the ‘‘credit crunch’’ and the availability of credit to...

The ``credit crunch'' and the availability of

credit to small business

Diana Hancock a,*, James A. Wilcox b

a Mail Stop 153, Board of Governors of the Federal Reserve System, 20th and C Streets, NW,

Washington, DC 20551, USAb Haas School of Business, 545 Student Services, #1900, University of California±Berkeley, Berkeley,

CA 94720-1900, USA

Abstract

We present estimates of how much bank loans and real activity in small businesses

responded to changes in banks' capital conditions and other bank and aggregate eco-

nomic conditions. Using data for 1989±1992 by state, we estimated the e�ects of those

factors on employment, payrolls, and the number of ®rms by ®rm size, as well as on

gross state product. In response to declines in their own bank capital, small banks

shrank their loan portfolios considerably more than large banks did. Large banks

tended to increase loans more when small banks were under increased capital pressure

than vice versa. Real economic activity was reduced more by capital declines and by

loan declines at small banks than at large banks. Small banks were making ``high-

powered loans'' in that dollar-for-dollar loan declines in their loans had larger impacts

on economic activity than loan declines at large banks did. Capital declines at small

banks produced larger changes in economic activity dollar-for-dollar than capital de-

clines at large banks did. Aggregate economic conditions had smaller e�ects on small

®rms than on large ®rms and smaller e�ects on small banks than on large banks. The

evidence hinted that the volume of loans made under Small Business Administration

(SBA) loan guarantee programs shrank less in response to declines in bank capital than

the volume of loans not made under the SBA loan guarantee programs. Ó 1998

Published by Elsevier Science B.V. All rights reserved.

Journal of Banking & Finance 22 (1998) 983±1014

* Corresponding author. Tel.: +1 202-452-3019; fax: +1 202-452-5295; e-mail: han-

[email protected].

0378-4266/98/$19.00 Ó 1998 Published by Elsevier Science B.V. All rights reserved.

PII S 0 3 7 8 - 4 2 6 6 ( 9 8 ) 0 0 0 4 0 - 5

JEL classi®cation: E44; G21

Keywords: Bank capital; Credit availability; High-powered loans; Small business; Credit

crunch

1. Overview and review

Small businesses have long relied heavily on banks for credit. Brewer et al.(1996) document that the smaller ®rms in their sample are more ``loan-de-pendent'' than larger ®rms, which rely more heavily on equity ®nance. Newly-available data on small business ®nance document that banks have remainedthe primary source of credit for small businesses. Cole et al. (1996) reportedthat in 1993 about 60 percent of credit extended to small businesses came frombanks.

Levonian and Soller (1996) and Peek and Rosengren (1995a) argued thatsmaller banks seemed to be the primary lenders to small businesses. Berger etal. (1995) also reported that smaller banks had larger proportions of their loansdevoted to small businesses than larger banks. Berger and Udell (1996) notedthat the type of lending to small businesses may also di�er by bank size. Theyfound that when large banks do issue credit to small businesses, they typicallyo�er lower interest rates and fewer collateral requirements. They concludedthat large banks' lending to small business was less often relationship-based.Further empirical support for the hypothesis that small and large banks spe-cialized in lending to di�erent sizes and types of ®rms was o�ered by Berger etal. (1998). They reported that small business lending fell when large banksconsolidated, while it rose when small banks consolidated.

Banks reduced the total supply of bank credit after loan losses around 1990reduced their capital. 1 The apparently heavy dependence of small businesseson banks for credit suggests that such a ``capital crunch'' on banks mightimpinge with particular force on small business.

Real economic activity in small businesses did shrink relative to that oflarger businesses in the years surrounding 1990. Fig. 1 shows that during 1989±1991, employment, payrolls, and the numbers of ®rms grew more slowly forbusinesses with less than 500 employees than they grew at large businesses.Figs. 1 and 2 show that those measures of economic activity grew slowerparticularly at the largest of ®rms classi®ed as small businesses.

1 See Bernanke and Lown (1991), Hall (1993), Hancock and Wilcox (1993, 1994a, b) and Peek

and Rosengren (1995b). Bizer (1993) argued that regulators may have also raised the e�ective

regulatory minimum amount of bank capital around this time.

984 D. Hancock, J.A. Wilcox / Journal of Banking & Finance 22 (1998) 983±1014

Fig. 1. The growth of employment, numbers of ®rms, and payrolls by ®rm size.

D. Hancock, J.A. Wilcox / Journal of Banking & Finance 22 (1998) 983±1014 985

Fig. 2. The growth of employment, numbers of ®rms, and payrolls by ®rm size.


Most studies of the bank capital crunch have reported signi®cant e�ects onbanks' portfolios of various measures of pressure on bank capital. 2 Fewstudies, however, have detected clear signals that the capital crunch reducedreal economic activity. Bernanke and Lown (1991), for example, found evi-dence that banks' loan portfolios responded, but found little evidence thatemployment growth responded, when banks lost capital. Hancock and Wilcox(1997) explicitly tested whether various aspects of real economic activity in thereal estate sector were a�ected, ceteris paribus, by the capital crunch. Theyconcluded that both commercial and residential real estate activity declined asa result of the bank capital crunch. Similarly, Peek and Rosengren (1997)found that the Japanese real estate collapse a�ected commercial real estatemarkets in the United States, particularly in local markets where Japanesebanks had signi®cant shares of the real estate loan market.

In this study, we directed our attention to the e�ects of bank capital pres-sures and of other banking and economic shocks on small businesses. We usedas proxies for real economic activity a number of measures: employment,payroll, numbers of ®rms, numbers of businesses, numbers of business failures,and numbers of bankruptcies recorded. We were particularly interested inwhether capital declines at small banks were associated with particularly largedeclines in the real economic activity of small businesses. To see whether smallbusinesses di�ered appreciably from large businesses in their responses, weestimated the e�ects of bank capital pressures on the real economic activity ofbusinesses of various sizes. Because smaller banks were thought to have specialties to smaller businesses and because we regarded smaller banks as havingmany of the same opportunities and constraints as other small businesses, weallowed smaller banks to have di�erent responses to various shocks and tohave di�erent e�ects on businesses of all sizes than larger banks. 3

More speci®cally, we addressed the following issues:1. Was lending a�ected di�erently at large and small banks by capital and oth-

er shocks?2. To what extent were capital pressures in one group of banks o�set by in-

creased lending at other banks?3. Was real activity a�ected by the capital crunch?4. Did businesses that were more ``bank-dependent'' respond more to the cap-

ital crunch?5. Did Small Business Administration (SBA) programs accentuate or attenuate

the capital crunch?

2 See Berger and Udell (1994), Peek and Rosengren (1994, 1995a, b), Hancock and Wilcox

(1993, 1994a, b, 1997) for estimates of the e�ects of bank capital pressures on bank portfolios.3 See Kashyap and Stein (1995) and Hancock and Wilcox (1995).


Section 2 sketches out the justi®cation for our empirical models of bankloans and real economic activity. Section 3 provides information about thedata we used. Section 4 details the speci®cations of the variables and the re-gression equations and estimation methods that we used. Section 5 reports anddiscusses our empirical ®nding that the decline in total real economic activityper dollar decline in capital at small banks was larger than for capital declinesat large banks. Among our conclusions in Section 6 are that small banks made``high-powered loans'', in the sense that dollar-for-dollar declines in smallbanks' loan portfolios had larger impacts on economic activity than loan de-clines at larger banks did.

