Full text of Economic Review (Federal Reserve Bank of Cleveland) : 1989 Quarter 4

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Vol. 25, No. 4

ECONOMIC REVIEW
19 8 9 Quarter 4

Deposit-lnstitution
Failures: A Review

2

of Empirical Literature
b y A s li D e m irg u c-K u n t

Settlement Delays
and Stock Prices

19

b y R am on P. D eG ennaro

The Effect of Bank
Structure and Profitability
on Firm Openings
b y Paul W . B auer
and Brian A . C rom w e ll

FEDERAL RESERVE BANK
OF CLEVELAND

29

E C O N O M I C

R E V I E W

1989 Quarter 4
Vol. 25 , No. 4

2

Deposit-lnstitution
Failures: A Review
of Empirical Literature

Economic Review

b y A s li D e m irg u c-K u n t

q ua rterly b y the Research

is published

D e p a rtm e n t of the Federal
R e s e rv e B a n k o f C le ve la n d .
Turbulence in the U .S . banking and financial system in the 1980s has led to
a major government bailout and impending reform of the financial industry.
Current literature on the failure of deposit institutions does not seem ade­
quate to engender complete understanding of the problem. This paper

C opie s o f th e

Review

are

availa ble through our Public
A ffa irs an d B an k R elations
D e p a rtm e n t, 2 1 6 / 5 7 9 -2 1 5 7 .

reviews previous studies, giving particular emphasis to the various definitions
of insolvent and failed institutions. The paper concludes with recommenda­
tions to include the regulatory decision-making process into future research.

C oordin ating Ec o n o m is t:
Randall W . Eb erts

19

Settlement Delays
and Stock Prices

Ed ito rs : Paul J . N ick e ls
Robin Ratliff
D e s ig n : M ich ae l G alk a

b y R am on P. D eG ennaro

T y p o g ra p h y : L iz H a n n a

In stock trades made for regular delivery, the buyer need not make payment

Economic

until the securities are delivered, typically for five business days. No tests

O pin ion s s ta te d in

have demonstrated whether investors consider the length of this delay and

Review

the opportunity cost associated with it. The author studies this issue by

au th ors an d n ot necessarily

modeling stock prices as a function of the federal funds rate during the set­

those of the Federal R e s e rve

are th o s e o f the

tlement delay and by conducting regression tests to determine if this variable

B an k of C le ve la n d or o f the

helps to explain the observed return. He concludes that investors do incorpo­

Board o f G ove rn o rs o f the F e d ­

rate the effects of the settlement delay into the stock price.

eral R e s e rve S y s te m .

The Effect of Bank

........

29

Structure and Profitability
on Firm Openings

M aterial m a y be reprinted prov id e d th a t the s ource is credited.
P iea se send copies of reprjn ted
m aterial to the editor.

b y Paul W . Bauer
and Brian A . C rom w e ll
IS S N 0 0 13 -0 2 8 1
A n often-overlooked determinant of firm openings in empirical studies is the
price and availability of credit from commercial banks. This study finds that
profitable and competitive banking markets are associated with higher rates
of firm births in metropolitan areas. These results support the position that
bank structure and profitability influence economic development.

D eposit-lnstitution
Failures: A Review
of Em pirical Literature
by Asli DernirgilC-Kunt

Asli Demirguc-Kunt is an economist
at the World Bank in Washington,
D.C . This paper was written while
she was a visiting scholar at the
Federal Reserve Bank of Cleveland,
1988-1989.
The author would like to thank
Edward Kane, Huston McCulloch,
and Jam es Thomson for helpful
comments and discussion.

Introduction

The decade of the 1 9 8 0 s has been a particularly
turbulent one for the U.S. banking and financial
system. Since the establishment of the Federal
Deposit Insurance Corporation (FDIC) in 1933,
more than 1,500 banks have been declared offi­
cially insolvent and were subsequently closed,
acquired, or received assistance to prevent closure
(see table 1). More than 800 of these closures
took place during the 1 9 8 0 s, with 2 0 0 institu­
tions being closed in 1988 alone.
De facto failures, which are defined more
broadly to include any regulator-induced cessa­
tion of autonomous operations, portray an even
gloomier picture. This dramatic increase in the
bank failure rate has intensified public criticism
of deposit-institution regulators, since bank
safety and soundness is a major regulatory re­
sponsibility. 1 The recent crisis in the savings and
loan industry7 helped the already existing problem
to surface, and the public has become more eager
to assess and assign blame.

■

1

For a thorough discussion of safe and sound banking, see Benston et

al. (1986).

Deposit institutions fail primarily because they
take risks, and subsequent events do not always
turn out favorably. However, as Kane (1985)
notes, when a series of failures occurs, or a major
crisis is threatened, the general public blames
regulators as much as it blames deposit-institution
managers. Regulators are criticized for not being
able to detect and curb different forms of unsuc­
cessful risk-taking in time to prevent failures.
Potentially adverse consequences of bank fail­
ures include financial losses to bank stockholders
and creditors, disruptions of community banking
arrangements, contagious losses of confidence in
other institutions, and widespread financial dis­
tress caused by sharp contractions in the money
supply (Benston et al. [1986] and Kaufman
[1985]). However, the consequences of an indi­
vidual bank failure on the local economy are
unlikely to be any more severe than those of the
failure of any other firm of comparable size
(Horvitz [1965], Tussing [1967], Kaufman
[1985]). Even the commonly feared financial
distress thought to result from multiple bank
failures is unlikely to occur. Destruction of the
means of payment is an indication that govern­
ment has not fulfilled its macroeconomic respon­
sibility. Under such circumstances, sensible
monetary policy would call for an expansion of
the monetary7 base. It is an established view that
bank failures that produce a decline in the

T

A

B

L

E

U .S . Bank Closures For Various
Subperiods, 19 3 4 -19 8 8

Average Number of
Closings per Year

Average Deposits in
Closed Banks (Millions)

All
Banks

Insured
Banks

1934-40

64.2

51.1

1941-50

7.3

6 .1

1951-60

4.3

2 .8

11.5

10.5

1961-70

6.3

5.0

34.2

33.5

Years

All
Banks

Insured
Banks

6 8 .2

62.3
9.9

10.3

1971-80

8.3

7.9

537.2

529.1

1981-85

59.8

59.8

6,023.4

6,023.4

19 8 6

138

138

6,471.1

6,471.1

1987

184

184

6,281.5

6,281.5

1988

20 0

20 0

37,200

37,200

1989a

145

145

21,400

21,400

a. As of August 18, 1989.
SOURCE: 1987 FDIC Annual Report and telephone calls to FDIC.

T

A

B

L

E

2

Equity, Insolvency, and
Failure Detinitions

Federally Contributed Equity = the capitalized value of the
deposit-insurance
guarantees.
Enterprise-Contributed Equity = the capital of the institution
net of the federally con­
tributed equity,
Book-Value Insolvency

the book value of assets
minus the book value of
liabilities (book value of
the net worth) is negative,

Market-Value Insolvency
Economic Insolvency
De Facto Insolvency

market value of assets
minus market value of lia­
bilities net of the value of
insurance guarantees
( enterprise-contributed
equity) is negative,

Official (D ejure) Insolvency
Closure
Dejure Failure

capital is judged inade­
quate by the regulators and
the institution is closed or
merged out of existence,

De Facto Failure

any regulator-induced ces­
sation of autonomous
operations.

money supply are the result of errors and mis­
conceptions by central bankers (Thornton
[1939], Friedman and Schwartz [1963], Brunner
and Meltzer [1964], Cagan [1965]).
The consequences of contagious bank failures
are no longer considered serious concerns
because of the Federal Reserve System’s macroeconomic responsibilities. Yet the failure of indi­
vidual institutions still remains a serious prob­
lem for the general taxpayer. As Kane (1985,
1 9 8 9 ) notes, in a crisis, taxpayers are called upon
to underwrite the cost to the Treasury of bailing
out these institutions. The burden eventually
falls on them in the form of higher taxes or
higher rates of inflation.2 The problem for tax­
payers is to minimize their own loss exposure.
By developing an accurate model for predicting
bank failures, and by understanding the behavior
of bank regulators, it will be possible to identify
and/or verify the changes necessary to reform
the deposit insurance system, thus minimizing
the future loss exposure of the U.S. taxpayer. 3
The purpose of this article is to review empiri­
cal literature on deposit-institution failures. Sec­
tion I introduces and discusses concepts crucial
in the analysis. Section II compares and contrasts
selected empirical studies. Section III identifies
weaknesses in the various approaches to study­
ing the problem and concludes by suggesting
future avenues for research.

I. Bank Insolvency,
Closure, and Failures:
Explaining Regulatory
Decision-Making

The purpose of this section is twofold.4 First, it
seeks to define and distinguish between the dif­
ferent insolvency and failure categories listed in
table 2. Second, based on the distinction
between insolvency and failure, it describes how
failure should be modeled within the framework
of a regulatory decision-making process.

■ 2

This fact is exemplified by the recent savings and loan bailout.

■ 3

The problems in the present deposit-insurance system and regulator

behavior have been identified by Meltzer (1967), Scott and Mayer (19 71),
Merton (19 77, 1978), Kareken and Wallace (1978), Sharpe (1978), Buser,
Chen, and Kane (1981), Kane (1981a, 1981b, 198 5,198 6 , 1988, and 1989),
McCulloch (1981, 1987), Kareken (1983), Pyle (1983, 1984), and Benston et al.
(1986).

SOURCE: Author.
■ 4

The definitions and theoretical analysis presented in this section draw

largely on Benston et al. (1986) and Kane (1985, 1989).

Insolvency Versus Failure

Official insolvency occurs when an institution’s
chartering authority judges its capital to be
inadequate. The procedures by which this deci­
sion is made are not clear, however.
A firm’s capital may be identified as a particu­
lar measure of its net worth. Net worth is the dif­
ference between the value of the firm’s assets
and nonownership liabilities. In order to deter­
mine the level of capital, itemization of assets
and liabilities and adoption of an appropriate
valuation rule are necessary (Kane [1989]).
To be able to define capital, various categories
of assets and liabilities need to be itemized. A
complete definition requires recognition of
implicit assets and liabilities as well as explicit
ones. Implicit assets and liabilities are defined as
all sources of positive and negative future cash
flows that are considered “unbookable” by the
accounting profession.
Valuation of capital is crucial. Using different
valuation rules leads to different asset and liabil­
ity values. Measuring an institution’s capital on
the basis of historical cost at which it acquired its
various balance-sheet positions is misleading.
But historical-cost principles provide the basis
for determining the book values of the balance
sheet accounts of U.S. banks. Book values are
recorded in terms of acquisition costs. As market
prices change, these costs tend to depart from
market values.
Kane (1989) notes two shortcomings of
historical-cost accounting. First, using acquisition
cost undervalues an institution’s best portfolio
decisions and overvalues its worst ones. Second,
historical-cost accounting neglects potentially
observable changes in the value of a firm’s
investments by not modifying the acquisition
costs to reflect market developments. This
method exaggerates the economic relevance of
the acquisition costs of an institution’s assets and
liabilities and fails to appraise its investment suc­
cesses and failures on an ongoing basis.
To determine a depository institution’s level of
capital for regulatory purposes, it is helpful to
break down its capital into two components:
enterprise-contributed equity and federally con­
tributed equity (Kane [1989] )• Enterprisecontributed equity is the capital of the institution
net of the capitalized value of its deposit insur­
ance guarantees. To the extent that federal guar­
antees are underpriced, the deposit insurer con­
tributes de facto capital to the institutions. The
present deposit insurance system allows aggres­
sive deposit institutions to pass off poorly moni­
tored and unpriced risks onto federal insurance

agencies. 5 The federally contributed capital is
determined by the amount of risk that insurance
agencies stand ready to absorb.
These valuable guarantees are actually equity
instruments that make the U.S. government a de
facto investor in deposit institutions. Unless an
appropriate recapitalization rule is imposed on
managers and stockholders, the capitalized value
of the guarantees increases as the institution’s
enterprise-contributed equity decreases or as the
riskiness of either its portfolio or environment
increases. Clearly, the value of the federally con­
tributed capital should not be counted as a part
of the institution’s capital for regulatory purposes.
The traditional supervisory approach to regula­
tion also neglects the role of subordinated debt
as a potential source of market discipline, and
views debt capital as less desirable than equity.
However, permitting institutions to count subor­
dinated debt toward capital-adequacy determina­
tions would provide increased protection for the
insurance fund in the form of increased market
discipline (Benston et al. [1986]).
Holders of subordinated debt are a source of
market discipline because, as opposed to depos­
itor debtholders, they cannot withdraw their
funds on demand. Also, as opposed to stock­
holders, they do not share the increased profits
that increased risk-taking may bring. Therefore,
they prefer safe and conservatively managed insti­
tutions. If banks were required to maintain rela­
tively short-term subordinated debt as a certain
proportion of equity, thus forcing them into the
market on a frequent basis, subordinated debt
could protect the insurance agency from losses.
An appropriate insolvency criterion is the
market value of enterprise-contributed capital,
which can be obtained by subtracting the value
of federal guarantees from the institution’s
market value of equity. 6
De facto or market-value insolvency exists
when an institution can no longer meet its con­
tractual obligations out of its own resources. This
occurs whenever the market value of the institu­
tion’s nonownership liabilities exceeds the
market value of its assets; or, in other words,

■ 5

■

For a thorough review of this issue, see references in footnote 3.

