ù+ +ê ú ê úë û ë û
å å å å å
Then we can write the weighted mean of the firm-level growth rate volatility as:
And the volatility of the aggregate growth rate as:
Positive co-movements over time in the cross section imply that the last cross-product above is positive.
49
Reproduced from DavisAnd Kahn, 2008 JEP
50
Reproduced from DavisAnd Kahn, 2008 JEP
51
Anempiricalstudyoftrendsinthevolatilityanddispersionofbusinessgrowthrates.
Keythemes• Publiclytradedversusprivatelyheld
– Resolutionofpuzzleturnsonthisdistinction.• Majorshiftintheeconomicselectionprocessgoverningtherisk/volatilityprofileoffirmsthatgopublic
• Roleofchangesinbusinessage,size,cohortandindustrydistributions 52
• Annualobservations,1976to2001(nowthrough2014or2015)
• All establishmentsandfirmswithemployees• Employment,payroll,andothervariables• Employmentconcept: countofworkerssubjecttoU.S.payrolltaxesinpayperiodcovering12thofMarch
• SeeJarmin andMiranda(2002)onLBD.• Welimitanalysistononfarmsector
53
• Publiclytraded,listedfirms• Annualdatafrom1950to2004(sinceupdated)• EmploymentConcept: Numberofcompanyworkersreportedtoshareholders– Annualaverageorend-of-yearfigure– Includesemployeesofconsolidatedsubsidiaries,domesticorforeign
– Missingdata,measurementerror• WeuseCOMPUSTATforsomeexercisesand tosupplementtheLBDwithinformationonwhetherfirmsarepubliclytraded
54
Employment-weighted correlation = 0.83
Weighted MeanAbsolute Difference = 30%
55
Weighted Corr. = 0.54
56
Average Volatility of Firm Employment Growth Rates:COMPUSTAT and COMPUSTAT-LBD Bridge Compared
0.07
0.11
0.15
0.19
0.23
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
0.07
0.09
0.11
0.13
0.15
COMPUSTAT, unweighted Bridge, unweightedCOMPUSTAT, weighted Bridge, weighted
57
Average Volatility of Firm Employment Growth Rates:COMPUSTAT and COMPUSTAT-LBD Bridge Compared
0.07
0.11
0.15
0.19
0.23
1954
1959
1964
1969
1974
1979
1984
1989
1994
1999
0.07
0.09
0.11
0.13
0.15
COMPUSTAT, unweighted Bridge, unweightedCOMPUSTAT, weighted Bridge, weighted
à Restricting attention to publicly traded firms we canidentify in the LBD has no material effect on measuredfirm-level volatility.
58
Publicly Traded Firms, Unweighted
0.060.100.140.180.220.26
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
0.10.150.20.250.30.35
Firm Level Volatility Cross Sectional Dispersion (right axis)
Dispersion measured as Cross-Sectional Standard Deviation of Annual Employment Growth Rates
Using LBD employment data and COMPUSTAT to identifypublicly traded firms
59
Publicly Traded Firms, Employment Weighted
0.07
0.09
0.11
0.13
0.15
1950
1953
1956
1959
1962
1965
1968
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
0.05
0.1
0.15
0.2
0.25
Firm Level Volatility Cross Sectional Dispersion (right axis)60
• Largeseculardeclineinthecross-sectionaldispersionofemploymentgrowthratesandinthemagnitudeoffirm-levelvolatility– 40%dropinfirm-levelvolatilitysince1982– Patternholdsforallmajorindustrygroups
• Hugetrenddifferencesbetweenpubliclytradedandprivatelyheldfirms– LBDconfirmsrisingvolatilityanddispersionforpubliclytradedfirms
– Butoverwhelmedbydeclinesforprivatelyheld– “Volatilityconvergence” inallindustrygroups
61
Employment-Weighted Dispersion of Firm Growth Rates, Three-Year Moving Averages
0.150.250.350.450.550.650.750.85
1977 1980 1983 1986 1989 1992 1995 1998 2001Year
Stan
dard
Dev
iatio
n
Total Privately Held Publicly Traded 62
Employment-Weighted Volatility of Firm Growth Rates
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1977 1980 1983 1986 1989 1992 1995 1998 2001
Year
Ave
rage
Firm
Vol
atili
ty
Total Economy Privately Held Publicly Traded 63
σ it =zi ,t+τPit −1
⎛
⎝⎜⎞
⎠⎟(γ i ,t+τ −γ it
w
τ =−4
5
∑ )2⎡
⎣⎢⎢
⎤
⎦⎥⎥
1/2
,
64
1.5( )it it itz x x -= +5
,4
/ ,it it i tK P z tt
+=-
= å
zi,t+τ = Pit
τ =−4
5
∑
zit = Kit zit
Correcting a typoin the paper
Fix the scalingquantity overthe windowof the firm-level volatilitymeasure. By construction, the
sum of the rescaledweights add up to P.