2. Models of bank loans and real economic activity

We began with the model of the bank supply of and borrower demand forbank credit presented in Hancock and Wilcox (1994a). 4 That model positedthat the bank supply of loans depended positively on the loan interest rate,negatively on perceived risks to the bank, positively on other factors that raisedthe expected return on loans, and positively on bank capital. Bank capital mayhave a�ected the supply of bank credit either because of an implicit regulatory¯oor on the capital-to-assets ratio or because the bank itself chose to imposeone on itself.

Borrower demand for credit depended negatively on loan interest rates andon perceived risks to the borrower of undertaking projects with credit. Bor-rower demand did not depend on bank capital. All variables other than thebank capital variables appeared in both the supply and demand functions.Thus, in the reduced form, only the capital variable carries a coe�cient ame-nable to a structural interpretation. The other coe�cients represent an amal-gam of supply and demand e�ects.

Our empirical implementation of the model for bank loans included thefollowing explanatory variables. As indicators of risk to banks and borrowers,we included loan delinquency variables as well as two measures of economicconditions (consumer sentiment and the bank prime interest rate). During thisperiod one could regard the interest rate as having been, in e�ect, set bymonetary policy and thus predetermined in our model. Even so, because theinterest rate would likely convey information to banks and borrowers not justabout the cost of credit but also about the expected future returns and risks ofcredit, it is not straightforward to interpret the coe�cients on the interest ratevariable. We also included capital variables split by bank size and by timeperiod.

4 We omitted the dynamics that Hancock and Wilcox (1994a) allowed for.


In our empirical models for real economic activity, we considered speci®-cations that directly included measures of bank loans, as well as speci®cationsthat replaced the loan variables with bank capital variables, which were pre-sumed to a�ect the supply of bank loans. We posited that the demand foroutput depended positively and directly on the supply of bank loans andpositively and indirectly on bank capital. 5 The demand for output also de-pended positively on consumer sentiment and depended negatively on loandelinquency rates and the interest rate. These variables had direct e�ects onspending and the demand for credit as well as indirect e�ects that operatedthrough the supply of bank credit. Thus, we cannot put a structural interpr-etation on the reduced-form coe�cients used to explain real economic activity.Consider the coe�cient on consumer sentiment. It is very possible that in-creased consumer sentiment re¯ects optimism and increased demand for out-put and thus more jobs, ®rms, and payrolls. Increased consumer sentiment alsolikely translates into increased supply of bank credit because banks use it as aninformation variable. As a result, the estimated coe�cient on consumer sen-timent re¯ects both of these positive in¯uences on demand for output.

3. Data

Our data measured various aspects of real economic activity at ®rms ofvarious sizes, conditions at commercial banks, and national economic condi-tions. We collected data by state for each year from 1988 to 1992. 6 We col-lected data for the loan holdings, loan delinquencies, and capital positions ofindividual banks. We also used some annual macroeconomic data. We col-lected data on total statewide economic activity and on statewide number of®rms, employment, and payrolls by size of ®rm. We also collected data on theSBA's loan guarantee amounts outstanding and on the numbers of businessfailures and bankruptcies.

3.1. Construction of bank data by state

We obtained data for banks' dollar, book-value holdings of total loans,commercial and industrial loans, commercial real estate loans, and consumerloans from end-of-year Call Reports ®led by roughly 11,000 individual

5 The demand for output determined real economic activity since we do not model the statewide

supply of output here.6 Following Hancock and Wilcox (1997), we omitted data for Alaska, Hawaii, and Nevada

because their real economic activity seemed dominated by factors beyond our speci®cation. Because

its statewide bank data were dominated by the portfolios of credit-card banks, we also omitted

Delaware from our sample.


commercial banks. 7 Equity capital and loan delinquency data by bank alsocame from the Call Reports.

We classi®ed each bank as operating either ``locally'' or ``regionally'' on thebasis of the size of its asset portfolio. The local banks, de®ned to be banks withless than $5 billion of assets, were assumed to be banks whose lending andother activities took place entirely within the state in which they were head-quartered. The regional banks, de®ned to be banks with more than $5 billion ofassets, were assumed (for each year in which they held assets above that level)to operate across state lines but within one of the eight regions de®ned by theNational Council of Real Estate Investment Fiduciaries (1992).

We apportioned the dollar amount of each category of loan holdings, ofdelinquencies, and of equity capital of a large bank among the states in itsregion according to the share of the total regional personal income that eachstate accounted for. Banks' holdings of each loan category for each state werethe sum of the dollar holdings of the loan category across the local banks andthe dollar holdings of the loan category attributed to that state for the regionalbanks.

The Call Reports de®ne equity capital as the sum of perpetual preferredstock (including related surplus), common stockholders' equity, surplus, andundivided pro®ts and capital reserves adjusted for net unrealized losses onmarketable equity securities. We de®ned the volume of commercial real estateand commercial and industrial delinquent loans as the sum of the dollaramounts of loans that were past due more than thirty days but still accruinginterest and loans that were in nonaccrual status. We constructed delinquencyrates as the ratios of the loan delinquencies in each category divided by thetotal loans in each category.

3.2. Measures of aggregate real economic activity

As a measure of total economic activity in each state, we used annual grossstate product. These data came from the US Department of Commerce(1994, 1995).

Our proxy for business owners' (and presumably lenders') views aboutcurrent and future national economic conditions was the index of consumersentiment produced by the Survey Research Center (1997) at the University ofMichigan. We also used national average data for interest rates ± the nominalprime interest rate ± from the Federal Reserve Bulletin.

7 Call Report data pertained to both domestic and foreign o�ces of insured US-chartered

commercial banks.


3.3. Measures of small business activity

We gauged the size of a business by the number of employees at the ®rm. 8

For research purposes, the SBA's O�ce of Advocacy de®nes a small businessas ``an independently owned and operated ®rm with fewer than 500 employ-ees''. 9 Thus, small businesses range from 499-employee manufacturing ®rms to1-employee, part-time businesses. We present results for ®rms with 500 or moreemployees (large businesses), and for three sizes of small businesses: ®rms withless than 20 employees, ®rms with 20±99 employees, and ®rms with 100±499employees.

Using 500 employees as the dividing line between small and large businessmeans that the overwhelming majority of businesses by number are small.These small businesses employ a little more than half of all employees andaccount for a little less than half of total dollar payrolls. In our dataset, about90 percent of all ®rms had less than 20 employees. About 20 percent of em-ployees were in ®rms with less than 20 employees. Thus, like banks, mostnonbank businesses were quite small. Also like banks, a relatively smallnumber of ®rms account for at least half of employment.

No single annual data series available by state seemed adequate to sum-marize the real economic activity of small businesses. Therefore, we used dataon the numbers of ®rms, on employment, and on the annual payrolls forbusinesses in several size categories. 10

Because they seemed like candidates that could provide additional infor-mation about the economic conditions of small businesses, we collected dataon indicators of business insolvency: the numbers of business failures and ofbankruptcies. Data on business failures, de®ned as enterprises that cease op-eration with a loss to one or more creditors, were collected and published bythe Dun and Bradstreet Corporation (1994) (D&B). These businesses were nolonger on D&B's list of active businesses during their latest survey, either be-cause of failure or because of the ®ling of a bankruptcy petition. Businesses

8 We aggregated small businesses into ®rm size categories of less than 20 employees, 20±99

employees, and less than 500 employees. We also used the data on the numbers of ®rms, numbers of

employees, and annual payrolls for ®rms with more than 500 employees.9 See US Small Business Administration (1997a).10 Data were published in US Small Business Administration (1994b). These data were created

for the SBA by the Census Bureau and were compiled from the Standard Establishment List as well

as the Master Establishment List. All of the subsidiaries within a state that were a�liated with a

particular company were considered part of one ®rm. Firms with operations in more than one state

were counted more than once because ®rms are de®ned within states. Employment and annual

payroll data depended on the location of the ®rm, not on the location of the residence of the

employee.


that participated in the D&B survey tended to be established and it is likelythat the survey under-represents very small and de novo businesses.