6 An estimate of the capitalized value of the federal guarantees can be

obtained using different approaches. For a review of different techniques, see
Merton (19 77), Marcus and Shaked (1984), Ronn and Verma (1986), Kane and
Foster (1986), Benston et al. (1986), Schwartz and Van Order (1988), and
Demirguc-Kunt (1990, forthcoming).

when the market value of its enterprisecontributed equity becomes negative. However,
in determining official insolvency, regulators
tend to look for book-value insolvency rather
than market-value insolvency.
Book-value insolvency exists when the differ­
ence between the book values of an institution’s
assets and liabilities is negative. Even when an
institution is book-value solvent, its market-value
or economic insolvency may be suggested by
refinancing difficulties that surface as an ongoing
liquidity shortage. A liquidity shortage occurs
whenever an institution’s cash, reserve balances,
and established lines of credit prove insufficient
to accommodate an unanticipated imbalance in
the inflow and outflow of customer funds.
If a continuing liquidity shortage is not relieved
by outside borrowing or government assistance,
assets may have to be sold at “fire-sale prices,”
that is, for less than their equilibrium value. Such
sales erode the institution’s capital, and may cause
the uninsured customers of the institution to
move their funds to safer locations. The resulting
run on the institution's resources causes the insti­
tution to borrow nondeposit funds or to sell
earning assets. Given that these runs are typically
motivated by the presence of large unbooked
losses in an institution’s balance sheet, asset sales
push the book value of the institution’s assets
toward their market value, eventually resulting in
the institution’s book-value insolvency.
Official (de jure) insolvency, or closure (de
jure failure), occurs when the market-value
insolvency is officially recognized and the firm is
closed or involuntarily merged out of existence.
De facto failure can be defined more broadly
than closure as any regulator-induced cessation
of autonomous operations.
The definitions in this section clarify the dif­
ference between economic insolvency and fail­
ure of financial institutions. Economic insolvency
is a market-determined event. In contrast, de jure
or de facto failure results from a conscious deci­
sion by regulatory authorities to acknowledge
and to repair the weakened financial condition
of the institution. Failure is an administrative
option that the authorities may or may not
choose to exercise even when strong evidence
of market-value insolvency exists.

lyzes the working of government by applying
and extending economic theory to the realm of
political or governmental decision-making.7
Myers and Majluf (1984), Narayanan (1985), and
Campbell and Marino (1988) apply public choice
theory to explain the managerial decision­
making of an enterprise. Again, based on the
public choice theory, Kane (1988 and 1989)
develops a model of regulatory decision-making.
The Kane model incorporates the economic,
political, and bureaucratic constraints as well as
the career-oriented incentives of federal regula­
tors in explaining the regulatory decision-making
process. These constraints and incentives foster
the difference between market-value insolvency
and failure of financial institutions. Due to con­
flicts of interest between politicians and regula­
tors, and between regulators and taxpayers, timely
resolution of market-value insolvencies is often
not attractive to deposit-institution regulators.
Kane (1989) argues that this conflict of inter­
est between regulators and politicians compli­
cates the regulatory task of serving the taxpayer.
Deposit-institution regulators find it difficult to
resist budget constraints imposed by politicians
because they are subject to appointment and
oversight controls from politicians. As appointed
officials, they face political pressures to leave
problems unsolved, thus keeping involved con­
stituencies and political action committees will­
ing to pay tribute to politicians.
Regulators also face oversight controls from
their regulatory clientele, that is, from the institu­
tions in the industry they regulate (Stigler
[1977]). Federal officials have career-oriented
incentives to keep their constituencies and clien­
tele happy. Their explicit salaries are lower than
what they can make in the private sector. Econ­
omists conceive this gap as being bridged by
implicit wages. As Kane (1989) notes, these
implicit wages consist of certain nonpecuniary
benefits of holding a high government office and
of future increases in wages that accrue in post­
government employment—very often within the
regulated industry.
The actions and policy decisions of regulators
are closely overseen by their clientele. If regula­
tors can successfully complete their term in
government service, they can generally expect
higher wages in postgovernment employment.
The importance of the perceived quality of their

Failure as a
Regulatory Decision

Economic theory can explain why deferring
meaningful action can be the rational choice for
federal officials. The theory of public choice ana­

■ 7

See Buchanan (1960, 1967), Tulloch (1965), Niskanen (19 71), Stigler

(19 77), and Buchanan and Tollison (1984).

performance makes federal officials very sensi­
tive to the opinions of the institutions they regu­
late, as well as to those of the trade associations
connected with these institutions.
These career-oriented incentives introduce
political and bureaucratic constraints to regulatory
decision-making. Therefore, federal regulators
tend to be influenced by their constituencies,
avoiding solutions unfavorable to them, or
promoting solutions that they find particularly
desirable. Lobbying activities exaggerate and
make the negative early effects of public policies
more visible, further slowing the adoption of
substantial changes in financial regulation. For
regulators, the economic, political, and bureau­
cratic constraints increase the career costs of
serving the taxpayer well. This conflict of interest
between the regulators and the taxpayers leads
to the adoption of forbearance policies that
allow the continued operation of market-value
insolvent institutions.
In his model, Kane (1988 and 1989) envisions
two extreme types of regulators: the unconflicted
or faithful agent of the taxpayer, and the con­
flicted or self-interested agent.
A faithful agent is expected to work toward
fulfillment of society’s long-term goals. In the
Kane model, faithful agents are modeled as max­
imizing the unobservable market value of the
deposit-insurance enterprise. This value is calcu­
lated as the net present value of the future cash
flows generated by its operations. A faithful agent
protects the interests of the taxpayer, resisting
politically imposed restraints and careeroriented incentives.
Self-interested agents do not resist economic
constraints to avoid the possibility of conflict
with politicians. In addition, they are tempted by
career-oriented incentives and serve their own
narrow interests rather than those of the tax­
payer. In the Kane model, conflicted agents max­
imize their own perceived performance image in
an effort to maximize their postgovernment
wages. The self-interested agent’s decision­
making process is subject to economic constraints
implicit in the budget procedures, as well as to
the political and bureaucratic constraints implicit
in career-oriented incentives. The agent, in an
effort to serve himself well, gives in to all of
these constraints and incentives, and imposes
the resulting costs on the unwary taxpayer.
The Kane model is a theoretical model of reg­
ulatory decision-making that underlines the fac­
tors leading to the distinction between eco­
nomic insolvency and failure of financial
institutions. Clearly, in a realistic analysis, bank
failures need to be modeled within the frame­
work of a regulatory decision-making process.

II. Review Of Empirical
Literature On FinancialInstitution Failures
A summary of selected empirical studies on

thrift-institution and commercial-bank failures is
given in table 3. The first group of studies (Sin­
key [1975], Altman [1977], and Martin [1977])
focuses on developing early warning systems.
These systems statistically analyze financial ratios
constructed from the balance sheets and income
statements that institutions file regularly with
federal agencies. The goal is to incorporate this
information into monitoring systems and to help
regulators by flagging financially troubled institu­
tions as early as possible. To identify these insti­
tutions, researchers typically fit cross-sectional
models for each year into their sample periods.
The second group of studies (Avery and Han­
weck [1984], Barth et al. [1985], Benston [1985],
and Gajewski [1988]) attempts to explain statis­
tically de jure failures, labeled in this article as
the closure process. Their models seek to identify
financial factors that affect the likelihood of an
institution’s closure. Using cross-sectional data
over a given sample period or cross-sectional
data pooled from different years, researchers try
to pinpoint determinants of closure by analyzing
the same types of financial ratios used by the first
group of studies.
To clarify the model specifications of earlier
researchers, it is helpful to review briefly the
regulatory supervision process.

Bank Supervision
and Examination

Supervision refers to the oversight of banking
organizations and their activities to ensure that
they are operated in a safe and sound manner.
Examination is a means by which supervisors
obtain information on the financial condition of
an institution (Benston et al. [1986]). Examina­
tion is an important part of the supervisory proc­
ess. Through periodic examinations and contin­
uous supervision, regulators try to prevent
deposit institutions from taking excessive risks
that could lead them to economic insolvency.
The supervision and examination of depository
institutions are performed by one or more of the
following institutions: The Federal Reserve System,
state and federal chartering agencies, and federal
deposit-insurance agencies. The Office of the
Comptroller of Currency (OCC) and the Federal
Home Loan Bank Board (FHLBB, now the Office
of Thrift Supervision) charter national banks and
savings and loan institutions, respectively. State

D
T

A

B

L

E

3

A Summary of Selected Empirical
Studies on Deposit-institution Failures

Author

Estimation
Technique

Institutions and
Time Period

Dependent
Variable

Ratioa

Sinkey
(1975)

110 Problem
110 Nonproblem
Commercial
Banks
(1969-1972)

Discriminant
Analysis

Problem/
Nonproblem

Over 100 are tested,
1 0 are chosen,
6 are significant.

Altman
(1977)

56 Serious
Problem/49
Temporary
Problem/107
No Problem
Savings and
Loans
(1966-1973)

Discriminant
Analysis

Serious
Problem/
Temporary
Problem/
No Problem

32/7

Martin
(1977)

58 Closed/
5,642 Nonclosed
Commercial Banks
(1970-1976)

Logit

Closed/
Nonclosed

25/4

Avery and
Hanweck
(1984)

100 Closed/
1,190 Nonclosed
Commercial
Banks
(12/1978-6/1983)

Logit

Closed/
Nonclosed

9/7b

Barth
et al.
(1985)

318 Closed/
588 Nonclosed
Savings and
Loans
(12/1981-6/1984)

Logit

Closed/
Nonclosed

12/5

Benston
(1985)

178 Closed/
712 Nonclosed
Savings and
Loans
(1981-1985)

Logit

Closed/
Nonclosed

28/4

Gajewski
(1988)

134 Closed/
2,747 Nonclosed
Commercial
Banks
(1984-1986)

Two-Step
Logit

Closed/
Nonclosed

25/10

a. The ratio of the total number of independent variables screened to significant independent variables.
b. Two are significant but have unexpected signs.
NOTE: Significant independent variable definitions are given in table 4.
SOURCE: See text.

banking commissions charter institutions with
state charters. The deposit insurance agency for
banks is the Federal Deposit Insurance Corpora­
tion (FDIC) and for savings and loan institutions
it is the Federal Savings and Loan Insurance Cor­
poration (FSLIC), now changed to the Savings

Association Insurance Fund by the 1989 Finan­
cial Institutions Reform, Recovery, and Enforce­
ment (FIRRE) Act.
The 1989 FIRRE Act restructures the savings
and loan industry. Under the new law, what was
formerly the Federal Home Loan Bank Board is

divided into three parts: the Office of Thrift
Supervision (OTS), the Savings Association Insu­
rance Fund (SAIF), and the Federal Housing
Finance Board. The Office of Thrift Supervision
is responsible for the examination and supervi­
sion of savings and loans, and has the powers
formerly vested in the FHLBB. The Savings Asso­
ciation Insurance Fund takes the place of FSLIC.
In addition, a new Bank Insurance Fund is
created. Both the Savings Association Insurance
Fund and the Bank Insurance Fund are FDIC
agencies. The obligations issued by either fund
are backed by the full faith and credit of the
United States. A five-member Federal Housing
Finance Board is established to oversee credit
allocation by the 12 district Home Loan Banks to
members in the form of advances. The five mem­
bers are the secretary of the Department of Hous­
ing and Urban Development and four others
appointed by the president with the advice and
consent of the U.S. Senate. In addition, a new
agency, the Resolution Trust Corporation (RTC),
is created to oversee the liquidation of assets
from insolvent thrifts.8 The FDIC is the day-today manager of the RTC. The new law restruc­
tures the financial institution industry, dismantles
the independent Federal Home Loan Bank Sys­
tem, and gives the FDIC expanded powers.
Besides expanding the FDIC’s regulatory7 turf
and power, the new law does not substantially
alter commercial bank supervision. National
banks may be supervised by the Federal Reserve
Board, the OCC, and the FDIC. However, unless
the banks require assistance from the FDIC or
the Federal Reserve, only the OCC supervises
national banks. State-chartered banks are exam­
ined and supervised by the Federal Reserve if
they are members of the Federal Reserve System,
and by the FDIC if they are nonmembers. Statechartered banks can also be examined by their
state banking supervisors, with or without the
federal examiners.
The Federal Reserve is also responsible for
regulating, supervising, and inspecting bank
holding companies. Additionally, the states can
regulate and supervise holding companies. Fed­
erally chartered savings and loan institutions are
examined and supervised by the FHLBB (now by
the OTS). State-chartered savings and loan insti­
tutions are examined and supervised by their
state examiners and the FSLIC (now by the SAIF).

■

8

See Kane (1989) for an analysis of the savings and loan crisis.

Federal examining efforts for banks are coor­
dinated in such a way that an institution is visited
by only one examination team from either the
Federal Reserve, the OCC, or the FDIC. Federal
and state examiners also coordinate their exami­
nation schedules and make an effort to conduct
joint examinations. If the examinations are con­
ducted separately, federal and state examiners
share information by sending each other copies
of their examination reports.
Regulators use on-site and off-site methods in
order to obtain information about the economic
condition of the institutions.
Traditionally, regulators have focused their
monitoring efforts on sending teams of field
examiners to conduct on-site examinations of
each institution. On-site examinations are still
heavily relied upon in regulatory monitoring
efforts. States require exams every 12 to 18
months for their state-chartered institutions. In
theory, sound national banks with assets of $300
million and above are supposed to be examined
every 1 2 months; smaller banks are examined
every 18 months. However, in practice, these
schedules are often not met, and federal regula­
tors tend to concentrate on large institutions,
those showing problems on their call reports,
and those with low ratings on past examinations,
in deciding how to allocate the limited time of
their examiners.
Principles and standards for federal examina­
tions are coordinated by the Federal Financial
Institutions Examination Council (FFIEC). This
council was established by the Financial Institu­
tion and Interest Rate Control Act of 1978. It
coordinates the activities of five regulatory agen­
cies: the Federal Reserve, the OCC, the FDIC, the
FHLBB (OTS), and the National Credit Union
Administration, which charters and regulates
national credit unions. Efforts of the FFIEC are
directed toward making the field examinations
conducted by different agencies similar in scope.
Examiners focus mainly on the adequacy or
inadequacy of the firm’s capital account for
meeting the particular forms of risk exposure.
Traditionally, they have devoted their attention
to risks from nonperforming and questionable
loans and from problems rooted in incompetent
management (Kane [1985] and Benston et al.
[1986]). The documentation, collateral, and
payment records of most large loans and a sam­
ple of small loans are examined, and the loans are
classified into good, substandard, doubtful, and
loss categories. The institution’s internal control
system and managerial practices are reviewed
and evaluated. The examiners discuss their find­
ings with management and may recommend
changes in management practices to improve the

institution’s performance, and increases in capi­
tal to strengthen the institution’s balance sheet.
After the on-site examination, federal examin­
ers prepare a formal report pointing out strengths
and weaknesses in the firm’s operation. This
report is further summarized into a five-point
CAMEL rating. CAMEL is an acronym for five cate­
gories of condition and performance on which
the institutions are graded: capital adequacy, asset
quality, management, earnings, and liquidity.
Capital adequacy is a measure of an institu­
tion’s buffer against future unanticipated losses.
As explained in section I, in the case of financial
institutions, the market value of enterprisecontributed equity is the appropriate indicator of
capital adequacy. However, regulators tend to
focus on the book value of an institution’s equity.
As previously mentioned, in evaluating an insti­
tution’s asset portfolio, examiners focus on loan
quality. Examiners go through loan documenta­
tion and check the quality of collateral, if any,
backing each loan. Judgments are made as to the
quality of each borrower and his ability to repay
the loan. In addition, examiners check to see if
the institution has a high concentration of loans
to a specific industry' or to a single borrower.
The determination of an institution’s man­
agement quality is very subjective. Typically,
examiners decide on the competence of man­
agement based on the institution’s performance
in the other four categories.
Examiners rate the earnings of an institution
on both recent performance and on the histori­
cal stability of its earnings stream. Performance
and stability are determined by looking at the
institution’s profit composition. Examiners tty to
see if the profits come from a solid operating
base or are driven by one-time gains, such as
those generated by the sale of assets (Whalen
and Thomson [1988] ).
Liquidity of the institution is analyzed to deter­
mine its exposure to liquidity risk. To determine
the institution’s ability to meet unanticipated
deposit outflows, examiners look at the bank’s
funding sources as well as the liquidity of its
assets.
Since troubled institutions often try to hide
their problems from the public and the regula­
tors, it is difficult for examiners to detect prob­
lems by looking at the institution’s accounts and
financial statements. On-site examinations are the
most effective way of detecting fraud. As studies
by Sinkey (1975, 1979) indicate, quality of man­
agement and honesty of employees are the most
important factors leading to bank failures. How­
ever, examiners were not specifically asked to
examine for fraud until 1984. The U.S. House of
Representatives Subcommittee on Commerce,

