American Economic Association
Are Banks Really Special? New Evidence from the FDIC-Induced Failure of Healthy Banks
Author(s): Adam B. Ashcraft
Source: The American Economic Review, Vol. 95, No. 5 (Dec., 2005), pp. 1712-1730
Published by: American Economic Association
Stable URL: http://www.jstor.org/stable/4132774
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Are Banks ReallySpecial? New Evidencefromthe
FDIC-InducedFailureof HealthyBanks
By ADAMB. ASHCRAFT*
Why arebanks so highly regulated?There are
probably several reasons, but one of the more
importantis a common belief that bank failures
involve significant macroeconomic costs. This
view has been shaped in partby the experience
of the United States during the Great Depression. Ben S. Bernanke (1983) documents the
severe contractionin bank lending in the early
1930s, which was associated with widespread
bank failures, and finds evidence that failed
bank deposits have marginalexplanatorypower
beyond the money supply in explaining the
variance of industrial production during this
period.' More recently, Charles W. Calomiris
and Joseph R. Mason (2003) use an instrumental variablesstrategywith panel data in orderto
identify bank loan supply shocks and trace out
their effect on local areaincome over the 19301932 period, estimating an elasticity of real
state income growthto bankloan supply growth
of 45 percent.2While there is some disagreement in the literatureover the precise mechanism throughwhich failure affects real activity,
it is hard to walk away without the conclusion
that bank failures played an importantmacroeconomic role in the severity of the economic
downturn.3
What are the possible mechanisms?The most
directeffect is throughthe loss of real wealth by
uninsureddepositors and other creditors. Even
in the absence of a wealth effect, however, the
creditors of a failed bank lose liquidity while
they wait for assets to be liquidated, which in
turn affects real spending in the presence of
borrowingconstraints.Moreover, when a bank
fails, some long-standingrelationshipswith its
customers are disrupted, if not destroyed. If
customers are unable to replace these relationships with other lenders on equal terms, this
contractionin the supply of bank credit has an
effect on real activity. Finally, thereis the threat
of contagion,where the failureof one institution
prompts a run on other banks, spreading the
effect of failure throughoutthe economy.
While the existing literature suggests that
bank failures were importantduring the 1930s,
this paper addressesthe question of whether or
not bankfailures still matter.The answerto this
question is not obvious for at least three reasons. First,the creationof deposit insurancehas
significantlyreduced the negative wealth effect
of failuresandmore or less eliminatedthe threat
of contagion. Second, establishing the Federal
Deposit Insurance Corporation (FDIC) as receiver has minimized the illiquidity of failed
bank deposits, as well as the claims of other
creditors,and shortenedthe overall contraction
in loan supply. Finally, the U.S. economy has
likely become less bank dependent since the
*
Banking Studies, FederalReserve Bank of New York,
33 Liberty Street, New York, NY 10045 (e-mail:
[email protected]).Sincere thanks to Jonathan
Guryan and Hoyt Bleakley, as well as participantsof the
University of Chicago GSB Macro lunch, the 2003 Federal
Reserve System Banking Conference, the 2004 Winter Finance Workshop, the 2004 Financial IntermediationResearch Society Conference, the 2004 European Financial
Management Association Conference, the Department of
Economics at the Universityof Californiaat San Diego, and
the Federal Reserve Bank of New York Banking Studies
lunch for their constructive comments. I also appreciate
excellent research assistance from Chris Metli and Sam
Hanson. Any remainingerrorsare my own, and the views
expressed here are those of the authorand not of the Federal
Reserve Bank of New York or the FederalReserve System.
the more important
'This view is not uncontested,as the observed contrac3 Hugh Rockoff (1993) argues that
tion in bank lending could reasonablyhave been caused by
effect of bank failuresis the illiquidity of suspendeddeposa decline in loan demand related to depressed business
its. When using a quality-adjustedmeasure of the money
conditions or promptedby a deflation-induceddeterioration
supply, Rockoff determinesthat nonmonetaryvariables are
in firm creditworthiness.
not necessary to explain the severity of the downturn.On
2 The
the other hand, Ali Anari et al. (2003) separatelyconclude
point estimate implies that a one-standarddeviation decrease in loan growth over three years (17.9
that the stock of suspended deposits is as important as
percent) reduces output growth over three years by about 7
money stock in explaining output change over forecast
horizons of one to three years during the 1930s.
percentagepoints.
1712
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
1930s. In particular, recent studies (John C.
Driscoll, 2004, and Ashcraft, forthcoming) estimate that the elasticity of real state income to
bankloan supply in more recentyears is close to
zero, and is likely no larger than 10 percent.4
Together, these three changes in the financial
system raise the questionof whetheror not bank
failures still matter.
This question takes on greaterrelevance as a
recent empirical literaturehas struggled to establish a convincing connection between bank
failures and local area economic activity. In a
study of ruralcounties in Kansas,Nebraska,and
Oklahoma from 1981-1986, R. Alton Gilbert
and Levis A. Kochin (1989) find weak evidence
that bank failures are followed by a decline in
economic activity,but only if the bankis closed.
In anotherpaper, Robert T. Clair et al. (1994)
find little correlationbetween bank failures and
ruralcounty output in Texas during the 1980s.
While there are likely severe endogeneity problems associated with a simple regression of
local area real economic activity on lags of
failed bank deposits, as is done in the literature,
the most natural bias is that such an analysis
overstates the effect of bank failure.
Ideally, one would like to randomly assign
failuresto banks,then step back and watch what
happens to real economic activity. Fortunately
or unfortunately,bank regulators lack the authorityto run such experiments,so we are left to
identify bank failures that occurredfor reasons
that have little to do with local area economic
activity. In this paper, I study two incidents
when healthy subsidiariesof a multi-bankholding company failed following the failure of unhealthy lead banks:
1. The cross-guaranteeprovision of Financial
InstitutionsReform, Recovery, and Enforcement Act of 1989 (FIRREA) permits the
FDIC to charge off any expected losses related to the failure of one subsidiarybank of
a multi-bankholding company to the capital
of a related subsidiary bank. In October
1992, the FDIC exercised this authority
4 Ashcraft
(forthcoming) documents that the effect of
monetary policy on bank lending aggregated to the state
level is mitigated by the share of banks affiliated with
multi-bank holding companies in the state, but that the
effect of monetary policy on state output is unaffected by
the share of these financially unconstrainedbanks.
1713
when the lead banks of First City Bancorporation were declaredinsolvent, in partdue to
losses on loans to Iraqi banks made before
the Gulf War, as well as continuedweakness
in the Dallas and Houston commercial real
estate markets. As the insurer expected
losses of $500 million, and the 18 other
subsidiaries held less than $300 million in
primary capital, these other banks also
failed.
2. In January1988, the FDIC provided $1 billion of open-bank assistance to the lead
banks of First RepublicBankCorporationin
the form of a six-month loan. The note was
guaranteedby the other 38 subsidiaries of
the holding company and was collateralized
by the equity that the parent had in these
subsidiaries. When the FDIC chose not to
renew the assistance loan in July 1988, the
lead banks defaultedand the insurerclaimed
its collateral, failing the non-lead banks in
the holding company.
