Financial Frictions: Empirical Facts

Financial Frictions: Empirical FactsPrinceton Initiative

David Sraer, Princeton University

This lecture

I “Survey” lecture: overview of empirical research on financialfrictions and their impact on firm-level and macro leveloutcome.

I Empirically difficult task: supply vs. demand? Holy grail: findthe perfect empirical setting/instrument.

I Literature mostly focused on showing effects of financialfrictions.

I Precise theories (moral hazard vs. adverse selection vs. limitedcommitment) have yet to be tested (as opposed to insuranceindustry).

Roadmap

Firm-level evidenceFirm liquidity and investmentFinancial sector liquidity and investmentBeyond Investment

“Macro” evidence

Alternative research designs

Table of Contents




Neoclassical Theory of Investment

I Hayashi [1982] dynamic theory of investment: frictionlesscapital markets, perfect competition in product markets,convex adjustment costs c(I ,K ).

I Assume production function and adjustment costshomogenous of degree 1. (F (K ) = θK , c(I ,K ) = γ I 2

K ).(I

K

)t

= δqt = δQt

where qt = ∂V∂K (Kt) and Qt = Vt

Ktand Vt is the discounted

sum of future profit.

I Q is a sufficient statistic for investment. Only determinant ofinvestment in a project is the project NPV. Simple empiricalmodel.

The failure of the Neoclassical Theory?

I Simple reduced-form way of thinking about credit constraints:internal funds are less costly than external funds.

I If this is the case, then available liquidity (i.e. cash) shouldinfluence investment behavior beyond Q.

I Run on a large sample of public corporations in the US:(I

K

)it

= αi + αt + βQit + µ

(cash − flows

K

)it

+ εit

I Typically: µ > 0 and ***: a rejection of neo-classicalinvestment theory?

I Endogeneity bias: cash-flows are > 0 correlated with εit

I Measurement error on Q ⇒ upward-bias on µ.

Epistemology: Fazzari et al. [1988]

I Literature starts with Fazzari et al. [1988].

I Empirical strategy: estimate µ separately for firms that arelikely to be constrained and firms that are not. Compareestimates.

I Idea: bias should be the same, i.e. same correlation ofunobserved investment opportunities to cash flows in bothsample.

I Issue for implementation: proxy for likelihood of creditconstraints? Fazzari et al. [1988] use payout policy: largedividend payers vs. non-dividend payers.

I Later in the literature: firm size, credit ratings and variousindices.

167Steven M . Fazzari, R. Glenn Hubbard, and Bruce C. Petersen

Table 4. Effects of Q and Cash Flow on Investment, Various Periods, 1970-84a

Indepertdent r'ariczhle and

summary statistic Class 1 Clriss 2 Class 3

-R' 0.46 0.28 0.19

Source: Authors' estimates of eauatlon 3 based on a sample of firm data fiom Value Line data base. See text and Append~x B.

a . The dependent variable is the investment-capital ratlo (ZK),,, where I is investment in plant and equipment and K IS beginning-of-perlod capital stock Independent variables are defined as follows Q is the sum of the value of equlty and debt less the value of inventories, dlvlded by the replacement cost of the capital stuck adjusted for corporate and personal taxes (see Appendlx B); (CNK),, IS the cash flow-capital ratio. The equations were estimated using fixed firm and year effects (not reported) Standard errors appear in parentheses.

information between firms and outside investors can be made for the shorter time periods, 1970-79 and particularly 1970-75.

The results in table 4 show large estimated cash flow coefficients for

firms in class 1. As expected, the cash flow coefficient is largest (0.670)

in the earliest period, when most of these firms had yet to be recognized

by Value Line. The coefficient is the smallest (0.461) for 1970-84.

Furthermore, as the sample period is extended one year at a time from

1970-75 to 1970-84, the estimated cash flow coefficients for these firms

decline mon~tonical ly .~~ The cash flow coefficients in classes 2 and 3 are

36. The coefficients for the periods 1970-75 through 1970-84 are: 0.670,0.571, 0.566,

0.554,0.540,0.520,0.510,0.494,0.481,and 0.461. Thecorrespondingcoefficients for firms

in the third class are: 0.254, 0.176, 0.160, 0.173, 0.185, 0.204, 0.217, 0.221, 0.230, and

0.230. The coefficients of firms in class 2 always fall in the middle.

Issues.

I Larger measurement error in Q in constrained sample ⇒larger coefficient on cash in constrained sample (Erickson andWhited [2000]).

I Maybe “constrained” firms have more persistent investmentopportunities?

I Ideal solution: find shocks to cash flows that are exogenous toinvestment opportunity set (εit) and Q.

I Large literature devoted to finding the perfect instrument forcash flows. No “smoking gun”, but some suggestive evidence.