Consumer, and Monetary Affairs of the Commit­
tee on Government Operations (1984) conducted
a study of 105 bank and savings and loan failures
between January7 1980 and June 1983 and found
that “...criminal activity by insiders was a major
contributing factor in roughly one-half of the
bank failures and one-quarter of the savings and
loan failures....” The committee subsequently
recommended that federal examiners be trained
and advised to specifically examine for fraud.
The component ratings of CAMEL categories are
subjectively weighed by the examiner to arrive at
an overall rating for the institution. A bank’s rating
depends on the examining regulatory agency and
the examination staff, since subjective judgments
are made in obtaining the CAMEL rating (Whalen
and Thomson [1988]). The CAMEL system
grades an institution on a five-point scale. Institu­
tions with ratings of 4 or 5 are considered “prob­
lem institutions.” The FDIC publishes a list of
problem banks, but the FSLIC does not publicize
its parallel list of problem savings and loan insti­
tutions. Problem institutions are examined more
frequently and monitored more closely.
The CAMEL rating is used by the federal exam­
iners. State examiners conduct similar examina­
tions, but they do not necessarily use the CAMEL
system. Federal and state examiners disclose
their overall rating to the institution’s board of
directors.
Regulators also use off-site monitoring to
complement on-site examinations. Off-site m oni­
toring focuses mainly on analyzing quarterly
income and balance sheet statements obtained
from Reports of Income and Condition (that is,
call reports) filed with the regulatory agencies.
Statistical early-warning models have been
available to supervisory agencies since the mid1970s. These models were developed to evaluate
the financial condition of institutions in order to
determine the priority or urgency for on-site
examinations. To a limited extent, off-site analy­
sis also looks at market data (such as growth
rates, deposit interest rates, and stock prices),
public disclosures, and credit ratings assigned by
private analysts.
Examiners seek to uncover regulatory viola­
tions and to identify problem institutions before
their condition deteriorates to the extent that the
deposit insurance fund is endangered. However,
in addition to their inadequate emphasis on fraud

□

Definition of Independent
Variables Found Significant in
Summarized Empirical Studies

Author
Sinkey
(1975)

Variable

LRTR
OETR
OEOI
LCR
SLRTR

Altman
(1977)

LA
NWTA
NOIGOI
RETA
ESTA
TLTS
HLBANW
SRETA

Martin
(1977)

GCARA
NITA
CI2LN

GCONI
Avery and
Hanweck
(1984)

LNTA
NLTA
KTA
CILNNL
NIT A
HERE
PTD

Definition
Loan Revenue/Total Revenue
Other Expenses/Total Revenue
Operating Expense/Operating
Income
Loans/( Capital + Reserves)
Revenue from State and Local
Obligations/Total Revenue
Loans/Assets
Net Worth/Total Assets
Net Operating Income/Gross
Operating Income
Real Estate Owned/Total Assets
Earned Surplus/Total Assets
Total Loans/Total Savings
FHLB Advances/Net Worth
Real Estate Owned (SI)/
Total Assets
Gross Capital/Adjusted Risk Assets
Net Income/(Total Assets-Cash
Items in Process)
(Commercial and Industrial
Loans + Loans to REITs and
Mortgage Bankers + Construction
Loans + Commercial Real Estate
Loans )/Total Assets
Gross Charge-offs/(Net Operating
Income + Loss Provision)
Natural Logarithm of Total Bank
Assets Less Loan Loss Reserves (TA)
Net Loans/Total Assets
( Equity Capital + Loan Loss
Reserve Allowances)/TA
Commercial and Industrial
Loans/Net Loans
Net After-Tax Income/TA
Herfindahl Index for Bank’s Local
Banking Market3

risk, examiners are typically slow in identifying
and evaluating new types of risks as they emerge.
The exposure of institutions to interest volatility
risk, foreign exchange risk, sovereign risk, and
technology risk is still not explicitly priced.9
The recent risk-based capital adequacy guide­
lines established by the Federal Reserve System
seek to explicitly price different categories of
risk. The guideline is based on a regulatory meas­
ure of capital. Capital adequacy is determined by
different capital requirement weights attached to
assets that fall into broad risk categories. By the
end of 1992, institutions are expected to meet a
minimum ratio of qualifying total capital to
weighted-risk assets of 8 percent.
The risk-based capital ratio focuses on broad
categories of credit risk and limited instances of
interest-volatility risk. However, it does not
incorporate other risk factors mentioned above.
Most important, “qualifying capital” is not
defined in objective economic terms, that is, as
enterprise-contributed capital.
Helping regulators perform the task of uncov­
ering financially troubled institutions is the orig­
inal motivation of the literature on depositinstitution failures. The next two subsections
discuss different approaches taken by earlier
empirical researchers.

Choice of
Independent Variables

The first group of studies tries to develop early
warning systems that are capable of mimicking
the regulator’s evaluation process. The hypothe­
sis of these empirical studies is that appropriately
selected financial ratios designed to measure
CAMEL’s five categories of information should be
able statistically to discriminate between prob­
lem and nonproblem institutions. According to
the definition of failure featured in this article,
these studies do not deserve to be called failure
studies because they analyze only the financial
condition of the institutions. Moreover, their eval­
uation of this financial condition is accurate only
to the extent that book values reported by an
institution approximate market values.
The second group of researchers has a more
ambitious goal. Instead of merely analyzing an
institution’s financial condition, these researchers

Semiannual Percentage Change
in Total Deposits within Each
Bank’s Local Banking Market
■ 9

For definitions of these risk categories and a discussion of how they

should be priced, see Benston et al. (1986) and Kane (1985,1989).

Definition of Independent
Variables Found Significant in
Summarized Empirical Studies

Author
Barth
et al.
(1985)

Benston
(1985)

Variable

NWTA
NITA
ISFTF
LATA
LNTA
NWTA
RETTA
YLDEAC
COSTFDC

Gajewski
(1988)

PKTAHAT
NALR
LPDR
NLTA
SENSDTD
AGTOTTL
CILTL
NITA
HCN
OGINR82

Definition
Total RAPb
Net W'onh/Total Assets
Net Income/Total Assets
Interest Sensitive Funds/
Total Funds
Liquid Assets/Total Assets
Natural Logarithm of Total Assets

Independent variables used in both groups of
studies are intended to proxy different dimen­
sions of the CAMEL rating system. Authors typi­
cally start out with either a large number of finan­
cial ratios that cover all the CAMEL categories, or
selected financial ratios that were found to be sig­
nificant in earlier studies. Independent variables
found to be significant in the reviewed studies
are summarized in table 4.
Interpretations of some financial ratios vary
across different studies. When the same ratios are
interpreted differently and classified under
separate categories by different authors, this is
noted and discussed. Authors’ classifications of
significant independent variables into CAMEL
categories are given in table 5.

Net Worth/Total Assets
Net Income/Total Assets
Change in Interest and Fee
Income/Earning Assets
Change in Interest and Depositors’
Dividends/Earning Assets
Regulator-Recognized
Capital/Assets
Nonaccrual Loans/Total Assets
Loans Past-Due 90 Days or More,
Still Accruing Interest/Total Assets
Net Loans/Total Assets
Sensitive Deposits/Total Deposits
Total Agricultural Loans/
Total Loans
Commercial and Industrial Loans/
Total Loans
Net Income/Total Assets
Corporate Structurec
County-Level Oil and Gas Sector
Earnings/Total County Earnings,
1982

a. Herfindahl index is the sum of squares of market shares for banking
organizations.
b. RAP stands for regulatory accounting principles. It is a more lenient set of
accounting principles than the generally accepted accounting principles
(GAAP). Under RAP, institutions have a higher book net-worth than under
GAAP.
c. Corporate structure variable equals zero if the bank is independent or a
one-bank holding company; it equals the number of banks in the multibank
holding company if a subsidiary.
SOURCE: See text.

set out to explain why it fails. However, although
they acknowledge the conceptual distinction
between economic insolvency and failure (Avery
and Hanweck [1984], Barth et al. [1985], Ben­
ston [1985], implicitly; and Gajewski [1988],
explicitly), their models contain the same finan­
cial ratios used in the first group of studies.

Choice of
Statistical Methods

Statistical techniques used in these studies also
differ. Earlier research used multiple discriminant
analysis (MDA), while more recent researchers
prefer qualitative response models (QRM ) . 10
Although discriminant analysis (DA) and quali­
tative response (Q R) models can be used inter­
changeably, the motivations behind the two m od­
els are quite different. What distinguishes a DA
model from the ordinary QR model is that a DA
model specifies a joint distribution of dependent
) variables, not just the
( j ; ) and independent
conditional distribution of
given t . In econ­
ometric QR models, the determination of
(bank characteristics) clearly precedes that of
(failure); therefore, it is important to specify
1 |
), while the specification of the dis­
tribution of
may be ignored. On the contrary,
in the DA model, the statement
1 (for exam­
ple, being a problem bank) logically precedes
the determination of
(problem-bank character­
istics); therefore, it is more natural to specify the
joint distribution of
and
(Amemiya [1981]).
In simple terms, DA is merely a classification
technique, while QR models analyze a causal
relationship. Because problem and nonproblem
banks do not come from different groups, but
the banks become problem banks through time,
QR models are intuitively more appealing in our
case. In other words, it is more natural to think
of problem banks being assigned to the problem
list because of their characteristics than vice versa.
In addition, QR estimators have desirable sta­
tistical properties. The discriminant analysis

(x i

xt
yf
P (y =

X

yi

X

x

y=

X

X

■

10

y

See Am em iya (1981) for a discussion of these two techniques. Judge

et al. (1985), Chapter 18 contains a thorough discussion of qualitative
response models.

Ea
T

A

B

L

E

5

1

Significant Independent Variables
Classified into C A M E L Categories

Variables

Avery and
Hanweck
(1984)

Barth
et al.
(1985)

Benston
(1985)

Gajewski
(1988)

GCARA

KTA
LNTA

NWTA

NWTA

PKTAHAT

GCONI
CI2LN

NLTA
CILNNL

Sinkey
(1975)

Altman
(1977)

Martin
(1977)

LCR

NWTA
HLBANW
ESTA
RETA
SRETA
TLTS

Capital
Adequacy
Asset
Quality

LRTR
LA

Management
Competence

OEOI
OETR

Earnings

SLRTR

NOIGOI

NITA

NITA
HERE
PTD

NALR
LPDR

NITA
ISFTF

NLTA
SENSDTD
AGTOTTL
CILTL
RETTA
NITA
YLDEAC OGINR82
COSTFDC

LATA
LNTA

Liquidity

HCN

Fraud
SOURCE: See text.

ML

X

estimator is the
estimator when
is multi­
variate normal. However, DA is not consistent
when this assumption is violated. Still, studies
analyzing robustness of discriminant analysis to
non-normality report good performance by DA.
QR models are not affected by the distribution of
Properties of the two estimators are further
discussed in Amemiya (1981).
Keeping in mind the underlying difference be­
tween the two models, DA might be useful if a
dichotomous classification is the goal. On the
other hand, QR models should be preferred
when the model, the estimation of the coeffi­
cients of the independent variables, and thus the
determination of the probability of the occur­
rence of the event, is important.

X.

Review of Prior
Empirical Literature

Sinkey’s (1975) problem-bank study is one of
the earliest on this topic. He uses linear multiple
discriminant analysis (MDA) to evaluate data on
2 2 0 problem and nonproblem commercial
banks for the period 1969-1972. Half of his sam­

ple consists of commercial banks that were listed
as problem banks by the FDIC in 1972 and early
1973- Each problem bank is matched with a
nonproblem bank based on the following char­
acteristics: ( 1 ) geographic market area, ( 2 ) total
deposits, (3) number of banking offices, and (4)
Federal Reserve membership status. The sample
contains mostly small banks (total deposits less
than $ 1 0 0 million).
After testing more than 100 ratios designed to
cover all CAMEL categories, 10 financial variables
are chosen. Among these, six significantly increase
the overall discriminatory power of the model in
a stepwise analysis. In table 4, these variables are
ranked in decreasing contribution to discrimina­
tory power. The loan revenue variable (
),
which is an indicator of asset quality, proves to
be the best discriminator.
Sinkey interprets most of the variables in his
study as proxies for management quality and
honesty, including two operating efficiency vari­
ables (
). The loan-to-capital ratio
) is taken as a measure of adequate bank
capital. Sinkey concludes that although the dif­
ferences in the means of these variables are sta­
tistically significant, the classification accuracy of

LRTR

(LCR

OEOI, OETR

the model is low due to group overlap among
the problem and nonproblem banks.
Altman (1977) also uses multiple discriminant
analysis to analyze three groups of troubled sav­
ings and loan institutions. Improving on Sinkey’s
(1975) study, he tests and rejects the equality of
group dispersion-matrices, and therefore uses a
quadratic structure. He examines data on 212 sav­
ings and loan associations during the period
1966-1973. O f these institutions, 56 are classified
as having serious problems, 49 as having tempo­
rary problems, and 107 as having no problems.
His definition of “serious problem” closely
matches the definition of failure in this paper.
He defines “temporary’ problem” institutions as
those with problems similar to the ones in the
serious problem group, but that have avoided
regulatory interference.
Finally, the “no problem” group serves as the
control group. It consists of institutions that did
not show any indication of financial problems
on the failure date of the serious-problem group,
or thereafter. The range of asset size in all three
samples is from $ 1 million to $ 1 0 0 million.
Altman tests 32 financial ratios that cover all
CAMEL categories. His best predictor model
includes only seven variables, listed in table 4.
Altman concludes that operating income
(
) and its trend are the most important
discriminators. He also finds net worth (
)
and real estate owned
) variables to be
important. He interprets these variables as
reflecting an institution’s profitability, capital
adequacy7, and asset quality.
Martin (1977) is the first author to use a logit
probability model to evaluate commercial-bank
failures. He analyzes data covering all commer­
cial banks that were members of the Federal
Reserve System between 1970 and 1976. In addi­
tion to closures, his failure definition includes
banks whose net worth “...declined drastically
over a year or less.” Therefore, his analysis
focuses on certain kinds of insolvency7 and not
just on failure.
Martin’s work represents the transition between
the first and second group of studies. He ana­
lyzes an institution’s probability of becoming
insolvent in a book-value sense before analyzing
the group characteristics. The second group of
studies takes this analysis one step further to
explain the closure process rather than merely to
approximate an early-warning system.
Martin obtains his best results using 1974 data
on 23 failed and 5,575 nonfailed commercial
banks. He analyzes 25 ratios chosen for their
usefulness in previous studies. The preferred
model includes only four variables. These varia­
bles measure earnings (
), loan quality

NOIGOI

(RETA

NIT A

NWTA

(

CI2LN, GCONI), and capital ( GCARA ).

