Investment, Financial Frictions and the Dynamic Effects of ... · the U.K. (Gerko-Rey) and since 1991 for the U.S. (Gertler-Karadi). Monthly macroproxy-SVARover 1987-2015 using the

Investment, Financial Frictions and theDynamic Effects of Monetary Policy

James Cloyne Clodo Ferreira Maren Froemel Paolo SuricoUC, Davis Bank of Spain London Business School & BoE

ESCB Research Cluster 2Paris, November 7th 2018

The views expressed are those of the authors and do not necessarily reflect theviews of the Bank of Spain, the Euro-system, Bank of England, MPC, FPC or PRA.

Monetary transmission and financial frictions

Which type of firms are more sensitive to interest rate changes?

How much do these firms contribute to the aggregate response?

How can financial frictions be identified from balance sheet data?

Do financial frictions dampen or amplify monetary policy shocks?

Monetary transmission and financial frictions

Which type of firms are more sensitive to interest rate changes?

How much do these firms contribute to the aggregate response?

How can financial frictions be identified from balance sheet data?

Do financial frictions dampen or amplify monetary policy shocks?

Empirical challenges and our approach

1 Assess heterogeneity across firms’ characteristics.

→ Look at firm-level capital expenditure in U.K. and U.S.→ Explore variation by age, size, growth, leverage and Tobin’s Q.

2 Evaluate balance sheet position across groups of firms.

→ Exploit info on dividends and bond issuance decisions, creditscores, and equity prices.

3 Identify a series of monetary policy shocks.

→ U.K.: Gerko and Rey (2017); U.S.: Gertler and Karadi (2015).






















Main empirical finding I: heterogeneity

Younger firms exhibit significantly larger adjustments in investmentafter an interest rate change and drive the aggregate response.

Within these, the strongest adjustment is recorded amongyounger firms paying no dividends.

Peak effect occurs between two and three years after the shock.

Results are robust to controlling for other, more traditional, firms’characteristics.

Main empirical finding II: mechanism

Younger firms’ borrowing is more asset-based (thanearning-based)...

...and their investment relies more on external funds (debt)

After a contractionary monetary policy shock :interest payments and net worth respond homogeneously for allage groupsborrowing,though, drops by a larger and significant amount foryounger firms, especially those paying no dividends;sales (demand) responses are less pronounced and morehomogenous across age/dividends groups.

Consistent with financial frictions playing a quantitatively importantrole to amplify business cycle fluctuations through collateral values.











Outline

1 Data & approach

2 Heterogeneity

3 Financial frictions

4 Other transmission mechanisms

5 Concluding remarks

Data & approach Heterogeneity Financial frictions Other transmission mechanisms Concluding remarks

Firm-level data: Worldscope (U.K.), Compustat (U.S.)

Panel of annual (UK) / quarterly (US) data. Sample: 1987-2015.

Real variables: capital expenditure, age (years since incorporationor IPO), size (by asset value), growth (by assets), net sales.

Financial variables: leverage (debt over assets); Tobin’s Q; equity;cash flows; dividends paid; share prices; interest payments, bondissuance.

U.K.: 2,435 unique listed firms and around 27,000 (firms x years) obs.U.S.: 11,577 unique listed firms and 623,000 (firms x quarters) obs.

13 / 44


Investment: National Statistics vs Micro dataLevels Growth rates

Uni

ted

Kin

gdom

8.5

99.

510

10.5

log

inve

stm

ent

1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1Year

investment (ONS) investment (WS)

-.2-.1

0.1

.2.3

Yea

r-on

-yea

r gro

wth

rate

s

1985q1 1990q1 1995q1 2000q1 2005q1 2010q1 2015q1Year

investment (ONS) investment (WS)

Correlation .58 (pvalue = 0)

Uni

ted

Sta

tes

66.

57

7.5

8R

eal I

nves

tmen

t (lo

g)

1985 1990 1995 2000 2005 2010 2015Date

National Statistics Aggregated Micro Data

-30

-20

-10

010

20%

-20

-10

010

%

1985 1990 1995 2000 2005 2010 2015Date

National Statistics Aggregated Micro Data

14 / 44


Monetary policy shock series

High frequency surprises on short rate futures in a 30 minuteswindow around policy announcements, available since 2001 forthe U.K. (Gerko-Rey) and since 1991 for the U.S. (Gertler-Karadi).

Monthly macro proxy-SVAR over 1987-2015 using the highfrequency surprises as proxies to extract a SHOCK SERIES forthe full sample (see Mertens and Ravn, 2014; Ramey 2016).

Firms are matched with monthly interest rate surprises based ontheir respective filing dates.