Business bankruptcy data were provided by the Reports Division of theAdministrative O�ce of the US Courts (unpublished). These data were re-corded when businesses ®led bankruptcy petitions under Chapters 7, 11, or 12of the bankruptcy laws. A business bankruptcy is a legal recognition that abusiness is insolvent and that it must restructure (Chapter 11) or completelyliquidate (Chapter 7). 11 The SBA's O�ce of Advocacy (US Small BusinessAdministration, 1994a) argued that business bankruptcy data were more likelyto include self-employed persons and new, very small ®rms than were businessfailure data. An economic indicator particularly pertinent to the vitality of denovo businesses was the number of Chapter 7 bankruptcies.

3.4. SBA loan guaranty loan amounts

The section 7(a) Loan Guaranty Program is one of the SBA's primary ve-hicles for providing loans to small businesses that are unable to secure ®-nancing on reasonable terms through normal lending channels. This programguarantees loans provided by private-sector lenders to applicants that meetcriteria with respect to (1) the type of business, (2) the size of the business, and(3) the use of the loan proceeds. 12 The vast majority of for-pro®t businesseswith fewer than 500 employees are eligible for ®nancial assistance through thisprogram, and the loans can be used for most business purposes including thepurchase of real estate to house the business operations, construction, reno-vation or leasehold improvements; acquisition of furniture, ®xtures, machineryand equipment; purchase of inventory; and working capital. In each instance,the business must have reasonable equity invested and have used alternative®nancing sources (including personal assets) ®rst.

Although the Loan Guaranty Program is generally intended to encouragelonger-term small business ®nancing, the actual loan maturities are based onthe ability to repay, the purpose of the loan proceeds, and the useful life of theassets ®nanced. Maximum loan maturities are twenty-®ve years for real estateand equipment and ten years for working capital. Interest rates are negotiated

11 Farm businesses are liquidated under the provisions of Chapter 12.12 The proceeds of a loan guaranteed by the SBA cannot be used to ®nance ¯oor plan needs, to

purchase real estate that will be held for investment purposes, to make payments to owners or pay

delinquent taxes, or to pay existing debt unless it can be shown that re®nancing will bene®t the

small business and that the need to re®nance is not indicative of imprudent management practices.

Special considerations apply to franchises, recreational facilities and clubs, farms and agricultural

business, ®shing vessels, and holding companies. Applications are not accepted from ®rms where

the principal is incarcerated, on parole, or on probation. Businesses with speculative or gambling

purposes are ineligible. (See US Small Business Administration, 1997b.)


between the borrower and the lender, but are subject to maximum rates thatare pegged to the prime rate of interest. For ®xed rate loans, the maximuminterest rate depends on the prime rate, the amount of the loan, and the ma-turity of the loan. Variable rate loans can be pegged either to the prime rate orto a weighted average of rates the Federal Government pays for loans withmaturities similar to the average loan that is guaranteed by the SBA.

The SBA can guarantee up to eighty percent of loans of up to $100,000, andup to seventy-®ve percent of loans above $100,000. There is no legislated limitto the total amount of the loan that can be requested from the lender, but themaximum SBA guaranty amount is generally $750,000.

Data on the dollar amount of the loans approved by lenders and the dollaramount of the guarantees approved by the SBA were available for each SBAo�ce. 13 These data were aggregated to form state level data on ``gross loans''approved (i.e. the total amounts of the loans) and on guarantees approved bythe SBA. 14 These data are not the dollar amounts outstanding, but rather arethe ¯ows of newly approved and extended loans.

4. Econometric speci®cation

In this section, we describe the empirical speci®cation of the dependent andindependent variables that we used in the regressions. We also describe theestimation methods that we used. In order to eliminate state-speci®c e�ects, thedependent variables were each speci®ed as ®rst-di�erences (and no interceptwas included in the estimated speci®cation). We regressed the ®rst-di�erencesof the bank loan variables, of statewide and small-business activity, of SBAloan guarantee extensions, and of business failures and bankruptcies on a listof right-hand-side variables that, with two exceptions, was the same for eachtable. 15 Thus, in Table 1, the dependent variables are the ®rst-di�erences ofreal loans per capita, and in Tables 2 and 3 the dependent variables are the

13 There are more than 80 SBA o�ces, with at least one in each state and the District of

Columbia. Data are in US Small Business Administration (1991±1993), O�ce of Financial

Assistance.14 Dollars of gross loans and dollars of US Small Business Association guarantees at o�ces

within a state were summed to obtain the state-level values of the variables.15 First, bank loan variables replaced bank capital variables as explanatory variables in Table 2.

Second, the bank capital variables for the 1989±1990 period and the real estate loan delinquency

variable were omitted from Table 4. Data availability for the ¯ows of SBA loans granted restricted

the sample period to the years 1991 and 1992. Thus, only the 1991±1992 capital variables were

relevant. Considerable correlation over this short period between the two loan delinquency rate

variables led us to omit the measure of real estate loan delinquencies.


®rst-di�erences of real per-capita gross state product, employment, ®rms, andpayrolls, and so on.

Each dollar magnitude in Tables 1±5 was expressed in 1996 dollars per res-ident of each state. The bank capital variables entered as ®rst-di�erences of thedollar amount of bank equity capital. Statewide totals were obtained by addingup these capital changes separately for each local bank and the apportionedamount of each regional bank in the state. We added up these capital changesfor small (assets less than $300 million) and for large banks. 16 For each sizebank, we calculated two bank capital variables to see if the responses to capitalchanges di�ered over time. The ®rst had data for capital changes in the ®rst halfof the period (1989±1990) and zeros for the second half of the period (1991±1992). The second had data for capital changes in the second half of the sampleperiod (1991±1992) and zeros for the ®rst half of the sample period umentalvariables (1989±1992, by State, per capita, 1996$). Thus, we had available fourbank capital variables as explanatory variables for each row of Table 1 (and ofTables 3±5). We followed the same procedure in calculating the four total bankloan variables that we used in Table 2 in place of the four capital variables.

The index of consumer sentiment was speci®ed in levels. The nominal primeinterest rate was speci®ed in level of percentage points. Though our priors calledfor their ®rst-di�erences to enter, the empirical estimates reveal strong level butnot di�erence e�ects of sentiment and the interest rate. 17 Each of these measuresof economic conditions was lagged one year, which should ameliorate any si-multaneity bias. Our speci®cation does not allow for any other dynamics.(Fig. 3 plots both of these national measures of economic conditions.) The loandelinquency rates were each speci®ed in levels of percentage points.

We judged the right-hand-side variables, such as the same-year capital anddelinquency rates of banks or one-year-lagged national consumer sentiment orinterest rate, would not be much a�ected by the loan variables used as de-pendent variables in Table 1. Since the extent of simultaneity bias in the or-dinary least squares (OLS) estimates shown in Table 1 seemed likely to besmall, we used OLS to obtain the estimates shown in Table 1. 18

16 These small banks were 93 percent of banks by number and held 22 percent of total bank

assets in 1988.17 One reason that the level rather than the ®rst-di�erence of the index of consumer sentiment

empirically is related to the ®rst-di�erences of the loan and output variables is that the index itself

seems to be a better measure of the ®rst-di�erences than of the levels of economic conditions.