Oil shocks: Lamont [1997]

I Oil price shocks affect cash flows of oil division. If there arecapital markets, this means a decline in available cash for alldivisions in oil conglomerates.

I Empirical strategy: investment behavior of non-oil divisionfollowing large decline in oil prices.

I Hypothesis: with financing constraints (and internal capitalmarkets), a decline in oil prices ⇒ decline in available cash fornon-oil divisions ⇒ decline in capex for non-oil division.

I Identifying assumption: oil price shocks ⊥ non-oil divisioninvestment opportunities.

Assumptions in the data

Non-oil division investment decreases following oil shock.

Caveats: small sample size; downward bias (upper bound).

Mandatory Contributions to Pension Plans: Rauh [2006]

I Firms with defined benefit plans and funding deficits arerequired to contribute to the pension plans.Underfunding ⇒ negative cash outflows.

I Funding status mostly determined by variations in the value ofthe asset side of pension fund (stocks, bonds, etc.).

I Identifying assumption: variation in asset prices ⊥ investmentopportunities.

I Empirical strategy: Compare investment behavior of firmswith and without mandatory contributions (not observedcontribution).

Large estimate: 60c of investment per $ of extra cash

Caveat: Manipulation of pension fund returns?

Over vs. Under-investment

I That firms invest more when > 0 cash-flow shocks is notnecessarily a sign of inefficiency.

I “Empire builder” managers – agency costs of free cash flows.(LBOs)

I Blanchard et al. [1994] use cash from unexpected wins incorporate lawsuits as an instrument for cash flows. Somewhatconvincing. Caveat: 13 observations – case studies.

I Finding: extra cash mostly invested in “pet” projects(corporate acquisitions).

I Welfare analysis essentially lacking from the literature (⇔hard to empirically pin down micro-mechanism at play(adverse selection, moral hazard, limited commitment, etc.).

Cash is not the Only Source of Liquidity

I Remember the motivation for cash-flows in the investmentequation: “liquidity” position of the firm should not affect itsinvestment decision.

I Firms’ tangible assets are another source of liquidity: providecollateral to borrow against. Theoretical foundation: Hart andMoore [1994].

I Empirical question: does collateral value affect corporateinvestment beyond Q? If so, this is another rejection ofneo-classical theory.

I Similar challenge: Find variations in the value of collateralthat are orthogonal to investment opportunities.

Liquidation values affect financial contracts

I Benmelech et al. [2005] use zoning regulation to determinethe collateral value of commercial property: properties inregulated areas are less redeployable – thus less valuable ascollateral.

I Within a census tract, more redeployable assets receive loansthat are (1) larger (2) longer maturity (3) lower interest rates.

I This analysis controls for many property characteristics,including sale price.

I Caveat: hard to entirely rule out endogenous selection ofprojects.

I Real effects?

Collateral value affect Corporate Investment

I Chaney et al. [2010] use variation in local real estate prices asshock to the value of collateral for firms that own real estate.

I Empirical strategy: triple differences – compare evolution ofinvestment of firms with vs. without real estate in cities withincreasing vs. decreasing real estate prices.

I Endogeneity issues:

1. Real estate prices is a proxy for local demand shocks. ⇒compare renters vs. owners in same city.

2. Reverse causality: firm’s investment impact city level realestate prices. ⇒ instrument real estate prices with interestrates × local elasticity of land supply.

Illustration: price variations during the real estate bubbleA Figures and Tables

Figure 1: Relative Evolution of O!ce Prices (High vs. Low Elasticity MSA, 2000-2006)

.91

1.1

1.2

1.3

1.4

1.5

1.6

MS

A o

ffic

e p

ric

es

2000 2002 2004 2006

year

Low elasticity High Elasticity

Note: This figure shows the average o!ce price index (normalized to 1 in 2000) for MSAs in the bottomquartile of land supply elasticity (“Low Elasticity MSA”) in blue and MSAs in the top quartile of landsupply elasticity (“High Elasticity MSA”) in red.

32

CAPEX differential of owners vs. renters in different areas

Figure 2: Relative Evolution of Accumulated Capex (owners vs. renters, 2000-2006)

-.04

-.02

0.0

2.0

4.0

6.0

8

Rela

tive A

ccum

ula

ted C

apex

2000 2002 2004 2006

year

Low Elasticity MSA High Elasticity MSA

relative accumulated capex (owner vs. renter)

Note: This figure shows, for each year between 2000 and 2006, the di!erence between the averageaccumulated capex of headquarter owners minus the average accumulated capex of headquarter renters,for MSAs in the bottom quartile of land supply elasticity (“Low Elasticity MSA”) in blue and MSAs inthe top quartile of land supply elasticity (“High Elasticity MSA”) in red. Accumulated capex is definedas 0 in 2000, and then as the sum of all capex made by the firm between 2001 and the current year,normalized by assets in 2000.