In both of these cases, subsidiarybanks of a
multi-bankholding company failed for reasons
that were arguably independent of local area
economic conditions. Figure 1A makes this
point clear in the First City Bancorporationcase
by illustratingthe relationshipbetween the primary capital ratio of the healthy subsidiaries
(triangles), of all nonfailing banks operatingin
the same county (squares), and of all other
Texas banks that failed in 1988 (circles). The
capital ratio of the healthy banks is slightly
lower than that of its peers in the two years
before failure, but changes in the ratio more
closely track movements in the peer ratio than
movementsin the failed bankratio.5While these
capitalratiosarebook measures,Figure 1B tells a
similarstoryfor the ratioof nonperformingloans
5 The lower capital ratio of banks affiliated with largebank holding companies is a persistent phenomenon, consistent with the idea that these banks are much larger than
their actual size indicates. In 1991:IV, the average primary
capital ratio was 9.4 percent for stand-alonebanks and 8.6
percent for banks affiliated with multi-bankholding companies in the population of commercial banks. There is a
similar patternin 1987:IV, where the primarycapital ratio
was 10.2 percent for stand-alonebanks and 9.1 percent for
banks affiliated with multi-bank holding companies. See
Murillo Campello (2002) for evidence that the presence of
internalcapital marketsin bank holding companies reduces
the financial constraintsfaced by subsidiarybanks.
1714
THEAMERICANECONOMICREVIEW
ae .
UnhealthyFailures1992
Non-failures
1992
-
-
DECEMBER2005
HealthyFailures1992
0.08-
0.06
0.04
0.02-
00
2
4
QuartersuntilFailure
6
8
FIGURE
TOASSETSOFTEXASBANKS(1992)
IA. PRIMARY
CAPITAL
to assets, which reflects differences in market
value. In Figure IC the primarycapital ratio of
the healthy subsidiaries(triangles) of First RepublicBankCorporationis again lower than the
capital ratio of peer institutions, but better
tracks movements in the peer ratio than the
capital ratio of all banks that failed in Texas in
1988. The healthy FirstRepublicsubsidiariesdo
have more problemassets, but changes in problem assets more closely track problems in peer
institutionsthan in failing institutions. In summary, while there are some differences between
these healthy banks and their nonfailing peer
institutions, especially in the case of First RepublicBank, there are much larger differences
between these healthybanks and all otherbanks
that failed in Texas in 1988 and 1992. The
bottom line is that there is clearly something
special about these failures.6
The use of the phrase "healthybank"is not intendedto
imply thatthese failures were unjustifiedor inappropriate.It
is certainly true that each failing healthy institutionhad an
off-balance-sheet contingent liability that rendered each
6
The paper proceeds as follows. Section I
investigates the simple lessons learned from
ordinaryleast squares (OLS), while Section II
describes the main institutional details behind
the FDIC-induced failure and resolution of
healthy banks. Section III describes the data
and analysis related to the natural experiments, and Section IV concludes.
I. A First Pass
Before turningto the naturalexperiments,it
is useful to take a careful look at the correlation
between failure and local area real economic
activity. Using data from the Bureau of Economic Analysis (BEA), I construct a panel of
annual real county income for 1969-2000, de-
bank insolvent. The key insight is, however, that these
failures were caused by factors that were plausibly exogenous to local area economic activity. Moreover, in absence
of a relationshipto other failing banks, none of these institutions would have failed when they actually did.
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
Non-failures1992
.-.--e-- HealthyFailures1992
A---
1715
UnhealthyFailures1992
0.08-
0.06 -
0.04-
0.02...
•
2 - -
...... ....
- ---
-------
00
2
4
untilFailure
Quarters
6
8
FIGURElB. NON-PERFORMING
LOANSTO ASSETSOF TEXASBANKS (1992)
flating by the national consumer price index. I
also use the FDIC's Historical Statistics on
Banking (HSOB) Web site in order to identify
the failure of, and assistance to, commercial
banks and thrifts for 1969-2002. While the
FDIC reportidentifies only the city and state of
the failing bank,I matchthis to county using the
FDIC's Summaryof Deposits (SUMD) in order
to establish a mappingbetween city and county.
The size of each transactionis measuredusing
deposits from the HSOB report relative to
county income, where the average failure involves deposits approximatelyequalto about 10
percentof county income. In the end, the dataset
is a balanced panel of 3,070 counties over 32
years correspondingto 98,368 observationsinvolving 1,471 county-yearsof bank failure and
1,230 county-yearsof thrift failure.
A first pass at measuring the effect of a
bank failure at time t on current and future
real economic activity is implemented using
an OLS regression of county income k years
after failure-denoted
by ln(yc,t + )-on
three lags of county income before
failure and
the ratio of failed deposits to county income
in the year of failure.
Oc,,
(1)
ln(y)c,t+k
3
=
3jln(yc,-j) +
8kOc,
k E {0, 1...
5, 6}.
j=1
The regression includes a full set of time effects, uses robust standarderrors, and clusters
standarderrors at the county level. This specification is chosen for its simplicity and transparency, but the main results below are robust
to other models.
If failures were randomlyassigned each year
within the populationof banks,a naturalspecification would simply be to regresslog income in
the currentor futureyears on a dummyvariable
for failurein the currentyear. The coefficienton
failure would measurethe differencein log income betweencountiesin which thereis a failure
and those counties in which there is not, and
correspondto the effect of failureon log income.
1716
DECEMBER2005
THEAMERICANECONOMICREVIEW
e--- UnhealthyFailures1988
a- - Non-failures
1988
,
HealthyFailures1988
0.08 -
0.06-
0.04-
0.02-
00
2
4
QuartersuntilFailure
6
8
FIGUREIC. PRIMARYCAPITALTO ASSETSOF TEXASBANKS (1988)
Of course failures do not occur in this fashion. Alternatively consider an environment
where the probabilitythat a county has a bank
failure depends on business conditions, measuredby the level and trendof log income. The
right specification now is to regress currentor
future log income on a dummy variable for
failure in the currentyear plus lags of log income, where the lags control for the differential
probability of treatment. The only difference
between this frameworkand equation (1) is the
accountingfor variabletreatmentintensity, captured by using the ratio of deposits to income
instead of a simple dummy for failure. The
assumptioninherentin this specificationis simply that after controlling for past business conditions, the probabilityof failure is unrelatedto
the counterfactualpath of income if the failure
had not occurred.
The first row of panel A in Table 1 reports
OLS estimates of Sk for commercial banks. In
order to interpret these coefficients, consider
a failure involving a ratio of deposits to income of 15 percent. Controlling for the effect
of pre-existing local area economic conditions, this failure is followed by a decline in
real county income equal to 1.12 percent in
the year of failure. Three years after failure,
real income has fallen by about 2.66 percent,
and shows no signs of recovery even six years
after failure. The message from the table is
clear: the average bank failure is followed by
a large and persistent decline in real county
income.
One might want to interpretthese estimates
as the effect of bank failure on real activity,
controlling for the effect of pre-existing economic conditions. However, bank failure does
not occur randomly conditional on the level
and trend of real county income. In particular,
if the reason why a bank fails in one county
and not another has anything to do with how
county income would evolve if the bank did
not actually fail, OLS estimates will not accurately measure the effect of bank failure on
the local economy. Since the risky part of a
bank's asset portfolio is just made up of loans
to local area firms, it seems especially hard to
VOL.95 NO. 5
a
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
1717
UnhealthyFailures1988
Non-failures1988
HealthyFailures1988
-.---
0.08-
0.06
0.04-
0.02-
00
2
4
QuartersuntilFailure
6
8
FIGURE
LOANSTOASSETSOFTEXASBANKS(1988)
1D. NoN-PERFORMING
argue that bank failure is in any sense exogenous to the counterfactual path of income.
While it might be hard to disentangle the
effect of bank failure from pre-existing weakness in local area business conditions, it might
be easier to identify differences in the effect of
bank failure on county income across some
measureof "specialness."I explore three differences below: bank versus thrift failures, small
versus large failures, and assisted mergers versus closures.