Avery and Hanweck (1984) study commercial
bank closures using semiannual data for 1 0 0
closed and 1,190 nonclosed commercial banks
during the period December 1978 to June 1983.
Their sample includes only institutions with
assets of $250 million or less. Although closure is
acknowledged to be a regulatory decision, it is
analyzed using only nine financial ratios, chosen
because previous authors found them significant.
They assume that the probability7 of closure de­
pends on a distributed lag of the financial condi­
tion of the institution and estimate a logit proba­
bility model. Five financial-ratio coefficients
prove significant and receive signs expected a
priori. These ratios incorporate elements of earn­
ings
), asset quality
) and
capital adequacy
).
Avery and Hanweck interpret bank size (
)
as an indicator of ability7 to raise new capital.
Observing the reluctance of regulators to fail
large banks, they state that larger institutions may
raise capital more easily since it may be assumed
that they are managed better and able to turn
around faltering situations quickly7. Local banking
market variables
) are also signifi­
cant, but receive unexpected signs. Their most
puzzling result is a counterintuitive sign for
lagged financial-condition variables. They con­
clude that lagged financial ratios are not impor­
tant in explaining bank closures.
Barth et al. (1985) study thrift institution clo­
sures using a logit probability7 model. They use
semiannual data for 3 1 8 closed and 588 non­
closed savings and loan associations covering the
period December 1981 to June 1984. They also
mention that closure is a decision made by the
regulators. Again, however, only 12 financial
ratios similar to the ones used in earlier studies
are analyzed. Five of these variables receive their
expected signs and prove statistically significant.
These measure capital adequacy
), asset
quality
), earnings
), and liquidity7
). They interpret size
) as an
indicator of greater liquidity, since they believe
larger institutions have a greater ability to borrow
in order to alleviate unexpected liquidity prob­
lems. A possible alternative interpretation is that
this variable captures the reluctance of regulators
to liquidate large institutions (Conover [1984],
Seidman [1986]).
Benston (1985) conducts a logit analysis of
178 closed and 712 nonclosed savings and loans
for the period 1981-1985. Among the 28 financial
ratios he includes, only four prove statistically
significant. These are measures of capital ade­
quacy
) and earnings
and
).

(NITA

(NITA, CILNNL
(KTA, LNTA

LNTA

(HERE, PTD

(ISFTF
(LNTA, LATA

(NWTA
COSTFDC

(NITA

(NWTA

(LNTA

(RETTA, YLDEAC,

Gajewski (1988) studies commercial-bank clo­
sures by analyzing a 1986 cross-sectional data set
of 134 closed and 2,747 nonclosed banks. Empha­
sizing the need to differentiate between insol­
vency and failure, Gajewski is the first author to
incorporate this distinction into his modeling.
His model has two equations. The first mimics
the regulatory screening process, in the spirit of
an early-warning model. The second studies the
closure process. Although Gajewski recognizes
the importance of the regulatory decision­
making process in explaining bank closures, his
two equations differ only in their endogenous
variables—book-value insolvency and closure.
He analyzes both insolvency and closure using
only financial ratios and county characteristics.
Characteristics of the bank’s local economy are
represented by the percentage of county-level oil
and agricultural earnings to total county earn­
ings. A total of 25 financial ratios covering
CAMEL categories are chosen to study the finan­
cial condition and closure of the institutions.
The final specification of the logit probability
model develops 1 0 significant variables, listed in
table 4. These include measures of capital ade­
quacy
) obtained from the first equa­
tion, asset quality (
), management
competence
),
earnings
and fraud
).
What Gajewski interprets as managementcompetence variables are interpreted as asset
quality variables by earlier authors.

(PKTAHAT

NALR\ LPDR
(NLTA, SENSDTD, CILTL, AGTOTTL
(NITA, OGINRH2),
(HCN

Relative Importance
of Different C A M E L
Categories

Although cited studies analyze the relative dis­
criminatory power of different CAMEL categories,
it is difficult to compare the findings of one
study against another, due to differences in data
sets, proxies, and interpretations. Nevertheless,
all authors find capital adequacy
), generally
proxied by the book value of net worth, to be
significant. In addition, earnings
usually a
measure of net income, are a significant indica­
tor of financial condition.
After capital adequacy and earnings, asset
quality
), as proxied by various loan ratios, is
found to be a significant indicator of financial
trouble by most authors. Fraud and management
) prove to be difficult categories
competence
to proxy. Instead of explicitly representing them
by financial ratios, most authors prefer to con­
sider the set of included variables as incorporat­
ing implicitly the effects of management and
fraud. With the exception of the study by Barth

(C

(E ),

(A

(M

et al., liquidity (Z ) is not found to be a signifi­
cantly important category.

III. Possibilities for
Improving the Empirical
Analyis of DepositInstitution Failures

The literature on deposit-institution failures still
leaves much room for improvement. The first
group of studies seeks to discriminate between
problem/nonproblem and closed/nonclosed in­
stitutions using only financial ratios. The choice
of candidate regressors in the accounting-ratio
models lacks a compelling theoretical founda­
tion. Financial ratios are simply utilized in var­
ious statistical procedures until they “work.” The
second group of studies seeks to explain failure
using only instrumental variables borrowed from
accounting-ratio models. These studies fail to
distinguish successfully between insolvency and
failure in their modeling and have little theoreti­
cal underpinning.
In studying the failure of financial institutions,
it is crucial to make a distinction between eco­
nomic insolvency and failure. As discussed in
section I, economic insolvency is a marketdetermined event. In contrast, the decision to fail
an institution requires that a state commission or
federal agency realize, often under the urging of
the deposit-insurance agency involved, that a
natural propensity to forbear is no longer in its
bureaucratic interest (Kane [1985]).
Failure is a regulatory decision, influenced by
conflicts of interest that exist between regulators,
politicians, and taxpayers. These conflicts of
interest allow political, bureaucratic, and eco­
nomic pressures, and career-oriented incentives
of the regulators, to shape failure decisions.
Therefore, economic insolvency and failure of
financial institutions should be distinguished but
studied simultaneously.
Furthermore, failure should be modeled for­
mally as the outcome of a regulatory decision­
making process, explicitly taking into considera­
tion regulators’ constraints and conflicts of
interest.
In studying economic insolvency of financial
institutions, the appropriate measure is the
market value of enterprise-contributed capital.
Assuming an efficient stock market, the market
value of enterprise-contributed capital summa­
rizes the institution’s financial condition, freeing
the researcher of the dilemma of picking and
choosing the “right” financial ratios among many
possibilities. Also, if one uses financial ratios cal­
culated from balance sheets and income state-

ments, the implicit assumption is that book
values adequately proxy market values.
Adopting the market value of enterprisecontributed equity as the measure of economic
solvency and analyzing failure within a theoretical
model of regulatory decision-making brings a
much-needed structure to the choice of inde­
pendent variables, establishing a theoretical basis
for the empirical research on deposit-institution
failures.
Most studies of problem and failed banks con­
centrate on small-bank failures. They include
few, if any, large banks in their samples. How­
ever, recent increases in large-bank insolvencies
indicate the importance of developing a model
of large-bank failures.
Developing a large-bank failure model has the
further advantage of allowing us to use stockmarket data. In addition, as Kaufman (1985)
states, consequences of insolvency and failure of
large banks are blown out of proportion by the
regulators. Regulators publicly show a fear of
large-bank failures, ostensibly because of the
possible repercussions on the banking system and
on economic policy. At the time of the Conti­
nental Illinois National Bank crisis, Comptroller
of the Currency C. T. Conover (1984), in defense
of his rescue of the bank, argued:
In our collective judgement (directors of the
FDIC, the chairman of the Federal Reserve
Board, and the Secretary' of the Treasury'), had
Continental failed and been treated in a way in
which depositors and creditors were not made
whole, we could very well have seen a
national, if not an international, financial crisis
the dimensions of which were difficult to
imagine. None of us wanted to find out.

What leads to forbearance policies and ineffi­
cient insolvency resolution methods, however, is
not necessarily these vague and poorly docu­
mented consequences, but the hidden fears of
what particularly visible large-bank failures can
do to the perceptions of the quality of regulators’
performance in office (Kane [1989] )." Thus,
one would expect the political and bureaucratic
constraints of the regulators to be especially
binding when their decision to fail concerns a
large bank.

Demirgiic-Kunt (1990, forthcoming) addresses
the above issues. 12 It is a study of large
commercial-bank failures for the period 19731989. Annual panel data are used in estimation.
The failure model developed distinguishes
between economic insolvency and failure, study­
ing them simultaneously. An estimate of the
market value of enterprise-contributed equity is
taken as the measure of economic insolvency.
Failure determination is based on a theoretical
model of failure decision-making in the spirit of
the Kane model. The theoretical model identifies
and explicitly incorporates important regulator
constraints and incentives. In the empirical
model, the FDIC’s number of examiners and size
of the insurance fund are proxies for economic
constraints, whereas failure rate (for banks and
businesses), number of problem banks, variance
of interest rates, and bank size are included to
proxy political and bureaucratic constraints
implicit in the career-oriented incentives of
regulators.
As expected, results indicate that regulator
constraint and incentives play a significant role
in failure determination. The empirical model of
bank failures developed in Demirgiic-Kunt
(forthcoming) is more complete because it takes
into consideration a previously ignored determi­
nant of the decision-making process and brings
theoretical structure to the empirical depositinstitution failure literature.
One possibility for future research in this area
of deposit-institution failures is to investigate
changes in regulatory decision-making through
the years. Periodic restructuring of the financial
system (most recently by the 1989 FIRRE Act)
leads to shifts of power among different regula­
tory bodies and may affect failure decisions. It is
also important to take into consideration differ­
ences among various insolvency resolution
methods, that is, different categories of de facto
failure (Maddala [1986] ) . 13 Development of a
failure model that distinguishes between differ­
ent methods of insolvency resolution is the next
challenging task facing economists.

■ 12

See Demirgiic-Kunt (1989) tor a preliminary version of the study and

empirical results. The theoretical model is fully developed in Demirguc-Kunt
(1990, forthcoming).

■ 11

A discussion of these policies can be found in Kane (1985, 1989),

Benston et al. (1986), and Caliguire and Thomson (1987).

■

13

For a discussion, see references in footnote 11.

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Settlem ent Delays
and S to c k Prices
by Ramon P. DeGennaro
Ramon P. DeGennaro is a visiting
scholar at the Federal Reserve Bank
of Cleveland. The author would like
to thank Randall W . Eberts, Jam es
T. Moser, and Jam es B. Thomson
for helpful comments.

Introduction

The typical stockbroker requires only about two
minutes to execute and confirm a market order.
During that time, the order is routed electroni­
cally either to the specialist or to the Intermarket
Trading System, which connects eight regional
markets including the New York Stock Exchange
and the National Association of Securities Deal­
ers. These agents then pair the order with another
buy or sell order. 1 Thanks to modern technol­
ogy, the process of executing a trade and pro­
ducing a confirmed order is quick and efficient.
Although this confirmed order represents a
binding contract between the buyer and seller,
neither the security nor payment for the security
changes hands at the time the trade is con­
firmed. Instead, payment for the stock occurs five
business days later, when the buyer delivers a
bank check to the seller and the seller delivers
the promised securities. 2 Until final payment is
made, the stock trade remains conditional, and

■

1

■ 2

For a further discussion of trading details, see Jakus and Chandy (1989).

In practice, these transactions usually are executed by brokers acting

as agents.

official title remains with the seller, who cannot
use the proceeds of the sale.
The equity markets have no provision to com­
pensate the seller for the opportunity cost he
bears while waiting for the trade to clear. In con­
trast, bond-market procedures call for explicit
adjustment of the cost of the bond for interest
accrued since the most recent coupon date.
Interest is calculated using the number of days
from the last coupon payment until the date of
delivery, not the date of the trade. If the terms of
the trade call for delivery tomorrow instead of
today, the buyer must pay an extra day’s worth of
interest. Another important market, residential
real estate, while not explicitly adjusting the pur­
chase price for the date of closing, does prorate
taxes and rents for the date of occupancy.
Although the stock markets make no explicit
adjustment for the opportunity cost of settlement
delays, rational investors do not ignore the fact
that they lose several days’ worth of interest.
Indeed, much empirical work has assumed that
investors consider delivery procedures in pricing
assets, although few studies have tested this
theory.
This paper studies whether investors do, in
fact, consider settlement delays in determining
stock prices. We construct two models of stock

returns. The first expresses returns as a function
of changes in the settlement delay. The second
models returns as a function of changes in the
length of the delay and in the federal funds rates
during the delay. The first model controls for
variation in the length of the delay, while the
second controls for both the opportunity cost and
the length of the delay. We then conduct regres­
sion tests of the significance of these variables.
Both models show that in the full sample and all
subperiods, investors apparently do consider the
settlement delay; the variables controlling for it
are statistically significant and correctly signed.
Section I reviews previous research regarding
payment delays, and section II develops our
model of the return-generating process. In sec­
tion III we describe the data, conduct prelimi­
nary tests, and report the results. A summary
concludes the paper.

I. Previous Research and
the Impact of Delivery
Procedures

Lakonishok and Levi (1982) speculate that set­
tlement and check-clearing delays might explain
the “weekend effect” in stock prices. The week­
end effect refers to the well-documented ten­
dency of stock prices to decline on Monday. 3
Lakonishok and Levi note that, in addition to the
settlement delay, the check presented at settle­
ment requires another business day to clear. They
claim this makes the total payment delay six bus­
iness days. For their empirical work, they add
and subtract interest based on the prime rate,
but, more important for our purposes, they con­
duct no tests to determine if buyers actually do
compensate sellers in the manner they suggest.
DeGennaro (1990, forthcoming) tests the con
jecture that the combined settlement and checkclearing delays explain the weekend effect. He
concludes that, while the combined delay fails to
explain the weekly return pattern, it does appear
to influence measured stock returns. However,
he also reports that the estimated rate of com­
pensation for the combined delay varies substan­
tially, suggesting that further work is necessary.

■ 3

The weekend effect was first identified by Cross (1973). An important

paper by French (1980) reexamined this apparent anomaly, demonstrating that
returns on M onday are so persistently negative that rational investors must
expect to suffer losses on M ondays. Lakonishok and Smidt (1988) extend the
evidence of negative Monday returns to a 90-year sample. Gibbons and Hess
(1981) show that Treasury bills also earn below-average returns on Mondays,
although returns are not negative for bills.