33

I Result works mostly through increased debt issuance.

I Low elasticity: 6 cents on the dollar. But: fraction of firmsthat invest × redeployability × fraction of constrained firms.

Endogeneity of ownership?

I Omitted variable bias: firms with larger co-movement ofinvestment and local real estate prices prefer to own theirproperties.

I Then estimate is upward biased.I If this omitted variable is fixed in time:

I Firms before they purchase real estate should already haveinvestment > 0 correlated with local real estate prices.

I Not the case in the data.I However, if omitted variable is time-varying, then no solution.

Table of Contents




Intermediation

I We have focused on the demand side of credit. Variations infirms characteristics (cash/collateral value/etc.) influencefirms financial capacity.

I However, most of finance is intermediated (∼ supply side).Frictions in the financial sectors can translate into reduction infinancial capacity for corporations.

I Empirical evidence on the role played by the financial sectoron firms’ investment behavior.

The Japanese Crisis as an experiment

I Bank collateral channel: (1) banks are credit constrained (2)other forms of financing are not readily available (relationshipbanking).

I Gan [2007] uses the burst of the Japanese real estate bubbleas a shock to the health of banks exposed to real estate risk.

I Two endogeneity issues:

1. Separate demand from supply.2. Endogenous matching of firms and banks (firms exposed to

real estate borrow from banks exposed to real estate).

I Identification strategy:

1. Compare within the same firm lending supply from differentlenders.

2. Compare investment of firms as a function of top lender’sexposure to real estate market.

A shock to lending supply. . .

Lendingij = aj +b×RE exposurei +c×Bank controlsi +d×Relationship controlij +εij

I Lendingij is (1) dummy for continuation of lending relationship(2) log amount borrowed after the crisis period (94-98).

I aj : firm FE controls for credit demand.

I RE exposure: % real estate loans, land holding.

I Results: b < 0 and significant.

. . . that turns into a shock to investment

Dependent variable: Average I/K from 1994-1998.

The Real Effects of Asset Market Bubbles

Table 3Effect of the top lender’s real estate exposure on the firm’s fixed investment

(1) (2) (3) (4) (5)

q 0.025*** 0.025*** 0.025*** 0.025*** 0.026***

(0.000) (0.000) (0.000) (0.000) (0.000)Cash flow/K 1.111*** 1.116*** 1.107*** 1.103*** 1.083***

(0.018) (0.022) (0.021) (0.022) !0.016Cash stock/K 0.205*** 0.204*** 0.205*** 0.206*** 0.209***

(0.003) (0.003) (0.003) (0.003) !0.002Land/K1989 (firm) !0.132*** !0.138*** !0.173*** !0.173*** !0.168***

(0.036) (0.044) (0.043) (0.044) !0.031% Recent purchase !0.064 !0.07 !0.042 !0.027 !0.024

(0.118) (0.145) (0.111) (0.109) !0.08Leverage 0.090* 0.116* 0.140** 0.157** 0.154***

(0.054) (0.067) (0.069) (0.068) (0.049)Land/K1989 (firm) * high leverage !0.092*** !0.097*** !0.118*** !0.107*** !0.117***

(0.018) (0.022) (0.022) (0.023) (0.016)Access to the public bond market !0.014 (0.004)

(0.014) (0.013)Top lender’s real estate exposure and other characteristics% Real estate loans !0.615** !0.509* !0.664** !0.640* !0.712***

(0.249) (0.305) (0.309) (0.369) (0.265)% Land holding !0.07 !0.134 !0.484 0.001 !0.009

(0.292) (0.352) (0.482) !0.514 (0.372)Top lender’s capital ratio 2.520* 2.218 2.811 5.654** 5.320***

(1.419) (1.694) (1.738) (2.343) (1.379)Top lender’s loan share !0.123*** !0.100*** !0.114***