Given the minimal presence of commercial
lending from thriftloan portfolios,bank lending
is arguably more information-intensive than
thrift lending. This view is reinforced by the
second set of coefficients in panel A, which
measures the decline in economic activity following thrift failure. While thrift failures are
also followed by permanentdeclines in county
income, the magnitudes are significantly
smaller. After six years the decline in activity
following thrift failure is less than 5 percent
(= -0.13/-2.83) of the decline following bank
failure of equivalent size.
If small firms are more bank-dependentthan
other firms,the failureof small banks should be
followed by a greaterdecline in outputper dollar
of deposits given the greaterpresence of small
business lending in small banks.This hypothesis
is validatedby estimatesof Sk brokenout across
failure size of panel B, where small failuresinvolve a ratioof depositsto incomebelow the nine< 0.2275). Small-bank
tieth percentile
(Oc,,
failures have a much
larger effect on real economic activity per dollar of deposits than largebank failures. The coefficients imply that for a
ratioof 15 perfailureinvolvinga deposit-to-income
in
the
declines
income
yearof failure
cent, county
coefficients
the
small-bank
1.83
using
percent
by
and only 0.36 percentusing the large-bankcoefficients.After six years, the small failurehas reducedrealincomeby 5.38 percent,while the large
failurehas reducedreal income by 1.16 percent.7
71 emphasize that these coefficients do not imply that
small-bank failures have a larger effect on county income
than large-bank failures. The key point is that while the
1718
DECEMBER2005
THEAMERICANECONOMICREVIEW
TABLE 1--OLS ESTIMATES
OF THEEFFECTOF FAILURE ON REALCOUNTYINCOME
Lead k of real county income
k=0
k= 1
k=2
k=3
k=4
k=5
k=6
-0.1030***
(0.0175)
-1.55%
-0.1569***
(0.0186)
-2.35%
-0.1774***
(0.0228)
-2.66%
-0.1741***
(0.0244)
-2.61%
-0.1941***
(0.0267)
-2.91%
-0.1885***
(0.0305)
-2.83%
-0.0099**
(0.0040)
-0.15%
-0.0074*
(0.0044)
-0.11%
-0.0073**
(0.0034)
-0.11%
-0.0097**
(0.0053)
-0.15%
-0.0081
(0.0050)
-0.12%
-0.0084
(0.0061)
-0.13%
Panel A. Ratio of failed deposits to income
8••
Evaluated at
Oc,
-0.0749***
(0.0120)
= 0.15 -1.12%
cthrift
-0.0060***
(0.0020)
Evaluatedat Oc,t= 0.15 -0.09%
Panel B. Ratio of failed deposits to income broken out across size and resolution type
-0.2085***
(0.0414)
-3.13%
-0.2550***
(0.0514)
-3.83%
-0.2310***
(0.0617)
-3.47%
-0.2843***
(0.0690)
-4.26%
-0.3519***
(0.0798)
-5.28%
-0.3585***
(0.0894)
-5.38%
8large bank
-0.0238
(0.0289)
Evaluated at 0,, = 0.15 -0.36%
-0.0251
(0.0336)
-0.38%
-0.0563
(0.0418)
-0.84%
-0.0607
(0.0492)
-0.91%
-0.0694
(0.0536)
-1.04%
-0.0860
(0.0624)
-1.29%
-0.0770
(0.0694)
-1.16%
8tyPe II bank
-0.0340
(0.0307)
Evaluatedat 0,,, = 0.15 -0.51%
-0.0433
(0.0360)
-0.65%
-0.0611
(0.0450)
-0.92%
-0.0778
(0.0540)
-1.17%
-0.0525
(0.0583)
-0.79%
-0.0541
(0.0689)
-0.81%
-0.0522
(0.0769)
-0.78%
-0.0839
(0.0580)
Evaluatedat 0c,, = 0.15 -1.26%
Observations
88,798
-0.1578**
(0.0745)
-2.37%
-0.1583*
(0.0882)
-2.37%
-0.2553**
(0.1066)
-3.83%
-0.2750**
(0.1237)
-4.13%
-0.1856
(0.1210)
-2.78%
-0.2124
(0.1401)
-3.19%
85,724
82,650
79,576
76,502
73,428
70,356
mall
bank
Evaluatedat
8type
II bank
Oc,
-0.1218***
(0.0327)
= 0.15 -1.83%
Notes: The table reportscoefficients and standarderrorsfrom OLS estimates of 8k from equation (1) in the text:
ln(yc,t+k)
jt=1 3jln(yc,_j) + 6kc,t, where c, is equal to is the ratio of failed-bankdeposits to county income. The data are a balanced
panel of U.S. counties 1969-2000. In panel B, small failures are defined using the ninetiethpercentileof the ratio of deposits
to income. Type II failures refer to assisted mergers, while Type HI failures refer to closures. Standarderrors have been
correctedfor heteroskedasticityand clustered at the county level. Coefficients accented by one, two, and three asterisks are
statistically significant at the 10-, 5-, and 1-percentlevels, respectively.
The decline in activity following failure
should also depend on the mannerin which the
institution is resolved. When an institution is
closed (a Type III failure), assets are liquidated
and relationships between customers and the
bank are permanentlydestroyed. On the other
hand,when an institutionis resolved by assisted
merger (a Type II failure), there is scope for
informationto be transferredto the buyer. Finally, when an institution receives open-bank
assistance (a Type I failure), no informationis
destroyed. The coefficients in panel B imply
that for an institutionwith a ratio of deposits to
average large-bankfailure is more thanten times largerthan
the averagesmall-bankfailure,it has less thantwice the real
effect on county income.
income of 15 percent, a Type III resolution is
associated with a decline in output three years
afterfailureof 3.83 percentagepoints more than
a the decline following a Type I resolution. On
the otherhand,a Type II resolutionis associated
with a decline after three years of only 1.17
percent more than a Type I resolution.
While all of this evidence is consistent with
banks being special, it is not possible to dismiss
some reasonable concerns. In particular, the
greaterconcentrationof businesslendingin banks
versusthriftsalso impliesthatthe creditqualityof
a bank loan portfolio is more sensitive to local
business conditions.It follows that bank failures
are a better signal of an unobservednegative
shockto businessconditionsthanthriftfailures.At
the same time, since small banks largely lend to
local area businesses, one might expect small-
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
bank performanceto be more closely tied to the
local economythanthatof largebanks.It follows
that small-bankfailures are a better signal of a
negative shock to the local economy than largebank failures. The differences across resolution
type are threatenedby the fact that the insurer
typicallygets to choose how a bankis resolved.In
particular,the FDIC is mandatedto choose the
resolutionmethodthat minimizes the cost to the
taxpayer.As this cost likely depends in part on
expectedlocal areaeconomic conditions,it is not
clear thatthe observedlargereffect of a Type III
resolutionreflectsthe destructionof relationships
or how the insurerchooses resolutiontype. Given
the challengesin makinga convincingconnection
between unhealthyfailure and real activity,it is
necessary to turn the analysis to the failure of
healthybanks.
II. The Failureand Resolutionof Healthy
Banks
Why would the FDIC let healthy banks fail?
Why would the failure of healthy banks have
any effect on real economic activity? The following two sections provide detailed explanations to each of these questions.