Several of the questions in the survey inquire about current conditions relative to past and expected

future conditions.18 Two-stage least squares estimates of the speci®cation used for Table 1 did not di�er markedly

from the OLS estimates that we present here. In Tables 2±5, however, simultaneity may have been

important to recognize and allow for since OLS estimates and two-stage least squares estimates

di�er considerably.


We presumed the potential for simultaneity bias was greater in speci®cationsthat linked the various measures of statewide real activity to the statewidemeasures of bank loans and bank capital. For example, not only might declinesin bank capital reduce economic activity, but weaker economic conditions,regardless of their source, might reduce banks' capital. OLS regressions ofeconomic activity on bank capital and other variables then might be impor-tantly biased by this reverse causation. To reduce simultaneity bias, we used aninstrumental variables (IV) technique to obtain the estimates shown in Ta-bles 2±5. As instruments for the bank capital variables we used two-year-lag-ged and three-year-lagged values of each of the bank capital variables;contemporaneous, one-year-lagged, and two-year-lagged values of each of theloan delinquency variables; and the one-year-lagged values of the interest ratevariable. Each of the instrumental variables was entered twice: once multipliedby a dummy variable that took the value 1 for the 1989±1990 period (and 0otherwise) and once multiplied by a dummy variable that took the value 1 forthe 1990±1991 period (and 0 otherwise). We also included as instruments thedummies for the 1989±1990 and 1991±1992 periods. 19

5. Findings

This section discusses the estimation results presented in Tables 1±5. In eachtable, the ®rst-di�erences of the annual values of statewide variables were re-gressed on the ®rst-di�erences of the change in bank capital in each state (splitboth by bank size (small and large) and by time period (1989±1990 and 1991±1992)) and on the levels of the delinquency rates of commercial real estate loansand on commercial and industrial loans and on the one-year-lagged levels ofthe index of consumer sentiment and the (nominal prime) interest rate. Thelone exception is found in Table 2, where bank loan variables were substitutedfor bank capital variables.

5.1. E�ects on bank loans

Row 1 of Table 1 displays what has become conventional wisdom aboutbank loans: losses of bank capital in the period around 1990 reduced lending.The estimates in Table 1 also imply that higher loan delinquency rates andinterest rates and reduced consumer sentiment reduced banks' total holdings ofloans. Rows 2±4 of Table 1 apply commercial and industrial loans, commercial

19 We used the same procedure to instrument the loan variables in Table 2, substituting lags of

the loan variables for lags of the capital variables.


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)1

.49

)4

.87

6.8

02.2

0)

136.8

039.5

731.2

9)

255.2

80.6

019

510

33.2

7

(0.7

5)

(3.2

4)

(7.5

3)

(3.5

5)

(7.3

6)

(1.3

7)

(5.6

4)

(5.1

1)

0.0

001

6.

Co

mm

erci

al

an

d

ind

ust

ria

l

)0

.03

)0

.77

1.2

40.3

8)

62.3

923.7

810.6

4)

90.6

90.6

842

176

47.6

7

(0.0

5)

(1.4

8)

(3.9

7)

(1.7

5)

(9.7

1)

(2.3

8)

(5.5

4)

(5.2

5)

0.0

001

7.

Co

mm

erci

al

rea

l

esta

te

)0

.54

)1

.19

1.1

3)

0.2

3)

32.7

03.5

64.1

1)

22.4

20.4

620

142

18.8

9

(0.9

8)

(2.8

6)

(4.4

9)

(1.3

5)

(6.3

4)

(0.4

4)

(2.6

6)

(1.6

2)

0.0

001

8.

Co

nsu

mer

)0

.50

)1

.00

2.9

10.3

612.0

1)

9.4

913.8

6)

132.3

50.3

3922

29

11.2

6

(0.5

6)

(1.4

7)

(7.1

8)

(1.3

1)

(1.4

4)

(0.7

3)

(5.5

6)

(5.8

9)

0.0

001


Tab

le1

(Co

nti

nu

ed)

Dep

end

ent

va

ria

ble

s

(Ba

nk

size

an

dlo

an

cate

go

ry)

Exp

lan

ato

ryv

ari

ab

les

R2

RM

SE

F-S

tati

stic

Pro

b>

FB

an

kca

pit

al

Lo

an

del

inq

uen

cyra

teE

con

om

icco

nd

itio

ns

Sm

all

ba

nk

sL

arg

eb

an

ks

Sin

gle

-fam

ily

an

d

com

mer

cial

real

esta

te

Co

mm

er-

cial

an

d

ind

ust

rial

Co

nsu

mer

sen

tim

ent

Ban

k

pri

me

inte

rest

rate

19

89±

90

19

91

±9

21

98

9±

90

1991±92

Sm

all

ba

nk

s

9.

To

tal

loa

ns

9.1

65

.79

)0

.33

)0.7

623.0

6)

34.7

50.6

2)

15.3

10.7

728

156

74.8

3

(15

.09)

(12

.60)

(1.2

0)

(4.0

1)

(4.0

6)

(3.9

3)

(0.3

6)

(1.0

0)

0.0

001

10

.C

om

mer

cial

an

d

ind

ust

ria

l

2.4

21

.48

)0

.10

)0.2

511.9

9)

18.9

01.7

8)

18.7

00.6

842

62

47.6

7

(10

.00)

(8.0

7)

(0.9

3)

(3.2

9)

(5.2

9)

(5.3

6)

(2.6

3)

(3.0

7)

0.0

001

11

.C

om

mer

cial

rea

l

esta

te

1.3

41

.25

)0

.02

)0.1

2)

2.8

6)

0.0

8)

0.4

14.0

90.4

967

51

21.7

1

(6.8

1)

(8.3

8)

(0.2

3)

(1.8

8)

(1.5

5)

(0.0

3)

(0.7

5)

(0.8

2)

0.0

001

12

.C

on

sum

er2

.49

1.4

1)

0.1

8)

0.2

19.2

3)

5.6

80.0

9)

7.2

10.6

318

58

37.7

5

(11

.01)

(8.2

6)

(1.8

0)

(2.9

5)

(4.3

7)

(1.7

3)

(0.1

5)

(1.2

7)

0.0

001

No

te:

t-S

tati

stic

sa

rein

pa

ren

thes

es.


Tab

le2

Th

ee�

ects

on

eco

no

mic

act

ivit

yo

fto

tal

ba

nk

loa

ns,

loan

del

inq

uen

cies

,an

dec

on

om

icco

nd

itio

ns.

Est

imati

on

met

ho

d:

Inst

rum

enta

lvari

ab

les

(1989±

19

92,

by

Sta

te,

per

cap

ita

,1

99

6$

)

Dep

end

ent

va

ria

ble

s

(Eco

no

mic

act

ivit

y)

Ex

pla

na

tory

va

riab

les

R2

RM

SE

F-S

tati

stic

Pro

b>

FT

ota

lb

an

klo

an

sL

oan

del

inq

uen

cyra

teE

con

om

icco

nd

itio

ns

Sm

all

ba

nk

sL

arg

eb

an

ks

Sin

gle

-fam

ily

an

dco

mm

er-

cial

real

esta

te

Co

mm

er-

cial

an

d

ind

ust

rial

Co

nsu

mer

sen

tim

ent

Ban

k

pri

me

inte

rest

rate

19

89

±9

01

99

1±

92

19

89±90

1991±92

1.