# of years from t-4 to t+5for which zit > 0.
65
Modified Volatility, Employment Weighted
0.1
0.2
0.3
0.4
0.5
0.6
0.7
1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001
Year
Ave
rage
Firm
Vol
atili
ty
Total Economy Privately Held Publicly Traded 66
67
Industry 1982 1996 % ChangeMinerals 0.24 0.15 -39Const. 0.45 0.22 -52Manuf. 0.13 0.08 -38TPU 0.18 0.10 -44Wholesale 0.24 0.11 -53Retail 0.19 0.10 -46FIRE 0.18 0.14 -23Services 0.29 0.14 -53 68
Volatility Ratio: Privately Held to Publicly Traded
Industry 1982 1996 ChangeMinerals 4.5 1.4 -3.1Const. 6.4 2.8 -3.6Manuf. 3.7 1.7 -2.0TPU 3.9 2.0 -1.9Wholesale 2.9 1.5 -1.4Retail 3.3 2.1 -1.3FIRE 3.2 1.2 -2.1Services 1.7 1.0 -0.7 69
Employment-Weighted Dispersion of Establishment Growth Rates, Three-Year Moving Averages
0.5
0.55
0.6
0.65
0.7
0.75
0.8
1977 1980 1983 1986 1989 1992 1995 1998 2001Year
Stan
dard
Dev
iatio
n
Total Privately Held Publicly Traded 70
Modified Establishment Volatility, Employment Weighted
0.35
0.45
0.55
0.65
0.75
1977
1979
1981
1983
1985
1987
1989
1991
1993
1995
1997
1999
2001
Total Economy Privately Held Publicly Traded71
Volatility and dispersion differ by industry and especially by business size and age. To investigate whether shifts in activity across categories accounts for the volatility trends, we use a cell-based shift-share methodology were we compute the modified volatility for 448 age, size and industry cells: Age: entrants, 1, 2, 3, 4, 5, 6+ years of age (oldest Establishment), eight size categories, and eight industry groups-- Fixing the industry distribution of employment at 1982 cuts the 21% rise in modifiedvolatility among publicly traded by half. Fixing the age distribution of employment at 1982 accounts for 27 percent of volatility fall among privately held. This figure probably understates role of age distribution shift – see Table 4. 72
Employment Shares Among Publicly Traded Firms, Selected Industries
0
0.1
0.2
0.3
0.4
0.5
0.6
1975 1980 1985 1990 1995 2000 2005
Manufacturing Retail Trade FIRE Services 73
74
• Hugeupsurgeofnewlylistedfirms(Fama-French,2004)– #newlists(mostlyIPOs)jumpsfrom156peryearin1973-1979to549per
yearfrom1980-2001– 10%oflistedfirmsareneweachyearfrom1980to2001
• Newlistsbecameincreasinglyrisky(FF,andFinketal.)– Newlistsbecameincreasinglyriskyrelativetoseasonedfirmsasmeasured
byprofitabilityandreturns– FirmageatIPOfellfrom40yearsintheearly1960stolessthan5yearsby
thelate1990s.(Jovanovic-Rousseau2001data)• Upsurgeofnewlistsexplainsmostorallofriseinvolatilityof
idiosyncraticcomponentofequityreturns(FF,Finketal.)• Pointstoadeclineinthecostofequityforriskyfirmsandfirms
withdistantpayoffs
75
Figure 11. Modified Firm Volatility By Cohort, 1951-2004
0.05
0.1
0.15
0.2
0.25
1951 1955 1959 1963 1967 1971 1975 1979 1983 1987 1991 1995 1999 2003
1950s 1960s 1970s 1980s 1990s Left-Censored76
Figure 12. Share of Employment By Cohort, 1950-2004
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
19501954
19581962
19661970
19741978
19821986
19901994
19982002
1950s 1960s 1970s 1980s 1990s Left-Censored77