(0.037) !0.029 (0.021)% Real estate loans* CON 0.075

(0.132)% Land holding * CON 0.487

(0.705)% Real estate loans * BIND !0.044

(0.145)% Land holding * BIND !1.189* !1.113**

(0.693) (0.557)% Stock holdings 0.075

(0.702)SOLD 0.07

(0.062)Observations 420 420 420 420 420Pseudo R2 0.44 0.44 0.45 0.45 0.45

This table presents the effect of the top lender’s real estate exposure on firm investment based on leastabsolute distance (LAD) regressions. The dependent variable I/K is the average investment rate (definedas fixed investments normalized by the beginning-of-period capital stock) between 1994 and 1998. Cashflow/K is the cash flow normalized by the beginning-of-period capital stock. Cash Stock/K is the cash stocknormalized by the beginning-of-period capital stock at the end of 1993. q is Tobin’s average q, Land/K(firm) is the firm’s market value of land normalized by the replacement cost of capital in 1989. % RecentPurchase is the proportion of land (in market value) purchased during 1988–1990. Leverage is the debtto assets ratio. High leverage is a dummy variable indicating a firm’s leverage above the median. % RealEstate Loans is the loans to the real estate sector normalized by the total assets in 1989. % Land Holding ismarket value of land normalized by the total assets in 1989 for the top lender. CON is a dummy variableindicating whether the top lender’s loan share is above the median. BIND is a dummy variable indicatingwhether the top lender’s capital ratio in 1989 is below the capital requirement 4%. Standard errors arecalculated on the basis of the asymptotic variance. They are presented in parentheses. Significance at the1%, 5%, and 10% levels is indicated by *** , ** , and * , respectively.

1963



(1) (2) (3) (4) (5)

q 0.025*** 0.025*** 0.025*** 0.025*** 0.026***

(0.000) (0.000) (0.000) (0.000) (0.000)Cash flow/K 1.111*** 1.116*** 1.107*** 1.103*** 1.083***

(0.018) (0.022) (0.021) (0.022) !0.016Cash stock/K 0.205*** 0.204*** 0.205*** 0.206*** 0.209***

(0.003) (0.003) (0.003) (0.003) !0.002Land/K1989 (firm) !0.132*** !0.138*** !0.173*** !0.173*** !0.168***

(0.036) (0.044) (0.043) (0.044) !0.031% Recent purchase !0.064 !0.07 !0.042 !0.027 !0.024

(0.118) (0.145) (0.111) (0.109) !0.08Leverage 0.090* 0.116* 0.140** 0.157** 0.154***




(0.249) (0.305) (0.309) (0.369) (0.265)% Land holding !0.07 !0.134 !0.484 0.001 !0.009







(0.693) (0.557)% Stock holdings 0.075

(0.702)SOLD 0.07



1963



(1) (2) (3) (4) (5)

q 0.025*** 0.025*** 0.025*** 0.025*** 0.026***

(0.000) (0.000) (0.000) (0.000) (0.000)Cash flow/K 1.111*** 1.116*** 1.107*** 1.103*** 1.083***

(0.018) (0.022) (0.021) (0.022) !0.016Cash stock/K 0.205*** 0.204*** 0.205*** 0.206*** 0.209***

(0.003) (0.003) (0.003) (0.003) !0.002Land/K1989 (firm) !0.132*** !0.138*** !0.173*** !0.173*** !0.168***

(0.036) (0.044) (0.043) (0.044) !0.031% Recent purchase !0.064 !0.07 !0.042 !0.027 !0.024

(0.118) (0.145) (0.111) (0.109) !0.08Leverage 0.090* 0.116* 0.140** 0.157** 0.154***




(0.249) (0.305) (0.309) (0.369) (0.265)% Land holding !0.07 !0.134 !0.484 0.001 !0.009







(0.693) (0.557)% Stock holdings 0.075

(0.702)SOLD 0.07



1963



(1) (2) (3) (4) (5)

q 0.025*** 0.025*** 0.025*** 0.025*** 0.026***

(0.000) (0.000) (0.000) (0.000) (0.000)Cash flow/K 1.111*** 1.116*** 1.107*** 1.103*** 1.083***

(0.018) (0.022) (0.021) (0.022) !0.016Cash stock/K 0.205*** 0.204*** 0.205*** 0.206*** 0.209***

(0.003) (0.003) (0.003) (0.003) !0.002Land/K1989 (firm) !0.132*** !0.138*** !0.173*** !0.173*** !0.168***

(0.036) (0.044) (0.043) (0.044) !0.031% Recent purchase !0.064 !0.07 !0.042 !0.027 !0.024

(0.118) (0.145) (0.111) (0.109) !0.08Leverage 0.090* 0.116* 0.140** 0.157** 0.154***




(0.249) (0.305) (0.309) (0.369) (0.265)% Land holding !0.07 !0.134 !0.484 0.001 !0.009







(0.693) (0.557)% Stock holdings 0.075

(0.702)SOLD 0.07



1963

Bank Collateral Channel in normal times

I Issue with previous approach: external validity. Japan is incrisis so other forms of financing not available.

I How do we look at the question in a normal environment?

I Peek and Rosengren [2000] uses Japanese lending forcommercial real estate projects in the US and exploitscross-sectional heterogeneity in Japanese banks health.

1. Japanese branches with affected parents cut lending for CRE.2. Number/size of CRE projects financed in a US state depends

on health of parents of Japanese banks.

I Large estimated effects: $100 decline in loans by Japanesebanks operating in a given state → $111 decrease inconstruction activity in that state.