Gro

ssst

ate

pro

du

ct1

.74

0.5

4)

0.2

3)

0.0

9)

102.4

949.8

734.1

6)

258.9

20.2

818

576

8.6

3

(2.7

7)

(1.5

9)

(1.1

1)

(0.4

4)

(3.0

2)

(1.5

6)

(3.4

1)

(3.0

5)

Em

plo

ym

ent

by

®rm

size

(p

erm

illi

on

resi

den

ts)

2.

>49

9E

mp

loy

ees

)6

.14

8.0

14

.28

5.0

4)

94.6

6202.1

4129.7

8)

775.2

70.5

181

4487

23.6

6

(1.2

5)

(3.0

1)

(2.6

5)

(3.2

2)

(0.3

6)

(0.8

1)

(1.6

6)

(1.1

7)

3.

10

0±4

99

Em

plo

yee

s4

.68

2.2

10

.55

2.4

7)

243.1

4268.2

0115.6

6)

913.9

80.4

962

1981

21.6

7

(2.1

6)

(1.8

8)

(0.7

7)

(3.5

8)

(2.0

8)

(2.4

4)

(3.3

6)

(3.1

3)

4.

20

±99

Em

plo

yee

s7

.01

6.3

51

.27

3.9

1)

284.2

4369.5

931.6

2)

158.7

90.4

582

2659

18.6

1

(2.4

1)

(4.0

2)

(1.3

3)

(4.2

1)

(1.8

1)

(2.5

0)

(0.6

8)

(0.4

1)

5.<

20

Em

plo

yee

s5

.59

4.6

8)

0.1

22.5

5)

208.0

1128.7

916.9

72.4

80.4

069

1885

15.1

0

(2.7

1)

(4.1

9)

(0.1

8)

(3.8

8)

(1.8

7)

(1.2

3)

(0.5

2)

(0.0

1)

Nu

mb

ero

f®

rms

by

®rm

size

(p

erm

illi

on

resi

den

ts)

6.

>49

9E

mp

loyee

s0

.02

0.0

10

.00

0.0

0)

0.8

12.1

80.1

8)

1.0

60.4

866

12

20.8

5

(1.2

8)

(1.5

6)

(0.2

3)

(0.3

1)

(1.1

6)

(3.3

2)

(0.8

7)

(0.6

1)

7.

10

0±4

99

Em

plo

yee

s0

.06

)0

.01

)0

.01

0.0

1)

1.4

3)

0.9

91.2

5)

8.8

50.3

411

19

11.3

9

(2.8

7)

(1.3

3)

(1.0

3)

(1.7

4)

(1.2

8)

(0.9

4)

(3.8

0)

(3.1

7)

8.

20

±99

Em

plo

yee

s0

.25

0.1

10

.00

0.0

8)

8.3

26.4

32.4

9)

16.5

10.4

139

72

15.5

4

(3.2

2)

(2.6

8)

(0.0

3)

(3.2

8)

(1.9

6)

(1.6

1)

(1.9

9)

(1.5

6)

9.<

20

Em

plo

yee

s1

.10

0.7

6)

0.0

50.2

3)

47.0

219.7

41.1

720.3

70.3

220

337

10.4

5

(2.9

9)

(3.7

7)

(0.4

3)

(1.9

4)

(2.3

6)

(1.0

5)

(0.2

0)

(0.4

1)


Ta

ble

2(C

on

tin

ued

)

Dep

end

ent

va

riab

les

(Eco

no

mic

act

ivit

y)

Exp

lan

ato

ryv

ari

ab

les

R2

RM

SE

F-S

tati

stic

Pro

b>

FT

ota

lb

an

klo

an

sL

oan

del

inq

uen

cyra

teE

con

om

icco

nd

itio

ns

Sm

all

ba

nk

sL

arg

eb

an

ks

Sin

gle

-fam

ily

an

dco

mm

er-

cial

real

esta

te

Co

mm

er-

cial

an

d

ind

ust

rial

Co

nsu

mer

sen

tim

ent

Ban

k

pri

me

inte

rest

rate

19

89±

90

19

91

±9

21

98

9±

90

1991±92

Rea

lp

ay

roll

by

®rm

size

10

.>

49

9E

mp

loy

ees

0.2

40

.07

)0

.05

0.0

1)

6.2

8)

9.1

111.5

0)

87.7

00.3

504

126

11.8

7

(1.7

7)

(0.9

7)

(1.0

4)

(0.2

2)

(0.8

4)

(1.3

0)

(5.2

5)

(4.7

2)

11

.1

00

±4

99

Em

plo

yee

s0

.24

0.0

1)

0.0

30.0

4)

6.6

12.1

14.6

9)

37.3

70.2

917

69

9.0

6

(3.1

6)

(0.3

0)

(1.0

8)

(1.6

2)

(1.6

3)

(0.5

5)

(3.9

4)

(3.7

0)

12

.2

0±

99

Em

plo

yee

s0

.23

0.0

70

.00

0.0

6)

8.0

03.6

81.7

9)

12.2

00.3

612

65

12.4

4

(3.1

8)

(1.8

5)

(0.0

4)

(2.4

8)

(2.0

8)

(1.0

2)

(1.5

8)

(1.2

7)

13<

20

Em

plo

yee

s0

.20

0.0

4)

0.0

20.0

3)

5.7

3)

2.5

11.2

7)

6.9

20.3

003

56

9.4

4

(3.1

9)

(1.1

5)

(0.7

8)

(1.5

2)

(1.7

2)

(0.8

0)

(1.3

0)

(0.8

3)

No

te:

t-S

tati

stic

sa

rein

pa

ren

thes

es.


Tab

le3

Th

ee�

ects

on

eco

no

mic

act

ivit

yo

fb

an

kca

pit

al,

loa

nd

elin

qu

enci

es,

an

dec

on

om

icco

nd

itio

ns.

Est

imati

on

met

ho

d:

Inst

rum

enta

lvari

ab

les

(1989±1992,

by

Sta

te,

per

cap

ita

,1

99

6$

)

Dep

end

ent

va

ria

ble

s

(Eco

no

mic

act

ivit

y)

Ex

pla

na

tory

va

riab

les

R2

RM

SE

F-S

tati

stic

Pro

b>

FB

an

kca

pit

al

Lo

an

del

inq

uen

cyra

teE

con

om

icco

nd

itio

ns

Sm

all

ba

nk

sL

arg

eb

an

ks

Sin

gle

-fam

ily

an

dco

mm

er-

cial

real

esta

te

Co

mm

er-

cial

an

d

ind

ust

rial

Co

nsu

mer

sen

tim

ent

Ban

k

pri

me

inte

rest

rate

19

89

±9

01

99

1±

92

19

89±90

1991±92

1.

Gro

ssst

ate

pro

du

ct1

6.1

88

.09

1.0

72.2

4)

60.2

315.6

922.7

1)

193.7

70.3

674

531

12.7

8

(4.0

7)

(2.3

2)

(0.6

6)

(2.9

7)

(2.8

8)

(0.4

6)

(3.8

2)

(3.6

6)

0.0

001

Em

plo

ym

ent

by

®rm

size

(p

erm

illi

on

resi

den

ts)

2.

>49

9E

mp

loy

ees

24

.88

)6

9.4

81

1.9

8)

22.8

6)

408.5

2)

120.9

1352.1

3)

2454.1

80.5

748

4061

29.7

4

(0.8

2)

(2.6

1)

(0.9

7)

(3.9

6)

(2.5

6)

(0.4

6)

(7.7

6)

(6.0

7)

0.0

001

3.