Modified Volatility among Publicly Traded Firms: The Role of Size, Age, Industry and Cohort Effects
0.08
0.1
0.12
0.14
0.16
0.18
1951
1954
1957
1960
1963
1966
1969
1972
1975
1978
1981
1984
1987
1990
1993
1996
1999
2002
No Controls Size Size, Age & Industry Cohort 78
79
1. Averagefirm-levelvolatilitydownsharply§ 23%declinefrom1978to2001usingourpreferredvolatility
measure(Figure6),29%since1987§ Patternholdsforallindustrygroups
2. Hugetrenddifferencesbetweenpubliclytradedandprivatelyheldfirms
§ Volatilityconvergenceinallindustrygroups3. Shifttoolderfirmsaccountsformuchofvolatility
declineamongprivatelyheldfirms4. Largeupsurgeandincreasinglyriskynatureofnewly
publicfirmsaccountsformostofvolatilityrise§ Theprocessgoverningselectionintothesetofpubliclytraded
firmsshiftedmarkedlyafter1979§ Casualempiricismsuggestsitshiftedagainafterearly2000s,
perhapsduetoSarbanes-Oxley.80
Simple selection story doesn’t fit the facts. It implies, contrary tothe evidence, a volatility rise in
both groups of firms and a rising share of employment at publicly traded firms. Neither occurred.
81
Our results also present a challenge to Schumpeterian theories of growth – the large decline in firm-level and establishment-level volatility that we document coincided with a period of impressive productivity gains.
-- This coincidence belies any close and simple positive relationship between productivity growth and the intensity of creative destruction, at least as measured by firm-based or establishment-based measures of volatility in employment growth rates.-- Perhaps there has been a big increase in pace of restructuring, experimentation and adjustment within firms.-- Perhaps more intense creative destruction among publicly traded firms, partly facilitated by easier access to public equity by high-risk firms, has been sufficient to generate the commercial innovations that fueled productivity gains throughout the economy. 82
1. Formoreonthechangingnatureofselectionintothesetofpubliclytradedfirms,readFama-French(2004)andBrownandKapadia(J.ofFinancialEconomics,2007)
2. Foraninterestingtheoreticalandempiricalefforttoexplainthedivergingtrendsofpubliclytradedandprivatelyheldfirms,see“ContrastingTrendsinFirmVolatility” byMathiasThoenig andDavidThesmar,AEJMacro,October2011.
3. OnSchumpeteriantheoriesofgrowththroughcreativedestruction,seeAghion andHowitt(1998)andCaballero(2006).Chapter2ofCaballero’sbookhasaverynicereviewoftheempiricalworkontheroleoffactorreallocationinproductivitygrowth.See,alsoBartlesman andDoms (2000)andFosteretal.(2001). 83
4. FormoreontheGreatModeration,seeDavisandKahn(2008)andreferencestherein.
5. ForastudythatdrawsonsimilardatasourcestoprovideanupdatedcharacterizationoftrendsinU.S.businessvolatility,see”TheSecularDeclineofbusinessDynamismintheUnitedStates”byDecker,Haltiwanger,Jarmin andMiranda.Theyshowthatbusinessvolatilityhastrendeddownwardsincetheearly2000sforprivatelyheldand publiclylistedfirms.
6. Wewillconsiderthelabormarketconsequencesofdecliningbusinessvolatilitylaterinthecourseinourdiscussionof“LaborMarketFluidityandEconomicPerformance."
84
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