15 / 44


Empirical specification: panel IV-Local Projections

Xj,t+h − Xj,t−1 = αhj +

G∑g=1

αhg × Dgh

j,t +G∑

g=1

βhg × Dgh

j,t × Rt + εj,t+h

Baseline Xj,t+h: capital expenditure over net PPE at horizon h;Dg: dummy for groups of age, size, leverage, paying dividends in previous year;Rt : interest rate in quarter t (slightly more convoluted for the U.K. annual data);Instrument: policy shocks in the accounting period t, extracted from proxy-SVAR.βh

g : impulse response for group g at forecast horizon h.

Additional firm-level Xj,t+h: borrowing, share prices, sales, interest payments;Additional aggregate Xt+h: industrial production, stock price index, credit spread.

Standard errors clustered by firms and time.

16 / 44


The average effect: capital expenditure over net PPE

United Kingdom United States

-.6-.4

-.20

.2.4

Perc

ent

0 1 2 3 4Year

-1-.5

0.5

Percent

1 4 8 12 16 20Quarters

Monetary Policy shock: 25 basis point increase. Standard errors: clustered by firms and time. Confidence band: 90%.

Consistent with the MACRO EVIDENCE using data from national statistics.Same message when reporting at the ANNUAL FREQUENCY

17 / 44

Outline

1 Data & approach

2 Heterogeneity




DESCRIPTIVE STATISTICS


Firms’ size, growth and revenues as function of AGE

Size Asset growth EBITDA

Uni

ted

Kin

gdom

3.5

44.

55

Line

ar P

redi

ctio

n

5 15 25 35 45 55Age

Size and age: Assets Book value

510

1520

Line

ar P

redi

ctio

n

5 15 25 35 45 55Age

Asset Growth and Age

89

1011

12%

5 15 25 35 45 55Age

Uni

ted

Sta

tes

4.5

55.

56

6.5

log(

asse

ts)

5 15 25 35 45 55Age

24

68

%

5 15 25 35 45 55Age

0.0

05.0

1.0

15.0

2.0

25%

5 15 25 35 45 55Age

Based on regressions of the variable of interest on age, squared age, sectorsXtime fixed effects (and size).

20 / 44


Firms’ financial characteristics as function of AGE

Credit scores Paying dividends Leverage

Uni

ted

Kin

gdom

6065

7075

80Li

near

Pre

dict

ion

5 15 25 35 45 55Age

Credit Score and Age

0.2

.4.6

.81

Pro

b(pa

id d

ivid

ends

)

5 15 25 35 45 55Age

Probabilities of Paying Dividends Last Year

1617

1819

20Li

near

Pre

dict

ion

5 15 25 35 45 55Age

Leverage and age: Book value

Uni

ted

Sta

tes

0.2

.4.6

.81

prob

abili

ty

5 15 25 35 45 55Age

low rating high rating

0.2

.4.6

.81

prob

abili

ty

5 15 25 35 45 55Age

2025

3035

40%

5 15 25 35 45 55Age

Based on regressions of the variable of interest on age, squared age, sectorsXtime fixed effects (and size).

21 / 44

Summary: younger firms tend on average to

be smaller in size

grow faster (in assets)

have less internal funds

have lowercredit scores (and probability of issuing bonds)probability of paying dividends

have lower leverage

have higher (average) Tobin’s Q

IMPULSE RESPONSE ANALYSIS


Dynamic effects of monetary policy on investment

Younger Middle-aged Older

Uni

ted

Kin

gdom

-2-1

0.5

Per

cent

1 2 3 4 5Years

-2-1

0.5

Per

cent

1 2 3 4 5Years

-2-1

0.5

Per

cent

1 2 3 4 5Years

Uni

ted

Sta

tes

-1.5

-.5-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-.5-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-.5-1

0.5

Percent

1 4 7 10 13 16 19Quarters

Monetary Policy shock: 25 basis point increase. Standard errors clustering: by firms and time. Confidence band: 90%.

24 / 44


Investment response by AGE & DIVIDENDS: U.K.

Younger OlderN

Odi

vide

nds

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

Div

iden

ds

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years


25 / 44


Investment response by AGE & DIVIDENDS: U.S.

Younger OlderN

Odi

vide

nds

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

Div

iden

ds

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters


26 / 44


Heterogeneity by age and dividends is ROBUST to...