10

0±4

99

Em

plo

yee

s2

6.6

23

.34

)2

.50

)1.1

5)

485.0

4296.4

0138.7

7)

1073.1

00.5

441

1731

26.2

5

(2.0

6)

(0.2

9)

(0.4

7)

(0.4

7)

(7.1

3)

(2.6

7)

(7.1

7)

(6.2

2)

0.0

001

4.

20

±99

Em

plo

yee

s5

4.5

9)

10

.00

3.0

9)

5.9

9)

562.2

1243.3

3102.7

6)

709.8

70.5

098

2281

22.8

8

(3.2

0)

(0.6

7)

(0.4

4)

(1.8

5)

(6.2

7)

(1.6

6)

(4.0

3)

(3.1

2)

0.0

001

5.<

20

Em

plo

yee

s3

6.0

6)

1.9

10

.89

)6.8

0)

320.9

052.9

938.5

8)

171.0

50.4

477

1588

17.8

3

(3.0

4)

(0.1

8)

(0.1

8)

(3.0

1)

(5.1

4)

(0.5

2)

(2.1

7)

(1.0

8)

0.0

001

Nu

mb

ero

f®

rms

by

®rm

size

(p

erm

illi

on

resi

den

ts)

6.

>49

9E

mp

loyee

s0

.27

)0

.12

)0

.01

)0.0

3)

0.1

40.4

00.2

3)

1.0

70.5

183

12

23.6

7

(3.1

0)

(1.6

3)

(0.2

8)

(1.9

7)

(0.3

1)

(0.5

4)

(1.7

5)

(0.9

3)

0.0

001

7.

10

0±4

99

Em

plo

yee

s0

.12

0.1

3)

0.0

60.0

8)

3.5

10.6

80.8

2)

5.2

60.5

232

13

24.1

4

(1.2

3)

(1.5

2)

(1.5

8)

(4.5

0)

(6.9

1)

(0.8

2)

(5.6

8)

(4.0

8)

0.0

001

8.

20

±99

Em

plo

yee

s1

.44

0.1

50

.09

0.0

8)

14.3

95.8

82.8

1)

19.8

90.4

857

59

20.7

8

(3.2

6)

(0.3

9)

(0.5

1)

(0.9

3)

(6.1

9)

(1.5

5)

(4.2

5)

(3.3

7)

0.0

001

9.<

20

Em

plo

yee

s7

.00

2.6

50

.84

0.0

9)

49.7

68.0

01.5

45.4

60.3

709

286

12.9

7

(3.2

7)

(1.4

1)

(0.9

6)

(0.2

1)

(4.4

3)

(0.4

4)

(0.4

8)

(0.1

9)

0.0

001


Tab

le3

(Co

nti

nu

ed)

Dep

end

ent

va

riab

les

(Eco

no

mic

act

ivit

y)

Ex

pla

na

tory

va

riab

les

R2

RM

SE

F-S

tati

stic

Pro

b>

FB

an

kca

pit

al

Lo

an

del

inq

uen

cyra

teE

con

om

icco

nd

itio

ns

Sm

all

ba

nk

sL

arg

eb

an

ks

Sin

gle

-fam

ily

an

dco

mm

er-

cial

real

esta

te

Co

mm

er-

cial

an

d

ind

ust

rial

Co

nsu

mer

sen

tim

ent

Ban

k

pri

me

inte

rest

rate

19

89±

90

19

91±

92

19

89±90

1991±92

Rea

lp

ay

roll

by

®rm

size

10

.>

49

9E

mp

loy

ees

1.3

61

.50

0.2

10.5

4)

5.5

8)

5.1

29.8

1)

80.2

70.4

280

112

16.4

6

(1.6

2)

(2.0

4)

(0.6

2)

(3.3

7)

(1.2

7)

(0.7

1)

(7.8

2)

(7.1

8)

0.0

001

11

.1

00

±4

99

Em

plo

yee

s0

.64

0.7

8)

0.0

90.2

6)

12.0

17.0

23.3

6)

27.8

80.4

668

47

19.2

6

(1.8

2)

(2.5

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real estate loans, and consumer loans to the same speci®cation of explanatoryvariables. The results across loan categories do not di�er markedly.

Row 1 of Table 1 demonstrates that on balance total loan holdings re-sponded by similar amounts per dollar of capital change at small and largebanks. It also indicates that the response of loans tended to be much smaller inthe later period than in the earlier period. One reason may be that capitalincreases in the later period partially re¯ected higher minimum e�ective capitalrequirements, so that capital coe�cients on capital increases in the later periodre¯ect a combination of increased capital pressure from raised requirementsand eased capital pressure associated with increased holdings of capital. In theearly period, capital increases probably resulted less from increased capitalrequirements, and thus the coe�cients associated with the capital variables forthe earlier period may be better estimates of the e�ect on loans of a change incapital not associated with a change in regulatory standards. Row 1 suggeststhat the e�ects on loans might well be twice as large as those reported by Peekand Rosengren (1995b).

Rows 5±8 of Table 1 use the uniform speci®cation to estimate the responsesof loans held by large ($300 million or more in assets) banks. Since these largebanks hold more than three-fourths of total bank assets, the estimated re-sponses are generally similar to those reported for total loans. Rows 9±12 ofTable 1 display the results for small banks (less than $300 million in assets).Both large and small banks reduced their loans when they lost capital.

Another similarity between large and small banks was their tendency torespond inversely to capital changes at other banks. Large banks' loans, takentogether and by category, showed some tendency to rise when capital fell atsmall banks: Each of the estimated coe�cients associated with capital at smallbanks was negative in the equations that accounted for loans at large banks.Rows 9±12 of Table 1 show that the converse was also true. Thus, borrowersdenied loans because their banks were under capital pressure seem to have beenable to o�set partially those e�ects by going to banks under less capital pres-sure. This result is similar to one presented in Hancock and Wilcox (1994b),where loans rose at banks whose neighboring banks had su�ered increasedloan delinquencies. 20 Note also that the o�setting, or indirect, e�ects weresmaller than the direct e�ects on lending of changes in bank capital. The es-timated increases in bank loans at large banks per dollar reduction in capital atsmall banks was smaller than the reduction in loans at small banks. Thus, assuggested by Row 1 of Table 1, total lending declined when bank capital fell.

The responses of large banks did di�er somewhat from those of small banks.One of the more noticeable di�erences was that loans at large banks respondedsigni®cantly to changes in consumer sentiment and the interest rate, whereas

20 See also Berger et al. (1998).


loans at small banks typically did not. This might re¯ect di�erences in thetypical borrowers at banks of di�erent sizes. Larger banks may well haveborrowers, such as manufacturers, who are more tightly linked to the durablegoods sector where consumer sentiment and interest rates may be more im-portant.

Another di�erence was that loans contracted more at small banks per dollarof capital pressure than they did at large banks. The responses at small bankswere about half again as large as they were at large banks. The di�erenceseemed concentrated in commercial and industrial loans at small banks. Incontrast, the large banks responded much more to small banks' loss of capitalthan the other way around. In response to capital reductions at small banks(and reductions in loans at small banks), large banks increased their lending(row 5 of Table 1). This partially o�setting increase in lending meant that totallending declined by the same amount regardless of whether capital declined atlarge or small banks.