Another example is Choi and Strong (1983),
who study “when-issued” common stock. Firms
announce stock issues well in advance of the
time the new securities are issued; investors
trade these securities on a “when-issued” basis.
Choi and Strong attempt to determine why this
when-issued stock commands a premium over
the corresponding stock that is currently out­
standing. They speculate that when-issued stock
represents the existing share plus a zero-interest
loan. They find that adjusting prices for the
interest savings is insufficient to explain the dis­
crepancy, but again, they do not test to see if
investors price the zero-interest loan.
More recently, Flannery and Protopapadakis
(1988) assume that settlement and clearing
delays are priced in their test of the generality of
the weekend effect. They study three stock
indexes and seven Treasury bond maturities to
learn if intraweek seasonality is the same across
these assets. Following the suggestion of Lakoni­
shok and Levi, they adjust the returns on the 10
assets to control for the financing costs incurred
during the payment delays. They find that the
returns on these assets do not vary in a similar
manner during the week, but again, the authors
do not test if the delay is actually priced.
DeGennaro (1988) shows such payment
delays can have important implications for inter­
est rates. If delays exist in the Treasury bill
market, but are not explicitly incorporated into
pricing equations, certain common estimators of
term premiums are biased in favor of finding
positive premiums. He shows that this bias is
sufficiently large to explain the results of McCulloch (1975). However, he does not test if inves­
tors do, indeed, consider these delays.
The results of the present paper are important
for several reasons. First, if the delays have no
impact on observed prices, then the aforemen­
tioned studies must be flawed: theoretical work
begins with inappropriate assumptions, and
empirical studies are misspecified. Second, if
investors do consider settlement delays in deter­
mining equity prices, then observed prices
diverge from true prices. This has implications
for the event-study methodology commonly used
in empirical tests (see, for example, Hite and
Owers [1983]). To conduct an event study, the
researcher first estimates the parameters of a
model using time-series data prior to the event
in question. He then calculates abnormal returns,
defined as realized returns less the returns pre­
dicted by the model. Significance tests can be
conducted using the cumulative sum of these
residuals.
To date, all event studies known to the author
have ignored the possibility that payment delays

may influence the measured stock price and
return. If these delays do affect stock prices,
events that may seem to be economically signifi­
cant may in fact be negligible once proper
accounting for the delays is made. Conversely,
events judged to be insignificant may be
important.
Consider, for example, an event that the
researcher expects to generate positive returns,
but which in fact does not. The total compensa­
tion for the settlement delay, capitalized in the
observed price, may be higher than usual on the
event date (due to a holiday that lengthens the
delay, or perhaps simply to an increase in interest
rates). This would make the observed price
higher than usual, biasing the significance of test
statistics. The reverse might also be true. The
economic effect of an event may be positive and
significant, but if the number of calendar days in
the delay is lower than usual, or if the opportunity
cost on a daily basis is less, then the impact of a
true economic event might be negated and
appear insignificant.
Other important results might also be affected.
For example, French and Roll (1986) document
a large decrease in volatility when markets are
closed. The variance of stock returns from Friday’s
close to Monday’s close is only about 10 or 15
percent higher than during a one-day holding
period. If the opportunity cost of the settlement
delay varies systematically— for example, if inter­
est rates or the delay varies according to the day
of the week— French and Roll’s variance ratio
measures both the true volatility and the variance
in the opportunity cost. While this is unlikely to
be sufficient to overturn their results, divergences
from true prices are especially important in stud­
ies of variance, which is a particularly sensitive
measure due to the squaring of deviations from
the mean.
Perhaps the most important reason for studying
whether delivery procedures are important and
whether settlement delays are priced is their
implication for market efficiency. If the settle­
ment delay does not affect prices, then research­
ers must not only reinterpret research that pre­
sumes it does, but they must also explain why
rational investors ignore the fact that the present
value of the purchase price is reduced because
of these delays.
The choice of delivery procedures may become
an increasingly important policy issue for the
securities industry. For example, the present fiveday delivery terms trace to the inability of tech­
nology to handle heavy' trading volume during
the late 1960s. Prior to February 9, 1968, the set­
tlement period was only four business days;
extending it to five ensures that brokers have a

weekend between the trade date and the delivery
date to complete the necessary paperwork. Con­
ceivably, further increases in volume could force
another extension, while technological advances
might permit a reduction.
A reduction in the time between the trade
date and the delivery7 date may be important in
preventing defaults on trades. For example,
although the buyer and seller commit to trade at
the confirmation of their order, large price
changes create incentives for one side of the
transaction to renege. For example, equity pur­
chasers during the week of October 12, 1987,
expected to receive stock worth a given amount;
instead, they received stock worth about 2 0 per­
cent less. Although the safeguards against such
defaults proved adequate in this case, the
increasing volatility of financial markets observed
in recent years means larger losses can be sus­
tained between the time of the trade and the
date that the trade becomes final, increasing the
likelihood that the buyer will default.

II. The Model

If investors consider delivery procedures in pric­
ing stocks, then observed prices contain the true
value of the underlying asset plus an adjustment
for the settlement delay. Observed prices mis­
state true values. Since empirical work must use
observed prices, we must devise a model that
removes any adjustment the market incorporates
for the delay. To do this, we first define the true
as the price observed in the
stock price,
absence of delays. The expected true price at
time / asa function of the true price at the
beginning of the holding period (time
1 ) is

P*,

t-

(1 )

£ ,.,( ? ;) =

P *_,exp [E (R *) - Et _ ,(d t)],
Et _ x

where
is the expectations operator condi­
tioned on information available at time
1 ,
is the unobservable true price at time ,
is the unobservable (constant) expected
continuously compounded daily rate of return
on the stock in the absence of delays,
is the
dividend yield, and
is the base for natural
logarithms. Equation (1) states that if no divi­
dends are expected to be paid, the expected
price at is the price at
1 adjusted for the
expected continuously compounded rate of
price appreciation. If dividends are expected to
be paid, the expected price is adjusted down­
ward accordingly.

t-

P*
E (R *)

dt

exp

t

t-

t

bQ

To incorporate the settlement delay, the
observed price
is written as

P

st

P - P *exp(% c/t),
j= i

(2)

bx

7

st

where
is the number of days in the settlement
delay and
is the continuously compounded
rate of compensation on day for trades made at
If investors ignore delivery procedures,
equals zero and the true price equals the
observed price. If sellers demand and receive
compensation for the settlement delay, is posi-

cJt

j

t.

c

c

stcjt

tive. In equation (2), ]£

represents the total

j= i

compensation to the seller for financing the
position until he receives the proceeds of the
sale at settlement.
Equation (2) is also true at
1, so

t-

(3)

P t _ ! = />*_

st - i
l exp( X C/o-i))j

=1

P*,

Solving equations (2) and (3) for
substitut­
ing into equation (1), and assuming that the
and are uncorrelated, we can rearrange equa­
tion ( 1 ) to obtain

P

c

(4)

st ~st-Xcj(t Xcjt
J = j=

log[£, _ x( P t) / P t . J +

Et „ x(dt) = E (R *) +

1

1

i

c

Et _x(Rt )

i) •

t,

=

E (R *)

s, as the

(c

(7)

y/j, = cj„
jJt

t,

st
'XS, as X fj, , so that
J=

7

X

R ,.

R

R, = bQ+ b xAs, + et .

st
^ S t = X cj f
j=

Substi-

1

tuting 2)5 into equation (4) and combining
terms yields

Et _ x(Rt )

= £ (/? * ) +

XS,

+ c X As,,

j

where
is the federal funds rate on day of the
settlement delay for trades at
and 7 is a
constant. For notational convenience, we define

(8 )

where the total expected return on the stock—
capital gains plus dividends— is written as
Intuitively, equation (5) says the observed
expected return equals the expected return in
the absence of delays plus changes in the impact
of delays.
To proxy for the dependent variable t , we
use the return on the value-weighted portfolio,
including dividends, provided by the Center for
Research in Security Prices (CRSP). Although we
have derived our model in terms of an individ­
ual stock, if the settlement delay affects any
stock, it must affect all stocks. Further, this effect
is not diversifiable: any settlement effects must
appear in the observed return on a portfolio.
Substituting ex post values, we obtain our test
equation:
(6 )

c,

1

Letting be constant and defining A
change in 5 at time we obtain
(5)

In this model,
estimates the unobservable
expected continuously compounded daily rate of
return on the stock in the absence of delays, and
estimates
the rate sellers receive as
compensation for the settlement delay. Theory
suggests that both coefficients should be posi­
tive. This is because risk-averse investors require
a premium to compensate for the nondiversifiable
risk contained in stocks, and increases in the
financing costs during the settlement delay
require buyers to raise their bids to compensate
the sellers. Therefore, one-tailed tests are
appropriate.
One potential problem with this specification
is that As varies relatively little. To circumvent
this, we also estimate a second specification.
Rather than letting the settlement cost per day
) be constant in equation (4), we use the
federal funds rate as a proxy for c. The federal
funds rate is both readily available and respon­
sive to changes in the economic environment.
Formally, we write

7

XS

X

AXS t ,
t,

where A
is the change in
at and the
total expected return on the stock is again writ­
ten as
Substituting ex post values, we obtain

Rt .

(9)

Rt = b'0 + b\AXSt + e't .
b'0

As in equation ( 6 ),
estimates the unobserv­
able expected continuously compounded daily
rate of return on the stock in the absence of
estimates 7 , the
delays, but in this model,
proportion of the federal funds rate sellers
receive as compensation for the settlement
delay. One-tailed tests are again appropriate.
Equation (9) offers both advantages and dis­
advantages relative to equation ( 6 ). In equation
( 9 ), the independent variable is a function of the
federal funds rate, and therefore may be simul­
taneously determined with the stock return.
However, it controls for both the length of the
settlement delay and the opportunity cost during
that delay rather than for simply the number of

b\

T

A

B

L

E

Regression Results

Estimates obtained by regressing the rate of return on the
CRSP value-weighted index, including dividends
on the
change in the settlement delay (A
corrected for
heteroscedasticity:

(Rt),

st),

Rt= b Q + b jAs, + u t ,

Full sample period: January 1, 1970-December 31, 1986
(4,296 observations)
Parameter estimate (f-statistic)
4.68 x IO ' 4
(3.03)a
7.27 x 10“ 4
(4.07)a

e

III. Data and Results
Data

ut = et - 8et _ j .

Variable

st

AXSt

Equation (1 0 ), Full Sample

(10)

This asymmetric treatment permits the brokerage
firm to use the funds between the two dates. The
firm generates revenue by imposing an added
cost of trading on its customers. If these inves­
tors are the marginal traders, neither A nor
measures the true cost of the delays these
investors face. Again, the estimated slope coeffi­
cients could be insignificant.

0.23
(15.5)a

a. Significant at the 1 percent level.
NOTE: Significance levels are for one-tailed tests on
SOURCE: Author’s computations.

b() and bY

The stock-return measure is the return on the
CRSP value-weighted index, with dividends. We
use 4,296 observations from January 1, 1970,
through December 31, 1986. Federal funds rates
used to compute the opportunity cost of the set­
tlement delay are from the Federal Reserve
Board. We estimate equation ( 6 ) in the full sam­
ple and in three subperiods partitioned at
October 6 , 1979 and October 9, 1982, the dates
of important changes in the Federal Reserve’s
operating procedures. O n the former date, the
central bank began focusing on the level of
nonborrowed reserves rather than on the level of
the federal funds rate. On the latter date, it began
attempting to stabilize interest rates.

Preliminary Tests

days in the delay. It is also much more variable
than
in equation ( 6 ).
Which economic or institutional forces could
cause the slope coefficients in equations ( 6 ) and
( 9 ) to be not significantly different from zero?
First,
is the
settlement delay
Although the exchanges alter
only rarely,
brokerage firms may not credit and debit
accounts as accurately as the exchanges. For
example, they may err and credit a customer’s
account later than promised. Such mistakes may
not always be discovered. Even if the customer
does detect the error, he must take the time to
complain. Investors may, therefore, base com­
pensation on the expected value of the delay
rather than on the promised delay. If so, the
independent variables in equations ( 6 ) and ( 9 )
are incorrect proxies for the true values, and the
estimated coefficients could be insignificant.
In addition, some investors face different
values of because of the procedures of their
agents. For example, some brokers debit
accounts for purchases on the trade date, but
credit accounts for purchases only on delivery.

As
st

promised

st

st

The ordinary least squares residuals from equa­
tion ( 6 ) exhibit positive first-order serial correla­
tion, while higher-order autocorrelations are
small. This is consistent with the use of an index
as the dependent variable and with the results of
Scholes and Williams (1977). To see this intui­
tively, note that some securities composing the
index do not trade at the closing bell. The most
recent prices for these securities are “stale.” If
the market moves up or down since the last
trade, these stale prices tend to move in the
same direction when the securities subsequently
do trade, inducing serial correlation at lag one.
Therefore, we fit a first-order moving average to
equation ( 6 ) and estimate
(10)

Rt = b 0 + b jAs, + u t ,
ut = et - 6et _ j .

To formally investigate the possibility that the
parameters in equation ( 1 0 ) may not be stable
across subperiods, we conduct the test according
to Chow (I960) for each subperiod partition.
These tests show that both break points are

T A B L E

2

F

Regression Results

Equation (1 0 ), Subperiods

Estimates obtained by regressing the rate of return on the
CRSP value-weighted index, including dividends (/?,), on the
change in the settlement delay (As,), corrected for
heteroscedasticity:
(10)

Rt=

b 0 b ,As, + u t ,

F

+

ut - e t - 6el -1 •
t

First sample period: January 1, 1970 - October 6 , 1979
(2,467 observations)
Variable

Parameter estimate (^-statistic)
x 10"4
(1.47)a

3 .1 0

b o

b\

7.20 x 10“ 4

Q

0.31
(I6.3)b

Results Using the Change
in the Length of the Delay

(3.43)b

Second sample period: October 7, 1979 - October 8 , 1982
(760 observations)
Variable

Parameter estimate (^-statistic)

bo

5.60 x IO ' 4
( 1.41 ) a

b\

8.73 x 1 0 - 4
(1.76)c

e

0.17
(4.86)b

Third sample period: October 9, 1982 - December 31, 1986
( 1 , 0 6 9 observations)
Variable

F

necessary. For the first partition, the -value is
7.64, which exceeds the 1 percent critical value
of 3-78. For the second, the -value is 5.59,
which again is significant at the 1 percent level.
In addition, the test rejects the conjecture that
the first and third subsamples can be combined.
Because of weekends and holidays, holding
periods range from one to four days. Given the
results of French and Roll (1986), we would
expect heteroscedasticity to be present, depend­
ing on the holding period for the observations.
This proves to be the case. In the full sample, for
example, the -ratio using the variance of the
three-day holding period and the one-day hold­
ing period is 1 .3 1 , which exceeds the critical 1
percent value of 1.15. Similar results are found
for both subperiods. Therefore, we weight
observations by the inverse standard deviation of
the residuals for the holding period in all
reported results.

Parameter estimate (^-statistic)

b o

b\
6

7.61 x IO " 4
( 2 .9 2 ) b
x 10 -4
( 1 .6 0 )a

5 .9 8

0 .1 0

(3.42)b
a. Significant at the 10 percent level.
b. Significant at the 1 percent level.

c. Significant at the 5 percent level.
NOTE: Significance levels are for one-tailed tests on b(j and bv
SOURCE: Author’s computations.