1 Size charts

2 Firm’s growth charts

3 Leverage charts

4 Tobin’s Q charts

27 / 44

Outline

1 Data & approach

2 Heterogeneity




UNCONDITIONAL CORRELATIONS


Borrowing: asset-based vs. earning-based

∆bLTi,t =

G∑g=1

β1,gxDgi,tCOLLATERALi,t−1+G∑

g=1

β2,gxDgi,tEBITDAi,t−1+X ′i,tγ+εi,t

U.K. U.S.Young / nodiv Old / div Young / nodiv Old / div

COLLATERALt−10.0245*** 0.0117 0.0628*** 0.0376**(0.0092) (0.0093) (0.0131) (0.0141)

EBITDAt−1-0.0126 0.0694*** 0.0068 0.0484**(0.0114) (0.0191) (0.0160) (0.0183)

R2 0.4128 0.4128N 14 921 16 149Dependent variable: ∆ long-term debt

Note: the regression includes timeXsector and firm-level fixed effects. Standard errors are clustered by time and firm.

30 / 44



∆bLTi,t =

G∑g=1


g=1



COLLATERALt−10.0245*** 0.0117 0.0628*** 0.0376**(0.0092) (0.0093) (0.0131) (0.0141)

EBITDAt−1-0.0126 0.0694*** 0.0068 0.0484**(0.0114) (0.0191) (0.0160) (0.0183)



31 / 44



∆bLTi,t =

G∑g=1


g=1



COLLATERALt−10.0245*** 0.0117 0.0628*** 0.0376**(0.0092) (0.0093) (0.0131) (0.0141)

EBITDAt−1-0.0126 0.0694*** 0.0068 0.0484**(0.0114) (0.0191) (0.0160) (0.0183)



32 / 44


Financing investment: external vs. internal funds

Based on a regression of investment on net debt and net equity issuances, cash flows, sectorXtime dummies (and firms’ controls).

33 / 44

CONDITIONAL CORRELATIONS


EQUITY (MKT. VALUE) responds for textbfALL firms

Younger & NO dividends Older & Paying dividendsU

nite

dK

ingd

om

-8-4

-20

2Pe

rcen

t

1 2 3 4 5Year end (Q4)

-8-4

-20

2Pe

rcen

t

1 2 3 4 5Year end (Q4)

Uni

ted

Sta

tes

-8-6

-4-2

02

Percent

1 4 7 10 13 16 19Quarters

-8-6

-4-2

02

Percent

1 4 7 10 13 16 19Quarters


35 / 44


More homogenous INTEREST PAYMENTS responses


nite

dK

ingd

om

-10

-5-2

02

5pe

rcen

t

1 2 3 4 5year end

-10

-5-2

02

5pe

rcen

t

1 2 3 4 5year end

Uni

ted

Sta

tes

-4-2

02

4Percent

1 4 7 10 13 16 19Quarters

-4-2

02

4Percent

1 4 7 10 13 16 19Quarters


36 / 44


Larger BORROWING response for Younger/No Div.


nite

dK

ingd

om

-10

-5-2

02

Per

cent

1 2 3 4 5Years

-10

-5-2

02

Per

cent

1 2 3 4 5Years

Uni

ted

Sta

tes

-1.5

-1-.5

0.5

1Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

1Percent

1 4 7 10 13 16 19Quarters


37 / 44

Outline

1 Data & approach

2 Heterogeneity





More homogenous SALES responses


nite

dK

ingd

om

-.5-.2

50

.25

.5P

erce

nt

1 2 3 4 5Years

-.5-.2

50

.25

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-3-2

-10

12

Percent

1 4 7 10 13 16 19Quarters

-3-2

-10

12

Percent

1 4 7 10 13 16 19Quarters


39 / 44

Outline

1 Data & approach

2 Heterogeneity





Our contribution: SIX NEW FINDINGS...

1 Younger firms respond more than any other group and drive theaggregate response of investment to a monetary policy shock.

2 Results are more pronounced among young firms paying nodividends and robust to controlling for other firms’ characteristics.

3 Younger firms’ capex relies more on debt (than internal funds).

4 Younger firms’ debt is more asset-based (than earning-based).

5 Net worth and interest payments move for all firms.

6 Borrowing move most among younger firms.

7 Sales responses are homogeneous.

41 / 44










42 / 44










43 / 44


Our contribution: ...and AN INTERPRETATION

Younger firms tend to use external finance (mostly debt) to fundcapital expenditure, and tend to borrow against the value of theassets used as collateral.

A contractionary monetary policy shock raises credit spreads,affecting most firms relying on external finance macro evidence

A contractionary monetary policy shock pushes down asset prices& tighten their borrowing constraint, leading to a fall in investment.

YOUNG firms face significant financial frictions & financial acceleratorplays a key role in the transmission of monetary policy to investment.