One possible reason that loans at large banks expanded when small bankshad capital reductions was that large businesses may have taken out loans fromlarge banks to fund trade credit for their small business customers. 21 Anotherreason for the di�erential response to others' capital pressures is that, whilelarge banks may be able to handle all the borrowing demands of many smallbusinesses, a small bank may not have the capacity to fund the borrowingstypical of even one large ®rm. Consider an example: Suppose that the smallestlarge ®rm has 500 employees who each produce $100,000 in annual grossoutput, so that the ®rm has $50 million in annual sales. With an inventory tomonthly sales ratio of two, the ®rm has over $8 million in inventory. Supposethe largest small bank has $300 million in assets, a 10 percent capital ratio, andis prohibited by regulations from making a loan equal to more than 10 percentof its capital to any one borrower. That makes its maximum loan size equal to$3 million. In this example, the largest small bank could not fund half of theinventory being carried by the smallest large ®rm. It is of course possible thatlarge ®rms will sometimes have smaller ®nancing needs than that calculated inthis example, but it is also readily seen that many small banks are not likely tobe able to provide su�ciently large loans for many large businesses.

5.2. E�ects on real economic activity

The results in Table 1 focus on the responses of bank loans to bank capitaland other factors. As we noted above, bank loans generally responded to thesefactors in the expected direction and di�erences in the magnitudes of the re-sponses generally corresponded to our priors. Our speci®cation also accounted

21 See Calomiris et al. (1995) and Petersen and Rajan (1994).


for a good deal of the variance in our pooled sample of changes in bank loansin toto and by category.

Our primary concern, however, was the response of real economic activity tochanges in bank loans, in bank capital, and in other factors. If borrowers hadclose substitutes for bank credit, real economic activity might have been littlea�ected by banks' problems. In that case, the health of the banking sectormight have been of little importance and received little attention by policy-makers.

To the extent that ®rms were not able to easily replace the supply of creditthat had come from banks, however, we would expect the coe�cients (obtainedby our IV technique) in Tables 2 and 3 to be consistent with those in Table 1.Indeed, we might regard Table 3, which relates bank capital to output, as beingakin to the ``reduced form'' of Tables 1 and 2, which relate bank capital tobank loans and bank loans to output.

Table 2 shows the IV-estimated e�ects on our measures of real economicactivity of bank loans, loan delinquencies, and economic conditions. The bankloan variables were split by bank size and by time period, in the same way thatthe bank capital was split in Table 1. Rows 2±13 in Table 2 show in threeblocks of four rows each the responses across ®rm size of employment, num-bers of ®rms, and payrolls. (Though ®rms with more than 500 employees arenot typically considered to be small businesses, their responses are shown forcompleteness and for comparison purposes.)

Reading across the rows of Table 2 shows that dollar-for-dollar reductionsin loans made by small banks generally depressed all measures of economicactivity by noticeably larger amounts than reductions in loans made by largebanks did. In seven of the eight comparisons in rows 2±5 for the two timeperiods, the point estimates for the e�ects on employment of a decline in loansat small banks exceeded the point estimates associated with large banks. In thatsense, small banks were supplying ``high-powered loans''. Per dollar of loan,loans supplied by small banks apparently had a larger e�ect on total economicactivity than loans supplied by large banks.

Some care is required to interpret Table 2 further. (The same caution appliesto Table 3.) To compare the e�ects of changes in bank loans on small busi-nesses (e.g., those with less than 500 employees) to their e�ects on large busi-nesses (e.g., those with 500 or more employees), compare the sum thecoe�cients for the three components of total small business (rows 3±5) with thecoe�cient in row 2. As we noted above, under this criterion for small business,about half of all employees work in small businesses. We also note that thereare many fewer larger ®rms, which makes comparing the e�ects on the numbersof ®rms across rows problematic.

Not surprisingly, the sums of the coe�cients across rows indicates thatchanges in small bank lending had much less e�ect on employment at largebusinesses than at the sum of all small businesses, especially in the 1989±1990


period. In contrast, during the same period lending at large banks seems tohave had more e�ect on large businesses than on small businesses. The nonloanvariables a�ected output in the expected way: Higher loan delinquency andinterest rates tended to reduce output, while higher consumer sentiment tendedto raise output.

The speci®cation in Table 3 substitutes the bank capital variables for thebank loan variables used in Table 2. Row 1 of Table 3 shows that gross stateproduct declined when bank capital did. 22 The e�ects on gross state product ofper dollar of capital loss at small banks were much larger than those associatedwith large banks.

Although the numbers of employees and ®rms provide independent infor-mation about activity, we focus our discussion of Table 3 on the real-dollarpayroll results, which we regard as largely though not completely encom-passing and representative of the results based on employee and ®rm data. Ingeneral, ®rms of all sizes reduced their payrolls, though not always by statis-tically signi®cant amounts, when bank capital declined. As in Table 2, theestimated e�ects on payrolls of capital losses at small banks were larger thanthose of capital losses at large banks in each of the eight comparisons possiblein rows 10±13.

Consistent with the results of Table 2, the sum of the small business re-sponses (rows 11±13) to capital changes at small banks considerably exceededthe responses of large businesses (row 10). The estimates shown in Table 1imply that a loss of $1 million of bank capital at small banks during 1989 or1990 reduced total loans at small banks by $9 million (row 9) and raised totalloans at large banks by $1.5 million (row 5). Row 1 of Table 3 implies that a $1million decline in bank capital reduced gross state product by $16 million, orabout double the $7.7 million decline in total bank loans. Further, Table 3implies that employment fell by 142 jobs (calculated by summing the ®rstcolumn coe�cients in rows 2±5) and that payrolls fell by nearly $4 million(calculated by summing the coe�cients in rows 10±13). These declines werefairly evenly spread over businesses of various sizes.

Large banks may have su�ered the largest capital depletions during thisperiod, but per dollar of capital loss, the e�ects of losses of capital at smallbanks were considerably larger. One reason may be that many of the customersof large banks can obtain credit from other large banks or even from nonbanksources, such as ®nance companies and the commercial paper market. Smallbanks' customers may not be able as quickly to arrange for credit throughother small banks, through large banks, or through nonbank sources. This may

22 The estimates based on the broader measure of economic activity used here, gross state

product, supplements the Hancock and Wilcox (1997) ®nding that real activity in the real estate

sector was hampered by declines in bank capital in the years around 1990.


occur because other lenders may be more informed about the capital conditionof larger banks. If so, borrowers denied renewing their credits at a large bankmay be better able to convince their next lender that the denial resulted fromthe bank's capital depletion rather than the condition and prospects of theborrower. In contrast, borrowers denied credit at small banks whose condi-tions are less well known may require more time and e�ort to convince theirnext lender that they are creditworthy.

Dunkelberg and Dennis (1992) and Cole and Wolken (1995) reported thatthe fraction of small businesses that uses bank credit rises with ®rm size. It isalso generally agreed that very large ®rms, which may have direct access tocommercial paper, bond, and equity markets, use banks less for credit thansmall ®rms do. If we equate the extent of bank-dependency to the share of®rms' credit that they obtain from banks, bank-dependency traces out aninverted-U shape with respect to ®rm size. That suggests that the pattern across®rm sizes of responses to banking sector shocks might also trace out aninverted-U-shaped pattern.

The coe�cients on capital in Table 3 do not mirror that pattern across ®rmsizes of bank-dependency. For example, the capital coe�cients on payrollsshown in rows 10±13 for both time periods for both small and large banksoscillate across ®rm size. Thus, the hypothesis that responses to capital changesacross ®rm sizes would follow an inverted-U-shaped pattern received littlesupport.