Table 1 contains the results obtained by estimat­
ing equation (10) using the full sample. Given
the results of the Chow tests reported above,
these estimates must be interpreted with caution,
but we report them for completeness. All
parameters have their expected signs and are sta­
tistically significant. The intercept, which esti­
mates the expected daily stock return in the
absence of delays, implies an annual rate of
about 11.80 percent. This is quite close to the
actual realized value of 10.97 percent. The
parameter
estimates
the rate of compensa­
tion for the settlement delay. This parameter is
also significant, with a -statistic of 4.07.
Table 2 contains the results from the subperi­
ods, which are broadly consistent with the full
sample. For the first subperiod, the intercept is
positive and significant at the 1 0 percent level,
and is almost exactly the correct magnitude. The
estimated value of . 0 0 0 3 1 0 implies an annual
rate of about 7.81 percent; the actual value was
7.13 percent. The estimate of
is reliably dif­
ferent from zero, with a -ratio of 3 43.
After the first change in Federal Reserve oper­
ating policy, the results are somewhat different.
The intercept is still marginally significant and
again about the correct size (it implies a daily
rate of 14.10 percent versus the actual 12.08 per­
cent). Despite being larger in magnitude, how­
ever, the significance of the slope coefficient is
smaller. The -ratio is 1.76. The larger standard
error is consistent with the smaller sample size
and with the increased volatility during this

bx

c,

t

t

t

bx

Regression Results

Equation ( 1 1 ) , Full Sample

Estimates obtained by regressing the rate of return on the
CRSP value-weighted index, including dividends
on the
change in total return from an investment in federal funds
during the settlement delay (ASS,), corrected for
heteroscedasticity:___________________________________ _

(Rt),

(11) Rt= b'0 + b{&XSt + u't,

b

y,

t

u \ - e't - d'e't _ j .

t

Full sample period: January 1, 1970-December 31, 1986
(4,296 observations)
Variable
Parameter estimate (^-statistic)

bo

For completeness, table 3 contains the results
obtained by estimating equation ( 1 1 ) using the
full sample. Again, all parameters have their
expected signs and are statistically significant.
The intercept, which estimates the expected
daily stock return in the absence of delays, is
very close to the value in table 1. The parameter
j estimates
the proportion of the federal
funds rate that buyers receive as compensation
for the settlement delay. This parameter is also
significant, with a -statistic of 4.02. The coeffi­
cient of 2.73 is also reliably different from unity.
A -ratio testing the hypothesis that the esti­
mated value equals one is 2 .5 5 , which rejects the
null hypothesis at the 1 percent level. Thus, we
reject the conjecture that the rate of compensa­
tion is the federal funds rate. The federal funds
rate is too low or too stable to serve as the rate
of compensation.
Table 4 contains the estimates from the sub­
periods, which are again similar to those from
equation (10). For the first subperiod, the inter­
cept is the same size and is equally significant as
in table 2. The estimate of the slope coefficient,
is 3-80. As is the case for the full sample, this
is reliably different both from zero and from
unity. The -ratios are 3.64 and 2.69, respectively
After the first change in Federal Reserve oper­
ating policy, the intercept is still significant and
again about the correct size, but the slope coeffi­
cient is much smaller. The estimated value is
1.84. This differs from zero at the 10 percent
level, but unlike the case in the first subsample,
it does not differ from unity. The -statistic is
only 0.71. We cannot reject the hypothesis that
the rate of compensation for settlement delays
equals the average realized federal funds rate
during the sample.
The third sample begins on October 9, 1982.
The results are similar to the second subsample
and comparable to equation (10). The estimate
of o is significant and implies a stock return of
19.18 percent, compared to the actual value of
19.07 percent. The estimated slope coefficient,
\, is 2.59, which differs from zero at the 5 per­
cent level, but does not differ from unity. The
-statistic is only 1.03.
The results suggest that during the first sample,
the Federal Reserve’s intervention in the federal
funds market prevented the federal funds rate
from tracking market conditions as well as it did
during periods when the Federal Reserve concen­
trated on other policy vehicles. When the federal

4.69 x 1 0 - 4
(3.03)a
2.73
(4.02)a

d'

0.23
(15.6)a

b \,

a. Significant at the 1 percent level.
NOTE: Significance levels are for one-tailed tests on b’} and b\.
SOURCE: Author’s computations.

period, when the Federal Reserve did not
attempt to stabilize interest rates.
The third sample begins on October 9, 1982.
The results of this subsample are similar to those
of the second subsample. The estimate of
implies a stock return of 19.17 percent; the
actual value was 19.07 percent. The -value of
2.92 is significant at the 1 percent level. The
estimated slope coefficient is 0.000598, which
differs from zero at the 1 0 percent level.

b0

t

Results Using the Change
in the Opportunity Cost
During the Delay

The preliminary tests using equation (9) yield
results similar to those of equation ( 6 ). Chow
tests confirm that the subperiods are best esti­
mated separately. Heteroscedasticity is again
present, and a first-order moving average is
required. We estimate

t

t

b

b
t

Regression Results

Equation ( 1 1 ) , Subperiods

Estimates obtained by regressing the rate of return on the
CRSP value-weighted index, including dividends
on the
change in total return from an investment in federal funds
during the settlement delay (A
corrected for
heteroscedasticity:

(R,),

XSt),

Rt= b'0 + b{AXSt + u't ,

(11)

u \ - e \ - d'e't _ j .
First sample period: January 1, 1970 - October 6, 1979
(2,467 observations)
Variable
Parameter estimate (^-statistic)
bo

b\
Q'

x IO ' 4
(1.46)a

3 .1 0

3.80
(3.64)b
0.31
( l 6 .2 ) b

Second sample period: October 7, 1979 - October 8, 1982
(760 observations)
Variable

Parameter estimate (^-statistic)

K

5.62 x 1 0 - 4
(1.41 )a

b\

1.84
(1.55)a

Q’

0.17
(4.85)b

Third sample period: October 9, 1982 - December 31, 1986
(1,069 observations)
Variable

Parameter estimate (f-statistic)

b'o

7.61 x IO ' 4
(2.92)b

b\

2.59
( 1 .6 8 )c

6'

b\

not

b

t

0 .1 0

(3.44)b
a. Significant at the 10 percent level.
b. Significant at the 1 percent level.
c. Significant at the 5 percent level.
NOTE: Significance levels are for one-tailed tests on
SOURCE: Author’s computations.

funds rate is permitted to float freely, we cannot
reject the notion that stock purchasers compen­
sate sellers for the settlement delay at the federal
funds rate. However, when the central bank
intervenes, the federal funds rate appears to be
too stable to serve as the rate of compensation.
Since the estimates of
exceed unity, they
are higher than predicted by Lakonishok and
Levi (1982), who argue that delays should be
compensated at the riskless rate. To the extent
that the overnight federal funds rate is riskless,
the coefficient should be one if Lakonishok and
Levi are correct. The results in table 4 are, how­
ever, consistent with their empirical results.
Lakonishok and Levi assume that settlement and
check-clearing delays are priced at the prime rate
and test to see if the prime rate is large enough
to explain the weekend effect. Although a strict
interpretation of their story requires that sellers
be compensated at the riskless rate, they report
that the prime rate is too low to eliminate these
effects completely. This suggests that if the set­
tlement and check-clearing delays were in fact
the sole reason for the weekly pattern, rates of
compensation during these delays must be
larger than the riskless rate. Since our results
apply only to the settlement delay and not to the
check-clearing delay, they do not directly relate
to those of Lakonishok and Levi. However, they
do suggest the possibility that rates of compensa­
tion are larger than the riskless rate.
Conceivably, though, the rate of compensation
should
be the riskless rate: errors in posting
to brokerage or bank accounts do occur. While
restitution is always made if the error is caught,
the seller may not notice it. Even if he does,
complaining is time-consuming. The seller may
therefore require a premium over the riskless
rate. In addition, the buyer may very well be wil­
ling to pay this premium. If he monitors his
account, it cannot be debited early, but through
bank or brokerage error, it may be debited late.
Since the buyer can only win, he is willing to pay
extra for this possibility.
Using the brokers’ call money rate as the
interest rate proxy7 would probably produce
smaller values of
This rate tends to be higher
than the federal funds rate, so smaller propor­
tions of the call money rate imply the same lev­
els of compensation. If the call money rate is as
variable as the federal funds rate, -tests would
be less likely to reject the notion that the rate of
compensation is the call money rate.

b'() and b\.

IV. Conclusion

This paper shows that investors consider delivery
procedures in pricing stocks. We model stock
returns in two ways. The first uses a function of
the length of the settlement delay, while the
second uses a function of both the length of the
delay and interest rates during the delay. We find
that the coefficient on this variable is always cor­
rectly signed and statistically significant. This
means that observed prices diverge from the
prices that would be observed in the absence of
this trading mechanism. This, in turn, means that
measured returns diverge from true returns.
While this result is comforting to researchers
who have assumed that settlement delays are
priced, it does have implications for empirical
studies using daily stock-return data. Since the
observed price equals the true price plus a pre­
mium to compensate for financing costs, meas­
ured returns diverge from true returns if the pre­
mium changes during the holding period. This
could, for example, affect event studies either by
masking the impact of a true economic event or
by lending statistical significance to “events”
which result only from changes in the premium
and not from any underlying economic force.

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Cross, Frank, “The Behavior of Stock Prices on

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Flannery, Mark J. and Protopapadakis, Aris A.,
“From T-Bills to Common Stocks: Investigat­
ing the Generality7 of Intra-Week Return Sea­
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June 1988,
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43,

French, Kenneth R., “Stock Returns and the

Journal of Financial Eco­
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Weekend Effect,”
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and Roll, Richard, “Stock Return Var
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Reaction of Traders,”
September 1986,
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the Week Effects and Asset Returns,”
October 1981,
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54,

Hite, Gailen L. and Owers, James E., “Security
Price Reactions Around Corporate Spin-off
Announcements,”
December 1983,
409-36.

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Jakus, Judy and Chandy, P.R., “After Your Order:

Financial
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the Saga of a Stock Transaction,”
Summer 1989,
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Lakonishok, Josef and Levi, Maurice, “Weekend

e," Journal of

Effects on Stock Returns: a Not
883-89.

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nal of Financial Economics,

The E ffe c t of Bank
Structure and
Profitability
on Firm Openings
by Paul W. Bauer
and Brian A. Cromwell

Paul W . Bauer and Brian A . Crom­
well are economists at the Federal
Reserve Bank of Cleveland. The
authors thank Randall Eberts, Kathe­
rine Sam olyk, Jam es Thomson, Gary
Whalen, and David Whitehead for
useful discussion and suggestions.
Ralph Day and Lynn Downey pro­
vided valuable assistance with the
data and systems. Fadi Alameddine
and Kristin Smalley provided excel­
lent research assistance.

Introduction

The banking industry has undergone significant
changes in recent years. Much attention has been
given to the effect of financial deregulation and
interstate banking on the structure of the bank­
ing industry7. Attention has also been directed at
the systematic effects of financial structure on the
national economy.
However, bank structure can also affect local
economic development. 1 The availability and
the cost of financing potentially varies across
regions due to differences in bank structure and
in the health of the local banking sector. Since
bank credit is an important source of financing
for new firms, differences in bank structure can
affect regional growth.
This paper examines the effects of bank struc­
ture and profitability on the birth of new firms,
an important component of economic develop­
ment. Specifically, we enter measures of profita-

■

1 W e use the term “bank structure" to refer to both the organization of

banks themselves (number of branches, employees per bank, etc.) and the
market structure of the banking sector (concentration, ease of entry, etc.).

bility, concentration, size, and entry of a region’s
banking sector (as well as an overall measure of
lending activity) into a standard model of firm
location. This enables us to test for independent
effects of bank structure and profitability on re­
gional growth, as measured by business openings.
Our results suggest that bank structure and
profitability have significant effects on firm open­
ings. A profitable and competitive banking mar­
ket is associated with a higher rate of firm births.
In particular, firm births are found to be asso­
ciated with higher bank profits, higher numbers
of bank employees, lower levels of concentration,
higher proportions of small banks, and freer
entry of new banks into the region. These results
support the position that bank structure and
profitability influence economic development.
Section I briefly reviews previous work relat­
ing banking and economic activity and discusses
the implications of bank structure for regional
growth. Section II presents a standard model of
firm location and extends it to include measures
of bank structure and profitability. Section III
describes the data, and section IV provides
results on the impact of banking on firm loca­
tion. Finally, section V presents conclusions.

I. Bank Structure
and Regional Growth

With the advent of deregulation and interstate
banking, the banking industry has changed sig­
nificantly in recent years. Much attention has
been given to the effects of these developments
on the structure of the banking industry itself. 2
Attention has also been directed at the systematic
effects of bank failures and financial structure on
aggregate economic activity. 3 The effect of
changes in bank structure on regional econo­
mies, however, remains an open question.4
For example, Eisenbeis (1985), in a recent
article on interstate banking, comments that:
The most controversial issues surrounding considera­
tion of modifying interstate banking laws deal with
the implications of proposed changes for competi­
tion and concentration of resources. There is little
doubt that restrictions on geographic expansion
have, in the past, insulated many local markets from
competition and have restricted economic growth.
While casual inspection of the data suggest that states
with more liberalized policies toward intrastate bank­
ing have generally had higher economic growth rates
than unit banking states, empirical studies show no
convincing relationship between banking structure
and economic development. More detailed study
would have to be done to determine whether this is
just a matter of correlation or causation, (p. 231-32)

■

2

For example, Lee and Schweitzer (1989) use event-study analysis to

determine the effect on stock prices of decisions by bank holding companies
(B HCs) to establish subsidiaries within Delaware and find no evidence of long­
term stock price changes during the postannouncement period. Trifts and Scan­
lon (1987) use a sample of interstate mergers to provide early evidence of the
effects of interstate bank mergers on shareholder wealth. Born, Eisenbeis, and
Harris (1988) provide evidence on the market evaluation of financial firms
entering into interstate banking when restrictions are relaxed and find no signif­
icant effect of an announced geographic interstate expansion on shareholder
values.
■

3 Gertler (1988) provides an overall review. Bernanke (1983) argues that

extensive bank runs and defaults in the 1930-1933 financial crisis reduced the
efficiency of the financial sector in performing its intermediation function, caus­

We approach this issue by studying the effect of
bank structure on business openings. If bank
structure and the health of the local banking sec­
tor affect the cost and availability of credit for
new firms, changes in bank structure will poten­
tially affect regional growth.
Financial institutions, especially banks, are the
primary supplier of external funds to new busi­
nesses, which are typically small, independent
enterprises. Unlike medium-sized (100 to 500
employees) or large corporations, small busi­
nesses have limited access to organized open
markets for stocks, bonds, and commercial paper.
Approximately three of every four existing small
businesses have borrowed from banks. 5
The availability of credit at affordable rates for
the start-up and the continued operation of new
firms is not necessarily a given. 6 For small start­
up firms (typically “mom and pop” operations),
financing comes mostly from private sources,
such as personal savings, home equity loans, and
loans from friends or relatives. For larger small
businesses, capital for start-ups comes from
financial institutions and organized venture capi­
tal firms, as well as from friends, relatives, and
informal investors. Even after being established,
firms may require financing when cash inflow
lags behind cash outflow due to a rise in receiv­
ables or an inventory buildup.
When external financing is used, it is received
primarily from commercial banks. The rates
charged for small start-up firms are typically 2 to
3 percentage points above that charged for larger
firms. This is due in part to the high-risk nature
of new small businesses, which lack collateral
and a credit history and suffer high rates of
failure.
Some researchers and many policymakers argue
that banks do not meet the needs of various
types of businesses, particularly small businesses.
They contend that due to high monitoring costs
and a lack of adequate information about risk, a
market failure exists— popularly referred to as
the “credit gap.” It has been argued that the
price of credit, especially working capital, pro­
vided to small and middle-sized firms is too high
after controlling for appropriate risk factors. The

ing adverse effects on real output, other than through monetary channels.
Samolyk (1988) conducts a similar test on British data, using corporate and
noncorporate insolvencies as proxies for the health of the financial sector, and
also finds that credit factors matter empirically on output. Gilbert and Kochin
(1990, forthcoming) provide additional tests of the hypothesis that bank fail­

between savers and investors. They argue that the role intermediaries play in

ures have adverse effects on economic activity using rural county-level data

improving the efficiency of intertemporal trade is an important factor governing

and find that closing banks has adverse effects on local sales and nonagricul-

general economic activity. The correlation between economic development and

tural employment.

financial sophistication across time and across countries has often been noted.
See Goldsmith (1969) and Cameron (1972) for examples of such studies.