A. The Failure and Resolution of First City
Bancorporation
The failure of 20 subsidiaries of First City
Bancorporationin 1992 standsout in the history
of bank failures, not only because it involved
$7.5 billion in deposits, but also because the
transactioninvolved no cost to the FDIC. First
City Bancorporation failed on October 30,
1992, when regulatorsdeclaredits lead banks in
Dallas and Houston to be insolvent. According
to the third-quarterCall Report, these two lead
banks, with about $4 billion in assets, had
posted a net loss of $86 million over the first
nine months of the year and $450 million over
the previous 45 months. One monthbefore failure, the Dallas and Houston subsidiaries declared only $31 million in equity capital and
$111 million in loan loss allowances against
more than $300 million in problem loans.8
8 The descriptionhere of the First City failure and resolution drawson a case study appearingin FDIC (1998), pp.
567-94.
1719
Later on the same day that the lead banks
were closed, the FDIC exercised the "crossguarantee" provision of FIRREA, permitting
the insurerto charge off to the capitalof solvent
subsidiarybanks any expected losses related to
the failure of the lead banks. As the FDIC's
original estimate of loss was $500 million and
the remaining subsidiarieshad only $276 million in primary capital as of the third-quarter
Call Report, the regulators also closed down
these other banks.
In resolving the subsidiaries of First City
Bancorporation,the FDIC established 20 separate bridge banksto assume the deposits of each
failed subsidiarybank.9 At the time of failure,
the FDIC expected losses from four of the subsidiary banks:the two lead banks in Dallas and
Houston andtwo subsidiarybanksin Austin and
San Antonio which failed to meet minimum
regulatorycapital adequacyguidelines.10In the
16 other subsidiaries, the FDIC transferredall
deposits, including $140 million in uninsured
deposits, to new bridge banks. In the four banks
where losses were expected, all insureddeposits
were transferredto bridge banks, and the FDIC
paid an 80-percent advance dividend on $260
million in uninsureddeposits.
On January27, 1993, the FDIC announcedthe
sale of the bridgebanks with about $9 billion in
assets to 12 financialinstitutionsat an aggregate
premium of $434 million. In 17 of the failed
banks,the acquiringinstitutionsagreedto absorb
all losses, while the acquirersof the Dallas,Houston, and Austin banks enteredinto loss-sharing
9 The FDIC is authorizedby the Competitive Equality
Banking Act (CEBA) of 1987 to create bridge banks in the
resolution of failed banks. A bridge bank is a nationalbank
charteredby the Office of the Comptrollerof the Currency
(OCC) that is operatedby the FDIC for a period of less than
two years. The creation of a bridge bank gives the insurer
time to stabilize the failed bank's situation,effectively market the franchise to potential acquirers, and perform due
diligence on the asset portfolio. The FDIC typically uses its
bridge bank authorityin the resolution of large, complex
bankingorganizations,replacing the prioruse of open bank
assistance. Between 1987 and 1994, the FDIC used its
authority ten times to resolve 114 failed banks into 32
bridge banks with total assets of about $90 billion. Most of
these failures occurred in the Northeast or the Southwest
and involved institutionswith assets of at least $1 billion.
10 In the
analysis below, note that the non-lead subsidiaries in San Antonio and Austin are not considered to be
healthy banks, given their capital ratios were below the
regulatoryminimum.
1720
THEAMERICANECONOMICREVIEW
agreementsof $2.5 billion with the FDIC. After
bidding,the FDIC announcedanother10 cents for
every dollarof uninsuredclaims for depositorsin
Dallas and for uninsureddepositorsand otherunsecuredcreditorsof the Austin and San Antonio
banks." On March30, the FDIC announcedthat
all creditorswith valid claims againstthe receivershipwould receive the full principalamountof
theirclaims and forecasta surplusof $60 million
to be returnedto the shareholdersof the bank
holdingcompany.As these shareholders
projected
a receivershipsurplusof more than$500 million,
they sued the FDIC for the improperclosure of
Dallas and Houston banks, as well as improper
use of cross-guaranteesto the other subsidiary
banks.The FDIC settled the lawsuit, seeking $1
billionin compensatorydamagesand$2 billionin
punitivedamages,for about$350 million in January 1994, and the agreementwas approvedby
the bankruptcycourtin May 1995.
Since the resolution of these healthy banks
involved no loss of wealth or liquidity to
either uninsured depositors or other creditors,
the only mechanism through which these failures could have plausibly affected real economic activity is the destruction of credit
relationships with local borrowers. It turns
out, however, that documenting any direct
evidence of a reduction in lending associated
with healthy-bank failure can be challenging.
One important issue is that data from regulatory reports measure only the stock of loans at
a point in time instead of lending over a
period of time. This creates a problem because the bias involved in using the change in
loans as a proxy for lending becomes larger
during a bank failure, likely overstating the
real contraction in credit. In particular, the
change in loans incorrectly counts as a contraction in lending any transfer of problem
assets from the failed bank's loan account to
other asset accounts (i.e., other real estate
owned or other assets) or to the insurer's own
balance sheet. Another problem is that the
assets of a failed bank are often liquidated (as
in a Type III resolution) or sold to thrifts,
which makes it difficult, if not impossible, to
track lending following failure.
" The Dallas bank was chartered the
state, and Texas
by
had a depositorpreferencelaw requiringthat all depositors
be paid before other creditors.
DECEMBER2005
I have two responses to this problem. First,
while it seems reasonableto be concernedabout
potentially serious problems when making real
inferences about lending from the regulatory
reportsof failed banks and their acquirers,it is
likely that these problems are minimized when
dealing with banks that were fundamentally
healthy at the time of failure.12 Second, this
problem can be overcome when looking at the
behaviorof off-balance-sheetmeasuresof lending, which do not suffer from resolution cleaning-up issues. In particular,while it is certainly
true that a decline in unused loan commitments
could reflect either a decrease in loan supply or
an increase in demandfor funds, the differential
movement in unused loan commitments across
failed andpeer banks isolates differentialmovements in supply, as the peer banks control for
local area demand.13
The first column of Table 2 documents the
stock of loans for the pro forma institutions
that acquired the healthy First City subsidiaries each June for two years before failure and
two years following failure.14 Panel A illustrates that commercial and industrial loans
dropped by more than 35 percent in the year
following failure and remained depressed two
years after failure. While panel B documents
a much smaller and more transitory decline in
loans secured by real estate, panel C documents a larger decline in unused loan commitments of more than 40 percent. In order to
interpret this data properly, in the second
column I investigate what happened to peer
12
Not only were these banks sufficiently healthy to
protectthe FDIC from any losses createdby theirresolution,
but they also had balance sheets with enough marketvalue
to protectthe insurerfrom any losses in the resolutionof the
lead and problem banks.
13 The change in unused commitmentsreflects the FDIC
exercising its authorityto cancel either commitments, the
expiration of these commitments without renewal, or a
takedown of credit lines by borrowers.While the first two
phenomenaclearly reflect a contractionin credit, the latter
explanationcould reflect either an attemptby firms to mitigate the contractionin on-balance-sheetcredit or a necessary response to local area business conditions.
14 The first column refers only to 10 of the 11 acquirers
of healthy banks that filed regulatoryreportsin June 1994.
The excluded acquirerwas very large relative to the target,
with substantialassets in other counties, making pro forma
analysis inappropriate.The other five failed healthy banks
were acquired by institutions not filing regulatoryreports
(i.e., thrifts) or by institutionsthat acquiredat least one of
the lead or problem banks.