44 / 44

Extra Slides

45 / 44

Monetary policy surprises and shocks

High-frequency Surprises Policy ShocksU

nite

dK

ingd

om

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015

Date

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

1985 1990 1995 2000 2005 2010 2015

Date

-4

-3

-2

-1

0

1

2

3

Uni

ted

Sta

tes

-.3-.2

-.10

.1%

1990m1 1993m1 1996m1 1999m1 2002m1 2005m1 2008m1 2011m1 2014m1Date

-3-2

-10

12

3%

1987m1 1991m1 1995m1 1999m1 2003m1 2007m1 2011m1 2015m1Date

Back

46 / 44

The response of selected macro variables

Investment Industrial productionU

nite

dK

ingd

om

0 2 4 6 8 10 12 14 16

Quarters

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6P

erce

nt d

evia

tion

12 24 36 48 60

Months

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Per

cent

Uni

ted

Sta

tes

0 2 4 6 8 10 12 14 16

Quarters

-1.6

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

Per

cent

dev

iatio

n

1 6 11 16 21 26 31 36 41 46

Months

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Per

cent

dev

iatio

n

Monetary Policy shock: 25 basis point increase. Standard errors clustering: by firms and time. Confidence band: 90%.47 / 44

The response of selected macro variables cont’ed

Employment Credit SpreadU

nite

dK

ingd

om

12 24 36 48 60

Months

-0.25

-0.2

-0.15

-0.1

-0.05

0

0.05

Per

cent

12 24 36 48 60

Months

-0.05

0

0.05

0.1

0.15

0.2

0.25

Per

cent

Uni

ted

Sta

tes

0 2 4 6 8 10 12 14 16

Quarters

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

Per

cent

dev

iatio

n

1 6 11 16 21 26 31 36 41 46

Months

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Per

cent

dev

iatio

n

Monetary Policy shock: 25 basis point increase. Standard errors clustering: by firms and time. Confidence band: 90%.Back to average effect Back to summary of results 48 / 44

The U.S. average effect reported at annual frequency

Quarterly Annual

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1-.5

0.5

Perc

ent

1 2 3 4 5End year (Q4)

Monetary Policy shock: 25 basis point increase. Standard errors: clustered by firms and time. Confidence band: 90%.Back to average effect

49 / 44

Investment responses by SIZE groups

Smaller Medium Larger

Uni

ted

Kin

gdom

-1-.5

0.5

Perc

ent

1 2 3 4 5Year end (Q4)

-1-.5

0.5

Perc

ent

1 2 3 4 5Year end (Q4)

-1-.5

0.5

Perc

ent

1 2 3 4 5Year end (Q4)

Uni

ted

Sta

tes

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters


50 / 44

’Controlling’ for (SMALLER) size

NO dividends & Younger No dividends & OlderU

nite

dK

ingd

om

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-2-1

01

23

Percent

1 4 7 10 13 16 19Quarters

-2-1

01

23

Percent

1 4 7 10 13 16 19Quarters

Monetary Policy shock: 25 basis point increase. Standard errors clustering: by firms and time. Confidence band: 90%.Back to robustness summary

51 / 44

Investment responses by ASSET GROWTH groups

Faster-growing Slower-growingU

nite

dK

ingd

om

-1-.5

0.5

Per

cent

1 2 3 4 5Years

-1-.5

0.5

Per

cent

1 2 3 4 5Years

Uni

ted

Sta

tes

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters


’Controlling’ for (FASTER) asset growth


nite

dK

ingd

om

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters


53 / 44

Investment responses by LEVERAGE groups

Lower Medium Higher

Uni

ted

Kin

gdom

-4-2

-10

2Pe

rcen

t

1 2 3 4 5Year end (Q4)

-4-2

-10

2Pe

rcen

t

1 2 3 4 5Year end (Q4)

-4-2

-10

2Pe

rcen

t

1 2 3 4 5Year end (Q4)

Uni

ted

Sta

tes

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters


54 / 44

’Controlling’ for (LOWER) leverage


nite

dK

ingd

om

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters


55 / 44

Investment responses by TOBIN’S Q groups

Higher LowerU

nite

dK

ingd

om

-10

.5P

erce

nt

1 2 3 4 5Years

-10

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters

-1.5

-1-.5

0.5

Percent

1 4 7 10 13 16 19Quarters


’Controlling’ for (HIGHER) Tobin’s Q


nite

dK

ingd

om

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

-4-2

-10

.5P

erce

nt

1 2 3 4 5Years

Uni

ted

Sta

tes

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters

-2-1

0.5

Percent

1 4 7 10 13 16 19Quarters


57 / 44

Documents

Investment, Financial Frictions and the Dynamic Effects of ... · the U.K. (Gerko-Rey) and since 1991 for the U.S. (Gertler-Karadi). Monthly macroproxy-SVARover 1987-2015 using the