Evidence from the Great Financial CrisisFigure 1: Historical LIBOR and Commecial Paper Spreads

This figure displays the 3-month LIBOR and commercial paper (CP) spreads over treasuries, for theperiod of January 2001 to June 2008. The data is from http://www.federalreserve.gov/datadownload/.

I Undoubtedly, the financial crisis was a large shock to bankfinancing capacities.

I Usual problem: supply vs. demand.

Disentangling supply and demand

I Almeida et al. [2009] slices COMPUSTAT sample by lookingat firms with a large fraction (> 20%) of long-term debtmaturing during the financial crisis (2008 vs. 2007).

I Identifying assumption: these maturity choices were made along time ago and are thus ⊥ to investment opportunities in2007

I Caveats: good firms actively manage their maturity structurebefore the crisis?

A difference-in-difference estimation

Table 3: Di!erences-in-Di!erences of Firm Investment Before and Afterthe Fall 2007 Credit Crisis with a Placebo Test Conducted aYear Before the Credit Crisis

Panel A of this table presents an estimate of the change in average quarterly investment rates from the firstthree quarters of 2007 to the first three quarters of 2008 (before and after the fall 2007 credit crisis). PanelB presents an estimate of the change in investment from the first three quarters of 2006 to the first threequarters of 2007 (a placebo test conducted before the credit crisis). In Panel A, the average of quarterlyinvestment during the first three quarters of 2008 and the first three quarters of 2007 is calculated for thetreated firms and control firms, as well as the di!erence in the di!erence between the two groups of firmsover the two years. The average quarterly investment is normalized by the capital stock at the precedingquarter; that is, by lagged property, plant, and equipment. The treated firms are defined as those for whichthe percentage of long-term debt maturing within one year (i.e., 2008) is greater than 20 percent and controlfirms are defined as those for which the percentage of long-term debt maturing within one year is less thanor equal to 20 percent. Control firms are a subset of the non-treated firms selected as the closest match tothe treated firms based on a set of firm characteristics: Q, cash flow, size, cash holdings, long-term debtnormalized by assets, 2-digit SIC industry, and credit ratings. There are 86 treated firms and 86 controlfirms in Panel A. Panel B is constructed analogously, but the tests are conducted one year earlier (before thecredit crisis). There are 113 treated firms and 113 control firms in Panel B. ATT is the Abadie-Imbens bias-corrected average treated e!ect matching estimator (Matching Estimator). Heteroskedasticity-consistentstandard errors are in parentheses.

Average Quarterly Investment / Capital Stock(in percentage points)

2008 2007 Di!erence

Panel A: Investment Before and After the Fall 2007 Credit CrisisInvestment in 2008 (Q1 to Q3) vs. Investment in 2007 (Q1 to Q3)

Treated Firms 5.7*** 7.8*** –2.1**(0.5) (0.9) (0.8)

Control Firms 7.3*** 7.3*** 0.1(0.6) (0.7) (0.7)

Di!erence –1.6*** 0.6 –2.2**(0.6) (1.0) (1.0)

Matching Estimator –2.5**(ATT) (1.1)

Panel B: The Placebo TestInvestment in 2007 (Q1 to Q3) vs. Investment in 2006 (Q1 to Q3)

Treated Firms 6.9*** 7.3*** –0.4(0.7) (0.6) (0.7)

Control Firms 6.9*** 7.2*** –0.3(0.7) (0.8) (0.8)

Di!erence 0.0 0.1 –0.1(0.8) (0.8) (1.0)

Matching Estimator 0.0(ATT) (1.1)

***, **, * indicate significance at the 1, 5, and 10 percent levels, respectively.

Table of Contents




Beyond CAPEX: precautionary motives and creditconstraints

I Problem with CAPEX analysis: unobserved investmentopportunity (i.e. demand).

I Almeida et al. [2004] looks at cash saving. If firms areunconstrained, cash saving should have no systematic pattern.

I For constrained firms, cash hoarding should prevail when > 0cash flow shocks.

I Simple test using Fazzari et al. [1988] methodology.

Cash-to-Cash Sensitivity

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Beyond CAPEX: Labor and Financing constraints

I Labor is not perfectly adjustable – adjustment costs becauseof organizational frictions. Thus labor needs financing too.Credit constraints may bind. Very little literature on thequestion.

I Benmelech et al. [2011] use Almeida et al. [2009]’smethodology and find that employment decision reactssignificantly to liquidity shocks during the financial crisis.

I Bakke and Whited [Forthcoming] use Rauh [2006]’smethodology and find significant response of employment topension fund’s funding deficits.

I Caveat: what is the counterfactual?

I Important area for future research – especially macro-oriented.Clear case where structure is needed.

Beyond CAPEX: Working Capital

I Operating Working Capital (maturity mismatch betweencurrent assets and current liabilities) need to be financed aswell.

I Typical example: exporters have long cash cycle.I Paravisini et al. [2011] looks at financial crisis in Peru and

impact on exporters.