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
1721
TABLE2-BANK FAILURE,LOANS,AND LOAN COMMITMENTS
First City Bancorporation(t = 1992)
Time
Healthy
banks
Peer
banks
First RepublicBankCorporation(t = 1988)
Combined
peer
All banks
Peer banks
Combined
banks
Combined
peer
110.02%
104.90%
1,701,784
84.18%
86.00%
114.29%
105.54%
1,401,017
102.03%
105.40%
158.84%
117.17%
8,270,092
84.26%
84.67%
135.86%
119.33%
13,151,809
100.55%
94.85%
144.73%
118.50%
21,421,901
94.26%
90.92%
124.03%
109.73%
3,201,696
90.42%
78.33%
99.30%
95.66%
2,837,250
102.49%
111.69%
100.75%
98.11%
3,094,413
101.57%
105.82%
135.65%
127.16%
8,772,169
37.99%
36.44%
109.36%
108.06%
14,492,417
95.34%
89.63%
119.27%
115.26%
23,264,586
73.72%
69.57%
93.31%
98.71%
5,280,353
97.73%
94.24%
88.20%
97.94%
923,326
113.30%
125.58%
138.68%
113.40%
8,476,549
82.13%
110.87%
130.92%
129.94%
10,267,390
111.33%
130.10%
134.43%
122.46%
18,743,939
98.12%
121.40%
90.95%
90.94%
780,850
107.10%
138.25%
98.98%
99.39%
126,501
117.86%
149.15%
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
N/A
Combined
banks
Panel A. Commercialand industrialloans
ttt
t+
t+
2
1
1
2
101.98%
113.79%
680,172
63.26%
62.20%
115.38%
98.99%
1,021,612
98.11%
101.84%
Panel B. Loans secured by real estate
ttt
t+
t+
2
1
1
2
110.82%
95.98%
805,876
86.71%
91.67%
94.73%
95.53%
2,031,374
108.75%
119.63%
Panel C. Unused total loan commitments
ttt
t+
t+
2
1
1
2
77.25%
120.19%
579,082
57.58%
64.87%
79.72%
92.42%
518,926
129.99%
163.12%
78.42%
107.06%
1,098,008
91.80%
111.31%
Panel D. Unused "retail"commitments
ttt
t+
t+
2
1
1
2
86.19%
84.70%
35,506
73.64%
230.51%
76.06%
81.76%
145,891
130.73%
174.48%
78.05%
82.33%
181,397
119.56%
185.45%
Panel E. Unused "commercial& industrial"loan commitments
ttt
t+
t+
2
1
1
2
76.67%
122.51%
543,576
56.53%
54.05%
81.15%
96.59%
373,035
129.70%
158.68%
78.49%
111.96%
916,611
86.31%
96.63%
86.49%
97.71%
796,825
112.58%
121.84%
Notes: The table displays June pro forma measures of lending. The first column refers to the pro forma institutions that
acquiredfailed healthy banks;the second column refers to banks that had all deposits in the same counties as the acquiring
banks in June 1994; the thirdcolumn refers to the combinationof the first two columns; and the fourthcolumn refers to the
pro forma banks that had no deposits in counties as institutionsacquiringthe failed First City banks. The last four columns
are constructedin a similar fashion, but refer to all failed subsidiariesof First RepublicBankCorporationsince they were
placed into a single bridge bank.
institutions, which refer to commercial banks
with deposits only in the same counties as the
acquirers of the healthy banks. The differential path of lending shown in each of the
panels by peer institutions suggests that the
decline in lending from the first column is not
driven by a decline in the demand for loans by
local borrowers. This point is particularly
powerful in interpretingthe decline in unused
commitments in panel C from the first column
as a contraction in the supply of credit. Panels
D and E break out the change in unused
commitments into "retail" and "commercial
and industrial"lines, where the latter involves
more information-sensitivelending.15Evidence
15 Unused "retail" loan commitments are the sum of
home equity lines, credit card lines, and commitments to
lend for securities underwritingor for the purchaseof real
1722
THEAMERICANECONOMICREVIEW
from the first two columns of panels D and E is
compelling, suggesting a transitorydecline in
the "retail"measureof lending, but a permanent
decline in the "commercialand industrial"measure of lending relative to peer institutions.
An obvious potential problem with the
acquirer-peer contrast from the first two columns is that while a differential movement in
loans could certainly reflect a contraction in
credit by the healthy failed banks, it does not
rule out that this contraction is mitigated
through a credit expansion by peer institutions. In order to solve this problem, in the
third column I construct, as local area aggregates, the sum of these two groups of institutions, and then in the fourth column compare
this to a local area aggregate of institutions
not located in counties where any First City
subsidiary failed. Together, these two columns suggest in panel A that aggregate commercial and industrial lending was 17 to 19
percent lower in the two years in counties
affected by healthy-bank failures relative to
aggregate lending in unaffected counties. In
contrast, while aggregate real estate lending
was about the same, unused loan commitments were 22 to 24 percent lower. Most
interesting, while the "retail" commitments
seem to bounce back strongly, the "commercial and industrial commitments" remain depressed, as if information were destroyed
during failure. The bottom line is that these
healthy-bank failures appearto have been followed by significant and persistent declines in
information-intensive lending, subject to all
the caveats mentioned above.
B. First RepublicBankCorporation
The failure of First RepublicBankCorporation in July 1988 was the largest ($33.4 billion
in assets) and costliest ($3.9 billion in outlay) in
FDIC history. First Republic was the largest
bankholding companyin Texas, with more than
160 offices throughoutthe state. Its banks had
major correspondent relationships with over
estate. The measurefor unused "commercialand industrial"
loan commitments is "all other commitments,"which includes commercial commitments to lend as well as some
other retail-typecommitments.
DECEMBER2005
1,000 banks, held 20 percent of all loans made
in Texas, and managed $50 billion in trust assets. The holding company was created as a
mergerbetween RepublicBankCorporationand
InterFirstCorporationin June 1987 as a means
to assist the latterholding company, which was
struggling at the time.16
It soon became clear, however, that the
former RepublicBank subsidiaries were not as
strong as previously thought. By the end of
1987, regulatorsforced the bank holding company to recognize troubled loans leading to a
loss of more than $650 million. As bad news
affected funding, the lead banks were forced to
raise funds from their other subsidiariesdue to
losses in demand deposits and correspondent
business. In the first quarterof 1988, the lead
banks lost $1.8 billion in deposits, creating a
liquidity crisis that forced the Dallas bank to
borrow $2.6 billion from The Federal Reserve
Bank of Dallas. Since the Dallas bank was on
the verge of failure, the FDIC approveda sixmonth, open-bank assistance agreement of $1
billion in Marchto the lead banks of the holding
company. The loan was subordinatedto depositors and was guaranteedby all other subsidiaries of the bank holding company and collateralizedby the equity stake the parentheld in
them. For the first time since the failure of
Continental Illinois, the FDIC assured all depositors and creditorsof the banks-and not the
bank holding company-that they would be
fully protectedagainst loss.
While the FDIC assistance plan slowed the
outflow of deposits from the lead banks, the
conditionof lead bankscontinuedto deteriorate,
as equity was negative $1 billion at the end of
the second quarter.On July 29, 1988, the FDIC
notified regulators that open-bank assistance
would not be renewed.The Office of the Comptrollerof the Currency(OCC) advised The Federal ReserveBank of Dallas thatthe Dallas bank
was no longer a viable entity, promptinga call
of its discount window loan. When the Dallas
subsidiarywas unable to repay, it was declared
insolvent and closed by the OCC, an event of
default underthe assistance agreementwith the
FDIC. The triggering of the event of default
16The descriptionhere of the First RepublicBankfailure
and resolution draws on a case study appearingin FDIC
(1998), pp. 595-616.