5.3. E�ects of SBA loan guaranty program

One socially useful role that a government loan guaranty program couldplay is to increase its supply of loans when private-sector banks are hit byshocks, either economic or regulatory, that ine�ciently reduce the currentsupply of bank credit. That ine�ciency might arise, for example, when a bank'seconomic net worth became negative as a result of past decisions and shocksand the bank could not raise su�cient capital to persuade the capital regulatorsto allow it to fund a positive net present value project. Given the capitalpressures that banks were under through the early 1990s, we sought infor-mation about the extent to which SBA-guaranteed lending rose or fell withbank capital pressures and other conditions.

Table 4 presents the results of regressing the ®rst-di�erences of the ¯ows ofthe gross and of the guaranteed amounts of SBA section 7(a) loans on some ofthe explanatory variables that we used in Tables 1±3. The di�erence betweenthese two dependent variables is the amount of the loan for which the bank isnot guaranteed repayment, i.e., the amount at risk. Because that amount tendsto be a fairly steady proportion of the gross loan amount, the results shown inrows 1 and 2 of Table 4 are virtually identical to each other. Because we haddata only for years 1990±1992 and thus ®rst-di�erences of the ¯ows of


lending only for 1991 and 1992, coe�cients for the large and small bank capitalchange variables for 1988±1990 could not be estimated. Because the loan de-linquency variables were highly correlated with each other during this short timeperiod, we omitted the real estate loan delinquency rate.

Although the F-statistics point toward a signi®cant overall relation, none ofthe individual coe�cients in Table 4 were statistically signi®cant. Not toosurprisingly, SBA lending fell with interest rates and rose with consumer sen-timent. Of course, demand e�ects alone might account for that perhaps-de-sirable reaction. Higher loan delinquency rates also showed some tendency toreduce SBA lending.

Losses of capital at large banks produced barely perceptible declines in SBAloan originations, while losses of capital at small banks hinted weakly that SBAloans would rise. Compared with their vigorous estimated reductions in lendinggenerally shown in Table 1, these changes are noteworthy for being so (statis-tically) negligible. In that regard, SBA lending programs might be regarded as acredit market stabilizer in that SBA lending slowed far less than total lendingand may have even risen in response to adverse bank capital shocks.

5.4. E�ects on business failures and bankruptcies

Table 5 presents the results of applying the speci®cation we used in Tables 1and 3 to data on business failures and bankruptcies. Since failures of largebusinesses account for only a very small proportion of the total number ofbusiness failures, we interpret the results in Table 5 as applicable to smallbusinesses. In contrast to the continuous dependent variables used above,business failure and bankruptcy are more discrete events. As such they maycontain information about responses to bank capital and other shocks that isindependent of the measures we used above.

The results in Table 5 are generally weak. For example, there is not a de-tectable relation between the number of Chapter 7 bankruptcies and the list ofexplanatory variables we used, individually or taken together. Few of the co-e�cients in rows 2 or 3 signi®cantly drive total business bankruptcies. Onereason may be that during this period changes in bankruptcy laws funda-mentally altered the relation between bankruptcy ®lings and economic condi-tions. Another reason may be that bankruptcy responds with longer lags thanour speci®cation allowed.

Row 1 of Table 5, however, does reveal some statistically signi®cant e�ectson business failures. There we see that higher real estate loan delinquency ratesand higher interest rates and lower consumer sentiment raised the number ofbusiness failures. Those results ®t our priors. The estimated e�ects of changes inbank capital were mixed, signs on the capital variables being positive as often asthey were negative and never signi®cant. Thus, these results are too weak toconclude that bank capital changes a�ected business failures and bankruptcies.


6. Conclusions

A number of empirical studies have demonstrated that declines in bankcapital, loan delinquencies, and local economic conditions help explain thedeclines in bank loans in the period around 1990. Less well documented havebeen the real economic repercussions, if any, of the shocks that struck banks atthat time. We have presented estimates of how much bank loans and howmuch real activity at businesses of all sizes declined when bank capital andloans declined and other bank portfolio and aggregate economic conditionsdeteriorated.

Using data for 1989±1992 by state, we estimated the e�ects on banks'business and consumer loans as well as on employment, payrolls, and thenumbers of ®rms by ®rm size and on gross state product. In response to capitaldeclines at their own banks, small banks reduced their loan holdings by largeramounts than did large banks; the responses at small banks were about halfagain as large as those at large banks; the responses of commercial and in-dustrial loans were particularly large. Supporting the hypothesis that bankcustomers have alternative sources of credit, banks, and in particular largebanks, raised their loan holdings when other banks reduced loans in responseto capital pressures. Thus, other banks partially o�set reductions in the supplyof credit from banks that lost capital. This does not imply that the sameborrowers that were denied credit at their small banks were the bene®ciaries ofthe increased credit provided by the large banks. Losses of capital at smallerbanks then produced larger direct declines in loans at small banks and largero�setting, increases in loans at large banks; both the direct and indirect e�ectson loans of capital losses at small banks were larger than those for capitallosses at large banks. The o�setting increases in lending by large banks in re-sponse to capital pressures at small banks were large enough that the change intotal loans was about the same, regardless of whether a large or small bank lostcapital. On balance, however, reductions in bank capital at either small or largebanks led to reduced bank lending. Not surprisingly, factors like loan delin-quency rates and macroeconomic conditions also a�ected banks' loan holdings.

We estimated that real economic activity was reduced by declines in bankcapital. Even though the net e�ect on total loans was similar regardless ofwhich size bank lost capital, gross state product declined more when smallbanks lost capital than when large banks lost capital. Payrolls, employment,and the numbers of ®rms also declined more in response to capital losses atsmall banks than to capital losses at large banks. The results in Table 2 providesome insight into the mechanism by which this occurs in showing directly that,dollar-for-dollar, loans made by small banks a�ected output more than loansmade by large banks. In that sense, small banks made ``high-powered loans''.

The estimates hinted that economic activity at (the aggregate of) small ®rmswas a�ected more per dollar of bank capital loss than economic activity at


large ®rms, regardless of whether the loss was incurred by small or large banks,but the statistical case for this hypothesis was not very strong.

Small ®rms may have had fewer close substitutes for bank loans (and per-haps more signi®cant complements) available than did large ®rms. To theextent that small ®rms tended to have lower debt-equity ratios than larger®rms, loans to small business may have a larger ``multiplier'' e�ect on total®nancial resources available to the ®rm. Such a multiplier e�ect might arise ifequity investors, such as angel ®nanciers or seed money funds, were enticed toparticipate in the ®rm when a lender like a bank committed to take on the®rm's debt. 23 Similarly, if the ability of small businesses to obtain credit fromnonbank sources is a�ected more by obtaining bank credit than is the ability oflarge ®rms to get nonbank credit when they obtain bank credit, bank loans tosmall businesses may have a larger ®nancial, and therefore real activity, mul-tiplier than bank loans to large businesses. Thus loans to small business mayalso be ``high-powered'' loans.

The combination of the larger e�ects of small banks with the larger e�ectson small business may render small ®rms associated with small banks the mostvulnerable to banking sector adjustments.

Acknowledgements

Kara Meythaler, Stacy Panigay, and Stephanie Russek provided superbtechnical support. Financial support from the Berkeley Program in Financeand from the Center for Real Estate and Urban Economics is gratefully ac-knowledged. For their comments we thank Allen Berger, Robert A. Eisenbeis,Mark Flannery, Joe Peek, Eric Rosengren, and Gregory Udell. All opinionsexpressed herein are the authors' and are not necessarily those of the Board ofGovernors of the Federal Reserve System, the Federal Reserve Banks, or theirsta�s; likewise, any errors are solely the responsibility of the authors.

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