1. Split banks along exposure to financial crisis (using foreignliabilities).

2. Identify credit supply shock using within firm attribution ofcredit (cf. Gan [2007]).

3. Compare, for given product/destination pair (demand shock),growth rate in exports as a function of top lender’s exposure.

⇒ Estimates low relative to rest of the literature. Peruvian exportsvolume growth was -9.6% after July 2008. Credit supply declineaccounts for ∼15% of the missing volume of exports.

Financing shock

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High Foreign Liability Share

Low Foreign LiabilitY Share

Source: Bank financial statements and credit registry, Superintendencia de Bancos ySeguros de Peru, and SUNAT. Banks with high (low) foreign liability share are thosewith fraction of foreign liabilities to assets above (below) 9.5% in January-June 2008.

Figure 3: Lending by Banks with High Share of Foreign Liabilities

33

Financial constraints and the intensive margins of export

Dependent Variable: ! lnCi ! lnV olipd ! lnFOBipd

FS RF OLS IV RF OLS IV(1) (2) (3) (4) (5) (6) (7)

Panel 1: Products defined at 4-digit HS

Dummy A"ected: > 50% -0.561*** -0.127** -0.144**(0.192) (0.058) (0.062)

! lnCi 0.025 0.227*** 0.035* 0.257***(0.018) (0.068) (0.020) (0.060)

Product-Destination FE Yes Yes Yes Yes Yes Yes Yes# Product-Destinations 5,997 5,997 5,997 5,997 5,997 5,997 5,997Observations 14,208 14,208 14,209 14,210 14,210 14,210 14,210R2 0.360 0.438 0.438 0.437 0.437

Panel 2: Products defined at 6-digit HS

Dummy A"ected: > 50% -0.636** -0.133* -0.155**(0.250) (0.071) (0.076)

! lnCi 0.029 0.209*** 0.044** 0.249***(0.019) (0.060) (0.021) (0.058)

Product-Destination FE Yes Yes Yes Yes Yes Yes Yes# Product-Destinations 8,567 8,567 8,567 8,567 8,567 8,567 8,567Observations 16,472 16,472 16,472 16,472 16,472 16,472 16,472R2 0.447 0.529 0.528 0.525 0.524

Estimation of equation (6). In the IV regression, the change in (log of) credit, ! lnCi,is instrumented with Fi, a dummy that takes value 1 if the firm borrows more than50% from an a"ected bank. Standard errors clustered at the product-destination level inparenthesis. !!!p < 0.01, !!p < 0.05, and !p < 0.1

Table 5: Export Elasticity to Credit Shocks: Intensive Margin

38

Table of Contents




Beyond firm-level evidence

I Most evidence so far have been based on firm-level evidence.

I Growth and Finance literature. Objective: empirical linkbetween development of financial sector and macro outcome(e.g. growth).

I First pass: cross-country regressions. E.g. King and Levine[1993].

I Obvious causality issue: growth → financial development?

I Obvious omited variable bias. → Need to find rightgranularity.

!"#$#%"$&'()*+,'

"#'-./0'

If BANK in Zaire in 1970 goes from 26% to 57% (mean samplevalue), growth rate in Zaire .9% larger. By 1980, real GDP/ capita∼ 9% larger than realized.

Industry-level/Cross Country Approach

I Rajan and Zingales [1998] looks at industry-level data aroundthe world.

I Assumptions:

1. Cross-industry heterogeneity in demand for outside finance.Driven by technology (growing vs. mature industry). Proxy byusing ratio of outside finance used to fund investment at theindustry level in the US during the 80s.

2. Cross-country heterogeneity in financial development. Proxy byusing size of stock market and lending market or accountingstandards.

I Hypothesis: Industries that rely more on outside financeshould have lower growth in countries with low financialdevelopment.

∆VA

VA jc| {z }growth in VA in 80s

= αj + δc + µ EFj|{z}Financial dependence

× FDc|{z}Financial Development

+εjc

Illustration

--

576 THE AMERICAN ECONOMIC REVIEW JIJNE 1998

In the fifth column, we include both total capitalization and accounting standards. The coefficient for total capitalization is no longer different from zero and its magnitude falls to one-fifth of its level in the first column. Similar results are obtained when we replace total capitalization by domestic credit to the private sector (coefficients not reported). This suggests that accounting standards capture the information about development that is contained in the capitalization measures. For this reason, we will use accounting standards as our measure of development in the rest of the paper. The reader should be assured, however, that the results are qualitatively similar when capitalization nieasures of development are used.