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
permittedthe FDIC to charge off losses to the
equity of the other subsidiaries, which, combined with losses on inter-bank funding, left
these banks insolvent.
On July 30, 1988, the FDIC placed all 40
subsidiary banks into the bridge bank NCNB
Texas. The insurer selected the out-of-state
bank holding company NCNB to acquire the
bridge bank, and contractedwith them to manage the bridge bank until the transactioncould
be finalized. NCNB purchased 100 percent of
voting stock for $210 million, while the FDIC
purchased 100 percent of nonvoting stock for
$840 million. In addition,the FDIC funded the
write-downof assets to marketvalue by assuming $1 billion of the bridge bank's debt to The
Federal Reserve Bank of Dallas and by forgiving debt to the bridge bank. In August 1989,
NCNB assumed control of the bridge bank by
acquiringthe rest of the FDIC's interest.17
Since depositors and uninsured creditors of
the healthy banks were protected against any
loss of real wealth or liquidity, the only mechanism throughwhich the failures could plausibly have affected real activity is through the
destructionof credit relationships.
In contrastto the failure of the healthy subsidiaries of First City, the healthy First RepublicBank subsidiarieswere lumped togetherwith
the problemsubsidiariesin a single bridgebank,
making it impossible to study bank lending to
borrowersof the healthy subsidiariesafter failure. Moreover, as noted above, the inclusion of
problembanks in the bridge bank creates a bias
towardoverstatingwhat happenedto lending as
the FDIC shifted problem loans to other accounts on the balance sheet or to its own balance sheet. With these warnings in mind, the
last four columns of Table 2 document what
happensto lending at the pro formabridge bank
in the two years before and two years following
failure.The firstcolumn documentsa 15-percent
decline in commercial and industriallending in
panel A, but this is a decline of only about 9
percent relative to peer institutions (shown in
17 In the analysis below, note that the non-lead subsidiaries in San Antonio and Austin are not included in the
sample of healthy banks.Duringthe InterFirstmergerin the
previous year, these two subsidiariesabsorbedthe San Antonio and Austin subsidiariesof the troubledbank holding
company.
1723
the second column).18 While there is also a
significant decline in real estate loans in panel
B, this is concentrated at the lead banks and
seems to be associated with resolutionclean-up
accounting(increasesin otherreal estate owned
(OREO) and other assets). Also note in panel C
the decline in unused commitmentsrelative to
peer institutions of 29 percent and 19 percent,
respectively, which is consistent with a contraction in credit as in the case of First City. The
final two columns are reported for completeness, but are less meaningful than the previous
case where they correspondedto a split between
counties affected by healthy-bank failure and
counties not affected by any First City subsidiary failure. At the end of the day, there is
evidence that the pro forma institution had a
lower level of commercial and industrial and
commitment lending following failure than its
peers.
III. Data and Analysis
The fundamentalunit of analysis is a county
in a given year, where I implicitly treat each of
the 255 Texas counties as a separate banking
market.I use the summaryof deposits (SUMD)
in orderto measure marketsize (total deposits)
and market concentration (deposit Herfindahl
index). The latteris constructedby aggregating
the underlying branch data to the bank-county
level and using the sum of squared market
shares for each bank within a county.
The HistoricalStatistics on Banking (HSOB)
is used in orderto produce a reportfor the state
of Texas regardingthe failure and assistance of
banks and thriftsin each year. In additionto the
date of each transaction,I collect information
on the failure type (liquidation or open-bank
assistance), charterclass (bank or thrift), location (name of city or town) of the head office,
and FDIC certificate number (for banks only).
In orderto identifythe bankingmarketsaffected
by each bank failure, I identify the location of
all bank branches by matching the certificate
numberto the SUMD.Figures2A and2B illustrate
18 A careful reader might note the significant drop in
commercial and industrial loans in the two years before
failure. This drop is concentratedin the non-lead subsidiaries, which sharply reduced lending in order to mitigate a
liquidity crisis with federal funds loans to the lead banks.
1724
DECEMBER2005
THEAMERICANECONOMICREVIEW
Subsidiariss
LeadBanks
Hanlty Arnks
PrabbnAaiks
2A. SUBSIDIARIES
OFFIRSTCITYBANCORPORATION
(JUNE1992)
FIGURE
Subsidior es
LondBarks
arir
Problem
Prabbnlarks
CORPORATION
OF FIRSTREPUBLICBANK
FIGURE2B. SUBSIDIARIES
(JUNE 1987)
thelocation of branchesof the lead and non-lead
banks of the First City and First RepublicBank
bank holding companies, respectively. In each
case, the lead banks had branchesconcentrated
in the Dallas and Houston metro areas, while
the non-lead banks had branches that were
VOL.95 NO. 5
1725
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
spread throughout other parts of the state. I
constructthe deposit marketshareof banks that
either fail or receive open-bank assistance in
each Texas county-year. Without a certificate
number,I match failing thriftsto counties using
the city or town of each institution, and constructthe ratio of deposits to income for failing
thrifts in each county-year.
I also use data from the FDIC's Call Reports of Income and Condition in order to
construct county-level bank measures that include: total assets, liquid assets, net federal
funds sold, loan performance,loan charge-offs,
and the return on assets. The bank data are
aggregated using the deposit market share in a
county in order to construct county-year data.
I construct a measure of the sensitivity of
county income to oil prices using the sum of
coefficients in a regression of county income
on current and lagged oil prices. Finally,
county income is taken from the Bureau of
Economic Analysis (BEA) 1969-2002 and is
combined with the national consumer price
index in order to construct a measure of real
county income.
Summary statistics are displayed in Table 3, brokenout across affected and unaffected
counties for each of the natural experiments
analyzed below. The affected counties appear
to be a little more urban than the unaffected
counties, as county income is much larger and
banking market concentration as measured by
the Herfindahl index (HHI) is much lower.
There is no obvious patternover two years for
bank failures or county income growth, but it
does seem to be the case that affected counties were more likely to have experienced
thrift failures.
If the location of each healthy-bankfailure
were randomlyassigned throughoutthe state of
Texas, the analysis would be straightforward.
Since therewould be no pre-existingdifferences
between the counties that were affected by the
FDIC-inducedfailure and those that were not, I
could simply focus on the behaviorof real variables following the failure. It follows that any
difference between these two groups is attributable to the effect of bank failure. Unfortunately,
the location of the healthy subsidiaries of our
two failing bank holding companies was not
randomlyassigned. Since the parentcompanies
chose to purchase banks in these counties and
not other counties, one might be concernedthat
there is something different between the counties where there are healthy failures and the
counties where there are not. If this is the case,
then the naive analysis described above is not
the right thing to do. For example, if the parent
purchasedbanks in marketswith fast growth or
little competition, we want to be sure that we
are using marketsthat had fast growth or little
competition in the control group of unaffected
counties.
The naturalsolution to this potentialproblem
is to control for county characteristicsin the
years before failure, and this is done througha
lagged dependent variable specification. For a
county (c) experiencing a healthy-bankfailure
at time (t), we can analyze the effect of failure
on real variables at time (t + k) using the
following model:
3
(2)
ln(yc,t+)=
a +
j ln(yc,t-j)
j=1
3
+
x
+
healthyoealthy
j=1
k
+ ct
+ unhealthyOunhealthy
Cc't
This is just a cross section of Texas counties.
The dependent variable is a measure of real
economy activity (y) in county (c) at time
(t + k). I control for pre-existing differences in
the level and trend of the dependent variable
before healthy failure using three lags of the
dependent variable (the 8j's). In addition, I
control for other differences in county banking
markets using three lags of county-level characteristics (yj' s) before the healthy failure.