■

4

A s discussed in Gertler (1988), the literature on financial structure and

economic development has principally focused on variations across countries.

■

5

Small Business Administration (1985), p. 206.

supply process. They note that in developed countries there typically exists a

■

6 Current information is not available on the sources of internal financing

highly organized system of financial intermediation facilitating the flow of funds

to small firms. For historical data, see Small Business Administration (1984).

Gurley and Shaw (1955) emphasize the role of intermediaries in the credit

credit gap is aggravated in times of tight credit,
during which banks ration funds, with larger
firms receiving a disproportionately large share.
This perception of market failure is reflected
in how public-sector development agencies
lower the cost of credit by providing access to
sheltered pools of money (such as public pen­
sion funds), by passing on the favorable tax
treatment of funds (through tax abatement and
public bonds), or by accepting risks greater than
private institutions are willing to bear (such as
the loan guarantee program of the Small Busi­
ness Administration) . 7
While there are no direct measures of the
price and availability of credit for small busi­
nesses across regions, they are likely to vary with
bank structure. 8 Concentrated banking markets
with large banks and high barriers to entry may
be unresponsive to the credit needs of small
businesses and new firms. Lending to new firms
entails higher risks than lending to established
firms, since a large proportion of new firms fail
in the first few years.
Heggestad (1979), Rhoades and Rutz (1982),
Clark (1986), and Liang (1987) argue that banks
in highly concentrated markets trade potential
monopoly profits for lower risk. Alternatively, a
highly competitive bank market, characterized by
large numbers of smaller banks and easy entry,
may result in a greater availability of credit at
lower prices for small businesses. Finally, a prof­
itable banking sector is expected to result in less
credit rationing and a greater supply of credit for
small firms. Even if most start-ups do not rely
directly upon commercial banks for their initial
financing, the expectation of ample credit for
future expansion at low cost potentially affects
the decisions of entrepreneurs to start a firm. 9
An understanding of the impact of bank struc­
ture on firm kx:ation and regional growth is
important because of the significant changes
occurring due to deregulation and interstate
banking. By the end of 1988, all but three states

■

7

See Hill and Shelley (1990, forthcoming).

■

8

This would not be true if banks were perfectly contestable; the actual

permitted some form of interstate acquisition of
their banks, 14,600 offices of banking organiza­
tions existed outside the organizations’ home
state, and more than half of these were permit­
ted to offer all banking services. 10 To the extent
that this results in freer entry and increased
competition among banks, the availability of cap­
ital for small businesses and new firms could
increase. In the Southeast and New England,
however, these developments have increased the
number of extremely large banks, called “superregionals,” at the expense of regional banks.
Increased concentration could reduce the supply
of credit for small businesses.
A recent survey of state bank regulators by Hill
and Thompson (1988) found that advancing eco­
nomic development is an important goal of state
bank regulators. 11 If changes in bank structure do
indeed affect regional growth, however, policy­
makers may be misjudging the costs and benefits
of deregulation and interstate banking. We now
turn to an empirical analysis of this issue.

II. A Model
of Firm Location

To study the effect of bank structure and profita­
bility on local economic activity, we concentrate
on firm openings because they are driven by
current and expected economic conditions, as
opposed to expansions, contractions, and deaths,
which will be greatly affected by the large fixed
costs associated with changing locations. The
model estimated here was originally developed
by Carlton (1979), though we more closely fol­
low Eberts and Stone (1987).12
The number of new establishments in a city is
assumed to depend on the number of potential
entrepreneurs in the city and on the probability
that a given entrepreneur will start a new firm.
The higher the level of economic activity in a
city, the greater the number of potential entre­
preneurs. Also, the higher the expected profita­
bility of new firms, the larger the probability that
they will actually emerge.

number and size distribution of competitors would not affect the price or the

■

availability of credit. Whalen (1988) found that there is evidence that bank per­

banking by King et al. (1989). Earlier surveys include Whitehead (1983a,

formance is systematically related to proxies designed to measure the inten­

1983b, and 1985), and Am el and Keane (1986).

10

These figures come from a recent comprehensive review of interstate

sity of actual and potential competition in rural banking markets in Ohio and
concludes that these n o n -S M S A banking markets are contestable, since

■

potential competition matters, but are not perfectly contestable. Our results

positors’ funds and providing banking (depository) services throughout their

suggest this m ay be true for S M S A s as well.

states.

■

9 Unfortunately, we do not have measures of sources of funds from non­

bank entities, which potentially compete with commercial banks.

■

11

12

It ranked third, just behind ensuring the safety and soundness of de­

For reviews of the firm-location literature, see Bartik (19 8 5 ,19 8 8 ),

Wasylenko (1988), and Wolkoff (1989).

Carlton (1979) modeled this birth process as a
Poisson probabilistic model, since the birth of
new establishments is a discrete event. Let
be
the probability that a potential entrepreneur will
start an establishment in a given city; then let

ln p t= x (b + ejy i - ,..., M ,
where x i is a vector of independent variables
affecting firm profitability, b is a vector of fixed
(1 )

1

coefficients, c; is an error term composed of the
variance of the Poisson process and a random
error, and
is the number of cities in the sam­
ple. Consistent estimates of the mean and var­
iance of
are given by

M

pl

(2)

E (p t) = (.Nt/BP ,),

(3)

Var(P') = (Nj/BPf),

where iV; is the observed number of births and
is the birth potential as proxied by employ­
ment in the standard metropolitan statistical area
(SMSA) . 13 Carlton shows that a consistent and
asymptotically efficient estimate of can be
obtained by weighted least squares, with weights
equal to the standard error of the Poisson process.
The independent variables typically used to
measure expected profitability include wage
rates, tax rates, unionization rates, and energy7
prices. We extend this list by including measures
of bank structure and profitability. As discussed
in the previous section, these measures deter­
mine, at least in part, the price and availability of
credit and thus expected profitability and firm
openings. Measures of bank structure and profit­
ability are employed because direct measures of
the price and the availability of credit are
unavailable. To control for the effects of bank
structure and the availability of credit on firm
births, we include measures of the number and
size distribution of banks as well as a measure of
the financial health of banks.

BP\

b

1980 to 1982 in the USELM data) to existing
employment in the SMSA14 A birth is defined as
an establishment that did not exist in 1980 but
did exist in 1982. Births within this two-year
period are treated as comparable.
We divide the independent variables into two
types. The first are measures of local economic
conditions, and the second are measures of bank
structure and profitability. All data are measured
at the SMSA level unless otherwise noted.
The measures of local economic activity are the
natural logs of the wage rate (
the num ­
ber of establishments
the gross state
product
the personal income
and the population
Also included is the
effective state corporate tax rate (
We
control for population by entering it directly into
our equation rather than using per capita varia­
bles that would impose additional structure.
Bank data are obtained from the Federal
Financial Institutions Examination Council’s
Reports on Condition and Income, known as call
reports, for 1980. (We assume that the lagged
1980 variables on banking are exogenous to firm
births occurring between 1980 and 1982.) Meas­
ures of bank structure and profitability are
created by aggregating data from individual
banks up to the SMSA level. The total amount of
loans and leases
is a measure of the
level of bank intermediation. The average rate of
net income divided by assets,
return
measures the amount of resources available for
future lending and the health of the banking sec­
tor. 16 This variable may also be measuring the
effects of bank structure and the general eco­
nomic health of the region. The empirical analy­
sis will thus explicitly control for these effects.
We employ standard measures of market struc­
ture such as the total number of banks
and branches
the number of bank
employees per bank
and a Herfin­
dahl index of the concentration of deposits
) . 1 7 We also include a measure of bank

(GSP),

(LOANS)

(RETURN),

(BRANCH),
(BANKEMP),

(HQS)

(HERE
■

III. Data

WAGE),
(FIRMS),
(PINC),
(POP).
TAX).15

14

U S E L M stands for the U .S . Establishment and Longitudinal Microdata

file constructed for the Small Business Administration by Dun and Bradstreet.

Data from 259 SMSAs across the country7 are
employed to estimate the model. The dependent
variable (
) is the natural log of the
ratio of new firm births (as reported for the years

BIRTHRATE

■ 15 WAGE and M X
GSP, PINC, and POP are

are 19 77 variables from the Census of Manufactures.
1980 variables from the Census Bureau and the

Department of Commerce. The number of establishments is a 1980 variable
from the U S E L M data.

■ 16

Specifications using income divided by equity capital yield similar

results.
■

■ 13

17

The Herfindahl index is defined as the sum of the square of each

Although policymakers concerned with economic development value

bank’s share of deposits for a given S M S A . While we are interested in the

the employment resulting from new firms, the firm location literature explicitly

effect of concentration in the lending market, we assume that deposits are

models the birth of the firm itself. Using job creation (instead of firm births) as

subject to less geographic dispersion than loans, and thus provide a more

the dependent variable, however, yielded similar results.

accurate indicator of concentration in the local banking sector.

T

A

B

L

(ENTRY

E

Descriptive Statistics

Variable

Mean

Standard
Deviation

BIRTHRATE (firm

0.008

0.003

5.986

1.183

0.403

0.039

13,150

24,713

635.4

1 0 6 0 .2

2,656.4

9,411.5

0.009

0.003

birth/ employment)

WAGE (manufacturing)
TAX (effective tax rate)
FIRMS (number of
establishments)

POP (population,

,

thousands)

LOANS (total loans
and leases, millions)

RETURN
(net income/assets)

HQS (number of banks)
BRANCHES

23

39

132

252

1 9 6 .8

324.6

2,499

1,849

0.456

0.224

0.180

0.129

0.084

0.092

0.058

0 .1 0 0

0.042

0.073

0.028

0.081

-0.014

0.156

6,740.4

12,413.0

(number of branches)

BANKEMP
(employees/bank)

HERF (Herfindahl
concentration index)

SIZE 1 (percent of banks
with $0-$25 million assets)

SIZE 2 (percent of banks
with $25-$50 million assets)

SIZE 3 (percent of banks
with $50-$75 million assets)

SIZE A (percent of banks
with $75-$ 100 million assets)

SIZE 5 (percent of banks
with $100-$250 million assets)

SIZE 6 (percent of banks
with $250-$400 million assets)

ENTRY (percentage change
in the number of banks)

PINC (personal

(SIZE I-SIZE
SIZE

SIZE

SIZE

SIZE

SIZE 5

SIZE

LOANS,

IV. Estimation and Results

income, millions)

GSP (gross state

entry
), the percentage net change in
the number of banks from 1978 to 1980.18
Our last measures of bank structure are a set
of variables
6 ) that control for the
size of banks.
1 is the proportion of banks
in an SMSA with assets less than $25 million,
2 is the proportion of banks with assets
between $25 and $50 million,
3 is the pro­
portion of banks with assets between $50 and
4 is the proportion of banks
$75 million,
with assets between $75 and $100 million,
is the proportion of banks with assets between
$100 and $250 million, and
6 is the propor­
tion of banks with assets of $250 to $400 million.
The proportion of banks with assets greater than
$400 million is the omitted category in our esti­
mations. 19 Summary statistics for these variables
are presented in table 1 .
A pervasive problem with this data set for the
purpose of looking at how banking activity
affects the regional economy is that regions for
which data are collected (SMSAs and states) and
economic regions do not necessarily match. In
addition, for some variables, such as
though the total dollar value of loans is known,
it is not possible to determine where the loans
were made. For example, loans made by an
Ohio bank to firms in Florida and Ohio are
counted in the same way.
With the banking data, there is an additional
measurement problem in that a call report for a
consolidated banking unit may include data for
branches not located in the SMSA In states that
allow branch banking, activity at the branches
may be reported solely in the SMSA headquarters.
Thus, our measures of competition and concen­
tration are potentially subject to errors. The sen­
sitivity of our full sample results to this potential
errors-in-variables problem is tested by running
the model without SMSAs in states that have state­
wide branching, and then again without SMSAs
in states that have limited branching (that is,
only SMSAs in unit banking states).

Full Sample Results

100,680

product, millions)

84,277
Estimates of variations of the above model for
the full sample are presented in table 2. Equa-

NOTE: Changes are measured as log differences.
SOURCE: Authors’ calculations.

■ 18

Note that this measure treats entry and exit symmetrically.

■ 19

Alternative measures of size were also tested. In general, only the

measures of the smaller banks were statistically significant.