Because of potential concerns about endogeneity, we will, however, instrument accounting standards with predetermined in- stitutional variables. Rafael La Porta et al. (1996) suggest that the origin of a country's legal system has an effect on the development of a domestic capital market and on the nature of the accounting system. Countries colonized by the British, in particular, tend to have so- phisticated accounting standards while countries influenced by the French tend to have poor standards. This suggests using the colo- nial origin of a country's legal system (indi- cators for whether it is British, French, German, or Scandinavian) as reported in La Porta et al. as one instrument. Also, countries differ in the extent to which laws are enforced. So we use an index for the efficiency and in- tegrity of the legal system produced by Busi- ness International Corporation (a country-risk rating agency) as another instrument. As the sixth column of Table 4 shows, the fundamen- tal interaction becomes even stronger in magnitude when we estimate it using instrumental variables.

Before going further, consider the actual (rather than estimated) effects of development on the growth of specific industries. In Table 5, we summarize for the three least-dependent and three most-dependent industries, the residual growth rate obtained after partialling out industry and country effects. The pattern is re- markable. For countries below the median in accounting standards, the residual growth rate of the three least-dependent industries is pos- itive, while the residual growth rate of the

TABL.E5-EFFECT OF FINANCIAL. ONDEVEL-OPMENT ACTUALGROWTH IN DIFFERENTRATES INDUSTRIES

Countries below Countries above the median in the median in

accounting standards accounting standards

Least financially dependent industries

Tobacco 0.53 -0.60

Pottery 0.25 -0.30

Leather 0.77 -0.7'7

Most financially dependent industries

Drug - - 1.11 1.30

Plastics -0.2.1 0.21

Computers -2.00 1.80

Notes: This table repolTs the mean residual growth rate (in percentage terms) obtained after regressing the annual compounded growth rate in real value added for the period 1980- 1990 on industry and country dummies.

three most-dependent industries is negative. The pattern reverses for countries shove the median. Clearly, this suggests no single country or industry drives our results and the realized differential in growth rates is systematic and large.

2. Varying Measures oj'Dependence. -We now check that our measure of dependence is, indeed, reasonable. We do this in two ways. First, we check that past financing in a country is related to the external dependence of industries in the country. Second, we check that our result is robust to different measures of dependence.

Total capitalization is a (crude) measure of how much finance has been raised in the past in the country. If external dependence is a proxy for an industry's technological need for external finance outside the United States, there countries more specialized in externally dependent industries should have higher capitalization. We calculate the weighted average dependence for each country by multiplying an industry's dependence on external finance by the fraction that the industry contributes to value added in the manufacturing sector in 1980. We then regress total capitalization

I Regression analysis: Moving from 25th to 75th percentile offinancial development & financial dependence increases annualgrowth rate by 1 ppt.

US State-level evidence

I Jayaratne and Strahan [1996] use banking deregulation in USstates – staggered in the 70’s & 80’s – as a > 0 shock tofinancial development.

I Deregulation allows banks to (1) establish new branches (2)acquire out-of-states banks. Interpretation: significantincrease in local competition and takeover threat.

I Simple diff-in-diff strategy: compare growth rate of statesafter vs. before deregulation relative to states that did notderegulate.(

∆y

y

)it

= αi + δt + βIt>reformdate + [γr ,t ] + εit

I Annual growth rates increase by ∼ 1 ppt followingderegulation.

I Caveats: endogeneity of timing of reforms;

Branching deregulation and state-level growth

!!"#$%&'()'*+#"!(,'-'

!!"#$%&'()'#%&+"'.%,%+.'

/'01'-'2341'5'2341'

Interpretation

I Interpretation: better allocation of capital – fraction ofnon-performing loans go down in the data by .5/1ppt on a 2%sample mean.

I Deregulation fosters entry of new firms: Cetorelli and Strahan[2006].

I Especially in industries which depends more on bank finance(cf. Rajan and Zingales [1998])

I Marginal firm is smaller.

Table of Contents




Other empirical approaches on credit frictions (1)

I Large body of survey evidence, best exemplified by Campelloet al. [2010].

I 1,500 multinational firms asked during the financial crisiswhether operations affected by credit availability.

I Response largely confirmed by actual data.

cuts in their policies. Asian firms show very pronounceddifferences in business plans for constrained versusunconstrained firms.

4.3. Financial constraints and corporate policies: a matchingapproach

One issue we investigate is whether the surveymeasure of financial constraint has a significant relationwith corporate policies that is not subsumed by standardmeasures of constraint. Our data allow us to test this ideaboth for the crisis peak period of 2008Q4 as well as for thequarters preceding it. In particular, prior rounds of the U.S.quarterly survey allow us to produce a rotating panelcontaining policy and demographic information forhundreds of companies in each of the following quarters:2007Q3, 2007Q4, 2008Q1, 2008Q2, and 2008Q3 (a total of2,226 observations). These data are interesting becausethey precede the Lehman debacle (which happened at thevery end of 2008Q3). For ease of exposition, we label thisperiod the ‘‘pre-crisis period.’’ We employ two matchingestimator approaches to make comparisons across time.