These characteristicsinclude the log of county
deposits, the county deposit Herfindahlindex,
the deposit market share of failed banks, the
deposit market share of failed thrifts, depositweighted bank balance sheet variables, and, in
the 1992 cross sections, the average exam rating. Finally, the effect of bank failure is taken
from the coefficient on the ratio of bank deposits to income.
Table4 reportsthe effects of healthy-bankfailureon local areaeconomicactivity,reportingOLS
estimates and standarderrors,which have been
In orderto interpret
correctedfor heteroskedasticity.
1726
DECEMBER2005
THEAMERICANECONOMICREVIEW
STATISTICS
TABLE3-TEXAS COUNTY
SUMMARY
First Republic (1988)
healthy
6unhealthy
Ln(yc,- 1)
- 1)
Ln(depositsc,
unhalthy
0cnhealthy
Thrift failures (t - 1)
Thrift failures (t - 2)
Sensitivity to oil price
HHIc-
1
Liquid assets (t - 1)
Primarycapital ratio (t - 3)
Primarycapital ratio (t - 2)
Primarycapital ratio (t - 1)
Problem loans (t - 3)
Problem loans (t - 2)
Problem loans (t - 1)
Returnon assets (t - 3)
Returnon assets (t - 2)
Return on assets (t - 1)
First City (1992)
Unaffected
Affected
Unaffected
Affected
0.0000
(0.0000)
0.0328
(0.1014)
12.24
(1.27)
11.69
(1.33)
0.0121
(0.0552)
0.2166
(0.1523)
0.1088
(0.1448)
14.35
(1.04)
13.81
(1.05)
0.0161
(0.0405)
0.0000
(0.0000)
0.0053
(0.0370)
12.51
(1.44)
12.03
(1.44)
0.0057
(0.0477)
0.1834
(0.1841)
0.0000
(0.0000)
14.23
(1.04)
13.64
(1.04)
0.0000
(0.0000)
0.0181
0.0079
0.0178
0.0250
(0.0908)
0.0188
(0.1361)
0.0657
(0.3153)
0.0028
(0.0043)
4864
(2556)
0.5070
(0.1196)
0.1017
(0.0226)
0.1033
(0.0253)
0.1002
(0.0256)
0.0129
(0.0101)
0.0165
(0.0116)
0.0181
(0.0157)
0.0094
(0.0075)
0.0061
(0.0080)
0.0029
(0.0108)
(0.0191)
0.0571
(0.2355)
0.0857
(0.2840)
0.0017
(0.0014)
2152
(938)
0.4610
(0.1095)
0.0822
(0.0083)
0.0792
(0.0090)
0.0779
(0.0106)
0.0152
(0.0062)
0.0218
(0.0106)
0.0248
(0.0164)
0.0066
(0.0046)
-0.0004
(0.0073)
-0.0029
(0.0093)
(0.0584)
0.0359
(0.1864)
0.1570
(0.4909)
0.0026
(0.0042)
4550
(2461)
0.5899
(0.1062)
0.0987
(0.0269)
0.0959
(0.0268)
0.0970
(0.0274)
0.0160
(0.0143)
0.0136
(0.0121)
0.0114
(0.0103)
0.0042
(0.0108)
0.0060
(0.0068)
0.0076
(0.0064)
(0.0548)
0.1176
(0.3321)
0.1765
(0.3930)
0.0022
(0.0016)
2392
(1206)
0.5758
(0.0710)
0.0793
(0.0170)
0.0791
(0.0158)
0.0796
(0.0155)
0.0151
(0.0099)
0.0121
(0.0067)
0.0124
(0.0048)
0.0036
(0.0065)
0.0051
(0.0045)
0.0078
(0.0065)
213
35
223
17
Notes: The table reportsthe mean and standarddeviation for several county-level variables.
The first two columns refer to Texas counties in 1988, while the second two columns refer to
Texas counties in 1992. In each unaffectedcolumn, therewere no healthy-bankfailures,while
in each affected column there was at least one healthy-bankfailure.Note that Oc,,refers to the
ratio of healthy or unhealthyfailed-bankdeposits to income.
these coefficients, I consider a bank failure with
a ratio of deposits to county income of 15 percent, approximatelyequal to the median of affected counties. Panel A indicates that the
failure of 16 subsidiariesof First City Bancorporation led to a decline in real economic activity of 2.25 percent after three years, and that
after six years the effect remainedstrongat 2.31
percent.In contrast,unhealthy-Texas-bankfailures in 1992 were followed by a decline in real
economic activity of 5.91 percent after three
years and a decline of 8.08 percent after six
years. Note thatthe decline in activity following
unhealthy-bankfailures in Texas is largely con-
VOL.95 NO. 5
1727
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
TABLE4-THE
EFFECTOF HEALTHY-BANK
FAILUREON REAL ACTIVITY
Lead of real county income
k= 1
k=0
k=3
k=2
k=4
k=4
k=6
Panel A. First City Bancorporation(cross section of 240 Texas counties in 1992)
Dependent variable: ln(y+ k)
-0.0374
(0.0293)
Evaluatedat Oc,, = 0.15 -0.0056
8healthy
-0.0797*
(0.0407)
-0.0120
-0.1792*** -0.2716***
(0.0435)
(0.0744)
Evaluated at 0c,, = 0.15 -0.0269
-0.0407
8~nhealthy
-0.1315**
(0.0555)
-0.0197
-0.1497**
(0.0736)
-0.0225
-0.1378
(0.1015)
-0.0207
-0.1347
(0.1070)
-0.0202
-0.1537
(0.1149)
-0.0231
-0.2198**
(0.1039)
-0.0330
-0.3942***
(0.1239)
-0.0591
-0.6538***
(0.1564)
-0.0981
-0.5023***
(0.1318)
-0.0753
-0.5384***
(0.1604)
-0.0808
Dependent variable: Aln(y +k)
-0.0554*
(0.0298)
-0.0954
-0.0143
-0.0538*
(0.0316)
-0.1492
-0.0224
-0.0210
(0.0344)
-0.1702
-0.0255
-0.0012
(0.0402)
-0.1690
-0.0254
0.0122
(0.0266)
-0.1568
-0.0235
-0.0055
(0.0296)
-0.1623
-0.0243
-0.1787*** -0.0923*
(0.0435)
(0.0538)
-0.1787
-0.2710
-0.0268
-0.0407
0.0511
(0.0672)
-0.2199
-0.0330
-0.1753**
(0.0778)
-0.3952
-0.0593
-0.2600***
(0.0695)
-0.6552
-0.0983
0.1511**
(0.0598)
-0.5041
-0.0756
-0.0384
(0.0690)
-0.5425
-0.0814
-0.0400
(0.0295)
Sum of coefficients
-0.0400
Evaluated at 0,,, = 0.15 -0.0060
8kcathy
8unhealthy
Sum of coefficients
Evaluatedat 0 = 0.15
Panel B. First RepublicBankCorporation(cross section of 248 Texas counties in 1988)
Dependent variable: ln(yt+ k)
8heacthy
Evaluated at Q0,,= 0.15
S•nhealthy
Evaluatedat Oc,, = 0.15
0.0029
(0.0308)
0.0004
-0.0564
(0.0442)
-0.0085
-0.0766
(0.0515)
-0.0115
- 0.1299"
(0.0671)
-0.0195
-0.1581**
(0.0723)
-0.0237
-0.1912**
(0.0820)
-0.0287
-0.2216**
(0.0890)
-0.0332
0.0108
(0.0193)
0.0016
-0.0479
(0.0318)
-0.0072
-0.0907**
(0.0364)
-0.0136
-0.1016**
(0.0482)
-0.0152
-0.1254**
(0.0524)
-0.0188
-0.1566**
(0.0619)
-0.0235
-0.1696**
(0.0746)
-0.0254
Dependent variable: Aln(yt + k)
-0.0597*
(0.0361)
-0.0568
-0.0085
-0.0201
-0.0522
-0.0289
-0.0316
-0.0323
(0.0309)
0.0029
0.0004
(0.0297)
-0.0769
-0.0115
(0.0370)
-0.1291
-0.0194
(0.0247)
-0.158
-0.0237
(0.0230)
-0.1896
-0.0284
(0.0304)
-0.2219
-0.0333
0.0126
(0.0195)
0.0126
0.0019
-0.0577**
(0.0265)
-0.0451
-0.0068
-0.0478**
(0.0209)
-0.0929
-0.0139
-0.0048
(0.0218)
-0.0977
-0.0147
-0.0244
(0.0159)
-0.1221
-0.0183
-0.0281
(0.0224)
-0.1502
-0.0225
-0.0111
(0.0246)
-0.1613
-0.0242
0.0029
khealthy
Sum of coefficients
= 0.15
Evaluatedat
Oc,,
8unhealthy
Sum of coefficients
Evaluatedat 0,, = 0.15
Notes: The table reportsthe coefficient estimate and standarderroron
in the text:
3
=
Zc,t+k
and
unhealthy
from estimation of equation (2)
3
+
+
+
i=1
8ealthy
iZc.,tj
x
+
+
+healrhealth
ealthyalthy
healthy
nhealt
+
c,t
i=1
where healhy and Ounhealthy are the ratios of healthy and unhealthyfailed-bankdeposits to county income, respectively. In
column k, the dependentvariableZc,t k is either the level or change in the log of real county income k years after failure.