■ 1

A

B

L

E

2

Estimation Results

Coefficient

W AGE

(1 )

(3 )

(2 )

-0.6823a
(0.1131)

-0.44263
(0.1023)

-0.50763
(0.1140)

TAX

-1 .8 3 6 8 a
(0.5694)

-1.70323
(0.5442)

-1.51933
(0.5490)

F IR M S

0.28253
(0.0940)

0.3453a
(0.0939)

0.30463
(0.1090)

POP

-0.24123
(0.1015)

-0.l694b
( 0 .1 0 0 2 )

-0.35323
(0.1692)

-0.0393
(0.0870)

-0 . 0 6 0 2
(0.0872)

31.7890a
( 6 .8 2 3 8 )

31.29403
(6.8055)

-0.0693
(0.1294)

-0.0451
(0.1293)

-0.227 l a
(0.0555)

-0.19453
(0.0574)

0.3192a
(0.0942)

0.31913
(0.0938)

-0.19873
(0.0687)

-0.19113
(0.0684)

0.86503
(0.2463)

0.85503
(0.2450)

0.3396
(0.2537)

(0.2525)

0.4889b
(0.2746)

0.4486
(0.2742)

0.4387
(0.2688)

0.4101
(0.2677)

-0.0085
(0.3159)

-0.0432
(0.3146)

-0.0803
(0.2784)

-0 . 0 8 1 6
(0.2770)

0.4314a
(0.1319)

0.42393
( 0 .1 3 1 2 )

LOANS

—

RETURN

—

HQS

—

BRANCHES

—

BANKEM P

—

HERF

—

S IZ E 1

—

S IZ E 2

—

S IZ E 3

—

S IZ E A

—

S IZ E 5

—

S IZ E

—

6

ENTRY

—

P IN C

—

—

0 .3 1 6 8

0 .1 8 3 8

(0.1785)

GSP
CO N STANT
Log likelihood
function

—

—

-4.05023
(0.4267)

-4.6572a
(0.7856)

0.0427b
(0.0239)
-6.3725a
(1.5336)

-95.4467

-46.6358

-44.1093

0.2109

0.4579

0.4683

Mean of the dependent
variable
-4.9267

-4.9267

-4.9267

259

259

R-square

No. of obs.

259

a. Significant at the 95 percent confidence level.
b. Significant at the 90 percent confidence level.
NOTE: Standard errors of the coefficients appear in parentheses.
SOURCE: Authors’ calculations.

tion ( 1 ) is a basic, static model of firm location,
where the probability that a birth will occur
depends on the wages, taxes, number of estab­
lishments, and population. This set of variables
differs somewhat from that employed by Carlton
(1979), who also used the unionization rate and
energy prices in his estimates for selected indus­
tries. Eberts and Stone (1987) found that energy
prices do not matter when the model is esti­
mated with aggregate manufacturing data, and it
is even less likely that energy prices would mat­
ter since we are looking at all industries.
Because we are not concerned about differ­
ences across industries and are interested only in
whether there are statistically significant effects
on aggregate regional economic activity as a
result of bank structure and profitability, energy
prices can safely be omitted. The unionization
rate was omitted due to lack of available data.
We assume that unionization is not systemati­
cally related to the banking variables.
All the coefficients in equation (1) are statisti­
cally significant at the 95 percent confidence
level. As expected, we find that higher wages
and higher effective corporate tax rates reduce
the probability of firm births in an SMSA. Also,
the probability of firm births increases with a
greater number of establishments
and a
lower population. Though the coefficient on
population is somewhat unexpected, this result
suggests that given the similar magnitude and
opposite signs of these two coefficients, perhaps
the number of firms per capita is the appropriate
regressor. We continue entering population as a
separate regressor because this is the most gen­
eral way of including population in the model. 20
Equation (2) estimates the same model, only
now the measures of bank structure and profita­
bility are included. The results strongly support
the view that bank structure and profitability
have a statistically significant effect on firm
births. The addition of the bank structure varia­
bles did not affect the estimates of the basic firm
location variables. The basic firm location coeffi­
cients have roughly the same magnitude and
remain statistically significant at the 90 percent
confidence level or higher.
The measure of the total amount of financial
intermediation
is negative but not sta­
tistically significant. The
variable has a
positive and statistically significant coefficient,

(FIRMS)

(LOANS)

■ 20

RETURN

More restrictive specifications using per capita variables yielded sim­

ilar results.

■T

A

B

L

E

3

Unit and Limited Branching States

Coefficient

WAGE

TAX
FIRMS
POP
LOANS

(1 )

-0.45593
(0.1075)

-0.46103
(0.1340)

-3.04843
(0.6175)

-1.50433
(0.6943)

-0.7901
(0.8031)

0.44373
( 0 .1 1 3 2 )

0.40133
(0.1392)

0.4063a
(0.1654)

-0.43373
(0.1224)

-0.30013
(0.1367)

-0.3458b
(0.2088)

-0 . 1 1 6 2
(0.1352)

-0 . 1 6 1 2
(0.1371)

44.34303
(9.9812)

43.40403
(9.9638)

0.1324
( 0 .2 0 0 0 )

0.2018
(0.2031)

-0.27783
(0.0735)

-0.26473
(0.0736)

0.54933
(0.1412)

0.58173
(0.1419)

-0.21633
(0.0863)

-0.21043
( 0 .0 8 6 1 )

1.24283
(0.3579)

1.22873
(0.3569)

0.70643
(0.3454)

0.6672b
(0.3449)

0.86703
(0.3380)

0.86773
(0.3370)

0.94563
( 0 .3 2 8 1 )

0.94593
(0.3270)

0.7980b
(0.4074)

0.7962b
(0.4068)

—

—

RETURN

—

HQS
—

BRANCHES

—

—

BANKEMP

—

—

HERF

—

—

SIZE 1

—

—

SIZE 2

—

—

SIZE 3
SIZE A
SIZE 5
SIZE

—

—
—

—
—

—
6

ENTRY

—

—
—

—

PINC
GSP
CONSTANT
Log likelihood
function

(3 )

(2 )

-0.75583
(0.1137)

(0.4510)

0.1004
(0.4527)

0.1757
(0.2295)

0.1948
(0.2311)

0 .0 3 6 0

—

—

—

—

—

—

—

—

-37568a
(0.4690)

-5.16423
(1.0234)

0.0108
(0.2472)

-5.92763
(1.9894)

-19.2143

-17.4198

0.3675

0.5569

0.5652

Mean of the dependent
variable
-4.9699

-4.9699

-4.9699

No. of obs.

190

HERF

HQS
BRANCHES, BANKEMP,

HERF),

SIZE

ENTRY

In equation (3), two more measures of
regional activity
and
are added to
the model to see whether the bank structure and
profitability effects are merely reflecting regional
economic conditions. O f the added regressors,
only
is statistically significant and only at
the 90 percent confidence level. The bankrelated coefficient estimates do not change
appreciably with the addition of these regressors.
In particular,
retains its positive and sta­
tistically significant value even when we control
as much as possible for local economic condi­
tions, suggesting that this variable is doing more
than just reflecting a robust local economy. 21

(PINC

GSP)

GSP

RETURN

0 .0 6 6 l b
(0.0372)

-53.0456

R-square

suggesting that (controlling for structure) a prof­
itable banking sector is associated with a higher
probability of firm births. Profitable banks could
have more opportunities for providing interme­
diation services and engage in less credit ration­
ing, suggesting a positive relationship with firm
births. Alternatively, high profits in the banking
sector could merely be indicating profitable
market conditions for other industries as well.
(We will therefore control for regional economic
activity in equation [3 ].)
The number of banks (
) is not statistically
significant, but
and
are, suggesting that the greater the
number of branches and the more concentrated
the banking market (at least as measured by
the lower the probability of firm births.
More branches could reflect more of a retail
orientation of the banks. Also, the more
employees per bank, the higher the probability
of firm births.
The statistical significance and the magnitude
of
1 suggest that smaller banks (those with
less than $ 5 million in assets) are more involved
in firm births than larger banks: the higher the
proportion of small banks, the higher the proba­
bility of firm births. Finally, the coefficient on
is positive and statistically significant,
implying that the more contestable the banking
market (as indicated by a larger value for entry),
the higher the probability of firm births.

190

a. Significant at the 95 percent confidence level.
b. Significant at the 90 percent confidence level.
NOTE: Standard errors of the coefficients appear in parentheses.
SOURCE: Authors’ calculations.

190

Partial Sample Results

As previously discussed, the banking data are
potentially subject to significant measurement

■ 21

Specifications that included the complete set of economic variables

but entered the various bank structure variables separately (instead of the full
set) yielded similar results. A n exception was our measure of concentration,

HERF,

which was statistically significant only when the

included as well.

SIZE

variables were

Unit Banking States

Coefficient

WAGE

TAX
FIRMS
POP
LOANS

(1)
-0.88473
(0.1994)

(2 )
-0.54943
(0.1951)

-0.34663
(0.2724)

-1.6874
(1.0677)

-0 . 2 8 1 6
(0.9922)

-0.9859
(1.7693)

0.51933
(0.1778)

0.3525
(0.2747)

0.5890b
(0.3543)

0.50293
(0.1885)

0.0184
(0.2915)

0.2364
(0.3563)

0.2934
(0.3359)

0.1598
( 0 .3 6 0 6 )

—
—

RETURN

—

HQS
—

BRANCHES

—

—

BANKEMP

—

—

HERF

—

—

SIZE 1

—

—

SIZE2

—

—

SIZE 3

—

—

SIZE A

—
—

SIZE 5

—

—

SIZE

6

—

—

ENTRY

—

—

PINC
GSP
CONSTANT
Log likelihood
function
R-Square

3 6 .6 8 0 0 b
(22.1410)

43.88105
(23.4160)

-0.4136
(0.6288)

-0.1035
(0.6956)

-0.3807b
( 0 .2 1 3 6 )

-0.4427b
(0.2367)

0.0810b
(0.4796)

0.1937
(0.5147)

-0.1543
(0.2107)

-0.0565
(0.2396)

2.71953
( 1 .3 6 6 2 )

2.5134b
(1.4066)

1.9879
(1.2694)

1.7754
(1.3086)

2.34523
(0.9367)

2 .2 6 0 1 3
(0.9560)

0.7998
(1.1518)

0.7543
(1.1646)

2 .0 3 0 0 b
(1.0934)

1.7276
(1.1633)

(1.0377)

1.1365
(1.0511)

1.58433
( 0 .6 2 3 8 )

1.36823
(0.6601)

1 .1 3 8 6

—

—

—

—

—

—

—

—

-4.2875a
(0.6673)
-13.6582

(3)

-0.4996
(0.4562)

-10.08503
(2.8175)

-5.8005
(4.9151)

1 2 .8 3 2 6

13.7363

0.4021

0.7603

0.7677

-4.7994

-4.7993

58

In table 3, we reestimate the model omitting
SMSAs in states with statewide branching. 22
Although the magnitude of the coefficients tends
to be larger, there is no qualitative change in the
results in equation (1). In equation (2), the
results are again quite similar to those in table 1 ,
except that more of the size variables are statisti­
cally significant, but ENTRY is no longer statisti­
cally significant. These differences carry over to
the results for equation (3). Thus, omitting the
SMSAs in the statewide branching states has little
effect on our results.
Though we remove most of the measurement
problems in the banking variables by omitting
the SMSAs in the statewide branching states, the
same problems hold to a much lesser degree for
the SMSAs in the states with limited branching,
which generally allow branches to operate only
in contiguous counties.
In table 4, the model is reestimated with only
the SMSAs in the unit banking states. 23 These sta­
tistical results are not as strong, but our sample
has fallen from 259 in table 2 , to 190 in table 3,
to only 58 in table 4. O f the bank structure and
profitability variables (reported in equation [2 ]),
1,
3, and
5
all remain statistically significant.
and
lose their statistical significance, but
once again becomes statistically signifi­
cant. When we add
and
in equation
(3),
is no longer statistically significant,
but the number of establishments
is. O f
the banking variables,

RETURN, BRANCHES, SIZE SIZE
SIZE
BANKEMP
HERE
ENTRY
PINC
GSP
WAGE
(FIRMS)
RETURNS, BRANCHES,

-0.0231
(0.0741)

Mean of the dependent
variable
-4.7987
No. of obs.

error. In states that permit statewide branching, a
call report for a consolidated banking unit may
include data for branches not located in the
SMSA. While the standard errors-in-variables
problem in econometrics results in a bias toward
zero in the estimated coefficients, we wanted to
test whether our results were due to measure­
ment error. We therefore estimate the model
without SMSAs in states that have statewide
branch banking, and then again without SMSAs
in states that allow statewide or limited branch­
ing. These results are reported in tables 3 and 4.

■ 22

Thus, we omit S M S A s in the following states: Alaska, Arizona, Cali­

fornia, Connecticut, Delaware, Florida, Hawaii, Idaho, Maine, Maryland, N e v­
ada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode
Island, South Carolina, South Dakota, Utah, Vermont, Virginia, and

58

a. Significant at the 95 percent confidence level.
b. Significant at the 90 percent confidence level.
NOTE: Standard errors of the coefficients appear in parentheses.

58

Washington.

■ 23

Thus, only S M S A s in the following states are included in this sample:

Colorado, Illinois, Kansas, Missouri, Montana, Nebraska, North Dakota, Okla­
homa, Texas, West Virginia, and Wyoming.

SIZE SIZE

ENTRY

1,
3, and
all remain statistically
significant. In the basic firm-location model
(equation [1 ]), the coefficients retain the same
signs and magnitudes, though the state corporate
tax rate (
) is no longer statistically signifi­
cant. When we add the bank variables, only
retains its statistical significance.
Clearly, the model does not perform as well
with this sample. Even the coefficients in the
basic firm location model lose their statistical
significance (except for
Whether this is
due to the small sample size or to possibly pecu­
liar characteristics of the included SMSAs is
unclear. 24 Yet even with this sample, bank struc­
ture (as measured by
1,
3, and
retains a statistically
significant effect on firm births.
In summary, the error-in-variables problem
discussed in the previous section does not
appear to severely bias our results. Estimates of
the model using the full sample are very similar
to the estimates obtained using only SMSAs in
states with unit or limited branching. When the
model is estimated with just the SMSAs in unit
branch banking states, the estimates change
much more, but the profitability of the banking
sector, the number of branches, the proportion
of small banks, and entry all have a statistically
significant effect on the probability of firm births.
Our measure of concentration (
retains
the same sign and magnitude but is not statisti­
cally significant. Banking structure and the avail­
ability of credit appear to have measurable
effects on firm births.

TAX

WAGE

FIRMS).

SIZE SIZE

RETURN, BRANCHES,
ENTRY)

HERE)

V. Conclusion

This study presents evidence on the effects of
bank structure and profitability on the births of
new firms. The attraction of new firms is an
important goal of local economic development
policies, which often provide public-sector
financial incentives. Private-sector financial struc­
ture, however, potentially influences firm loca­
tion through the price and availability of credit
from commercial banks.
The empirical analysis examines the relation­
ship between banking activity and regional
development from 1980 through 1982. Using
bank-level data, we construct measures of lend-

■

24

The remaining S M S A s in the sample tend to be in states with large

energy and agricultural sectors.

ing, profitability, concentration, size, and entry in
the banking sectors of 259 SMSAs. Measures of
bank structure are included in a standard model
of firm location in order to test for independent
effects of banking on regional growth as meas­
ured by firm births.
As with other firm location studies, we find
firm births to be positively associated with low
wages, low taxes, and a large number of existing
firms. Our analysis, however, also shows that the
private banking sector appears to be systemati­
cally related to the probability of firm births.
Higher rates of firm openings are associated with
a healthy and competitive banking sector. Specif­
ically, firm births are associated with higher rates
of bank profits, higher numbers of bank employ­
ees, lower levels of concentration, higher pro­
portions of small banks, and higher rates of entry
of new banks into the SMSA These results are
robust across several specifications and samples
and support the position that bank structure and
profitability are significant factors in facilitating
economic development.

m

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m

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