Our variables are largely categorical and fit well withthe full-covariate matching procedure of Abadie andImbens (2002).10 For every firm identified as financiallyconstrained (or ‘‘treated’’), we find an unconstrainedmatch (a ‘‘control’’) that is in the same size category, thesame ownership category, and the same credit ratingcategory. We also require that the matching firm isin the same industry and survey quarter. The procedurethen estimates the differences in corporate policies(‘‘outcomes’’) for constrained firms relative to those that

are unconstrained, conditional on matching on each andall of the aforementioned characteristics. Generallyspeaking, instead of comparing the average difference inpolicy outcomes across all of the constrained and all of theunconstrained firms (as in Figs. 1 and 2), we now comparethe differences in average outcomes of firms that arequite similar (i.e., matched) except for the ‘‘marginal’’dimension of CFO-reported financial constraints. Thisyields an estimate of the differential effect of financialconstraints on corporate policies across ‘‘treated’’ firmsand their ‘‘counterfactuals.’’11

Table 5 shows how the survey measure fares ingauging the effects of financial constraints on firmpolicies prior to the crisis (2007Q3 through 2008Q3) andduring the crisis (2008Q4). For now, we focus on columns1 and 3, which present the results from the Abadie-Imbens estimator for the pre-crisis and crisis periods,respectively. A number of patterns stand out. First, evenfor the pre-crisis periods, our measure of financialconstraint picks up significant differences in policyoutcomes for constrained vis-!a-vis unconstrained firms.Column 1 shows that firms that report themselves asbeing financially constrained systematically planned toinvest less in technology (an average differential of !5%per year), invest less in fixed capital (!8%), cut marketingexpenditures by more (!6%), reduce employment bymore (!6%), conserve less cash (!3%), and pay fewerdividends (!8%). These numbers are economically andstatistically significant.12

ARTICLE IN PRESS

-22.0

-9.0-9.1

-0.6

-32.4

-4.5

-10.9

-2.7

-15.0

-2.7

-14.2

-2.9

-40

-30

-20

-10

0

10

% C

hang

e

Constrained Unconstrained

Plans of constrained vs. unconstrained firms

Tech expenditures Capital expenditures

Marketing expenditures Number of employees

Cash holdings Dividend payments

Fig. 2. This figure displays U.S. firms’ planned changes (% per year) in technology expenditures, capital expenditures, marketing expenditures, totalnumber of domestic employees, cash holdings, and dividend payments as of the fourth quarter of 2008 (crisis peak period). The data are taken from the2008Q4 U.S. survey. The sample excludes non-financial, governmental, and non-profit organizations. Responses are averaged within sample partitionsbased on the survey measure of financial constraint.

10 See Abadie and Imbens for a detailed discussion of their matchingestimator. Here we apply the bias-corrected, heteroskedasticity-consis-tent estimator implemented in Abadie, Drukker, Herr, and Imbens(2004).

11 In the treatment evaluation literature, this difference is referredto as the average treatment effect for the treated firms, or ATT [seeImbens (2004) for a review].

12 Because the troubles with Lehman could have been anticipated in2008Q3 and the economy had already notably slowed down, weconducted robustness checks excluding 2008Q3 from the analysis. Ourinferences are unaffected by that sample restriction.

M. Campello et al. / Journal of Financial Economics 97 (2010) 470–487478

Other empirical approaches on credit frictions (2)

I Many public policies aim at reducing credit frictions: creditguarantee & subsidized loans. (SBA in the US).

I Clean evaluation of these policies often suggest they havesignificant effect on firms’ investment.

I Lelarge et al. [2010] use variations in eligibility rule for aguaranteed loan program in France and show that guaranteedloans allow firms to grow more but increase risk-taking(consistent with the existence of ex ante credit constraints).

I Banerjee and Duflo [2004] use variations in eligibility rule for atargeted lending program in India and show that greater bankcredit availability increases eligible firms’ output.

Conclusion

I Vast literature on the empirics of credit constraints. Verydifficult task: disentangling supply vs. demand or investmentopportunities vs. financing shocks.

I Despite 25 years of research, no smoking gun. But animportant number of papers converge toward same conclusionthat finance matters, both at the micro- and the macro-level.

I Literature poor on theoretical insights. What model of creditfrictions should we use, based on empirical evidence?

I Moral hazard vs. adverse selection? Welfare implications arevery different (over-investment vs. under-investment).

I Evidence on role of collateral suggests limited commitment(and inalienability of human capital) is important. Hart andMoore [1994] may be a good starting point.

I Important that macro- and empirical micro- researchcommunicate more – what elasticities should we be lookingfor in the data?

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Financial Frictions: Empirical Facts

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