Standarderrors have been corrected for heteroskedasticity.Coefficients accented by one, two, and three asterisks are
statistically significant at the 10-, 5-, and 1-percentlevels, respectively.
1728
THEAMERICANECONOMICREVIEW
sistent with panel B of Table 1, where the effect
of healthy-bankfailure is about half of the decline in activity following unhealthysmall-bank
failure after six years.
To explore robustness to the specification
employed, I also estimate the model using the
change in log income instead of the log level of
income in the second partof panel A. In orderto
compare these estimates to those from the first
part of the panel, I constructthe sum of coefficients across leads of income and compute an
effect using a ratio of deposits to income of 15
percent. While the estimates are measuredless
precisely than those using the level of income,
the implied levels are quite similar.
Panel B indicates that the failure of healthy
First Republic subsidiaries led to a decline in
real county income after six years of approximately 3.32 percent, which is larger but not
statistically different from the decline of 2.54
failure.While it
percentfollowingunhealthy-bank
is comfortingthat the estimates of healthy-bank
failure are similar across the two different failures, one might wonder why the decline in real
activity following unhealthyTexas bank failure
was much smaller in 1988 than the unhealthy
Texas failures from 1992 in panel A or than all
unhealthyfailures in Table 1. A simple answer
is suggested by the fact that of the 139 unhealthy banks that failed in Texas in 1988, 62
received a total of $1 billion in open-bank assistance from the FDIC. At the same time, of the
81 thrifts that failed in Texas in 1988, 74 received a total of $26 billion in assistance from
the SavingsAssociationInsuranceFund (SAIF).19
Given the differences in the decline in real
activity across resolutiontype reportedin panel
B of Table 1, it is not surprisingthat the decline
in county income following unhealthy-bank
failure is less severe in 1988 thanin otheryears.
While the effects of healthy-bank failure
seem large, they representonly abouthalf of the
decline in activity following unhealthy smallbank failure (panel B of Table 1). The measured
effect of the averagehealthy-bankfailureis also
much smaller than estimates of the effect of
bank branchderegulation,which Jith Jayaratne
and Philip E. Strahan(1996) conclude increased
19
In contrast, of the 11 unhealthy banks that failed in
Texas in 1992, only two received a total of $250,000 in
open-bankassistance from the FDIC. Moreover,there were
no failures of, let alone assistance to, Texas thriftsin 1992.
DECEMBER2005
per capita state income growth by as much as
one percentagepoint. Moreover, several recent
cross-country studies (for example, see Giovanni Dell'Ariccia et al., 2004, and John Boyd
et al., 2004) document that modern banking
crises are frequentlyassociated with large permanent losses of output.
IV. Conclusions
This paper has developed evidence that
healthy-bankfailures have significant and apparently permanent effects on real economic
activity. While there are importantcaveats to
keep in mind concerning the interpretationof
pro forma failed bank balance sheets, much of
this effect can be explainedby a severe contraction of bank lending.
One might object that the lessons to be
learned from the failure of healthy banks are
very narrow.While this might be a clever way
of disentanglingthe effect of pre-existing economic conditionsfrom the effect of bankfailure
on real economic activity, it might have little to
say about the effect of more typical bank failures. In particular,since banks often fail because of poor underwriting standards, the
contraction in credit following a traditional
bank failure is likely to be much more severe,
since other banks in the market are likely unwilling to extend credit on the same terms. In
addition, it is possible that liquidatingbank assets has a larger effect when economic activity
is depressed, and since bank failures typically
reflect weakness in the local economy, healthybank failures likely understatethe effect of this
liquidationon real activity.
On the other hand, while one might be limited in making inferences about the effect of
traditionalbank failure on real economic activity, the failure of a healthy bank might be considered the ideal experiment in which to study
the questionof whetheror not banks are special.
In each of these healthy failures, there is a
severe contractionin bank credit that is unrelated to local area economic activity, and the
question is whether or not loan customers are
able to replacethis crediton equal termsat other
banks. This seems like the perfect framework
for evaluating the real macroeconomic importance of bank-specific relationships, and the
costs of their destruction.
VOL.95 NO. 5
ASHCRAFT:ARE BANKSREALLYSPECIAL?NEW EVIDENCEFROM FAILURES
The micro literaturesurveyedby Christopher
James and David Smith (2000) generally supports the hypothesis that bank loans are special,
but the macro literaturehas been less conclusive. Joe Peek and Eric S. Rosengren (2000)
find evidence that changes in U.S. real estate
lending by Japanese banks related to the collapse of the Nikkei caused a change in U.S. real
estate output.On the otherhand,Driscoll (2004)
fails to find any evidence that shifts in state
bank loan supply created by money demand
shocks have any effect on state income. Finally,
Ashcraft (forthcoming)concludes that the frictions associated with bank loans are not large
enough for the lending channel to be a significant part of the transmission mechanism of
monetary policy, largely because real state income is so insensitive to state loan supply.
Why does bank lending matter in some
places, e.g., Peek and Rosengren (2000) and in
this paper, while not in others? If Japanese
banks were making loans on the margin that
were not being made by U.S. banks, it is not
surprising that these changes in loan supply
have importantreal effects. On the other hand,
the frictions relatedto the lending channel generate loan supply shocks that are fairly small.
Ashcraft (forthcoming) documents that differences in the response of bank lending to a fairly
significanttighteningin monetarypolicy, a 100basis-pointincreasein the federalfundsrate, are
no more than one percentagepoint across affiliation with a multi-bank holding company. It
follows thatone reasonableway to reconcile the
results above with the existing literatureon the
macroeconomic importanceof bank lending is
that firms are able to use non-bank sources of
credit or draw down on liquid assets in orderto
shield investment from small-bankloan supply
shocks, but not large ones.
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