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FINANCIAL INDUSTRY
December 1 9 9 4

STUDIE S
Federal Reserve Bank of Dallas

When A r e Failing Banks Closed?
Rebel A. Cole
Economist
Board of Governors
of the Federal Reserve System
Jeffery W. Gunther
Senior Economist and Policy

Advisor

Have Small Banks
Been Caught Off-Balance?
Robert R. Moore
Senior
Economist
Karen Couch
Financial Industry

Analyst

This publication was digitized and made available by the Federal Reserve Bank of Dallas' Historical Library (FedHistory@dal.frb.org)

Financial Industry Studies
Federal Reserve Bank of Dallas
December 1994

President and Chief Executive Officer
Robert D. McTeer, Jr.
First Vice President and Chief Operating Officer
Tony J. Salvaggio
Senior Vice President
Robert D. Hankins
Vice President
Genie D. Short

Industry Studies is published by the Federal Reserve Bank
of Dallas. The views expressed are those of the authors and
do not necessarily reflect the positions of the Federal Reserve Bank
of Dallas or the Federal Reserve System.

Financial

Subscriptions are available free of charge. Please send requests for
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Contents
Page 1
The financial turmoil of
latter
When Are Failing 1990s has led policymakersthe devote1980s and early
to
increased attenBanks Closed? tion to the procedures used in resolving troubled
Rebel A. Cole and
Jeffery W. Gunther

banks. Of particular concern has been the potential
influence of bank-specific characteristics, such as bank
size, on the resolution process. In this article, Rebel
Cole and Jeffery Gunther provide new evidence that
differences in regulatory treatment have enhanced the
ability of large banks to avoid failure. However, they
find little evidence to suggest that regulatory factors
also have worked to delay the resolution of large
banks that eventually fail. More generally, their results
indicate that only a limited number of the variables
typically used to explain bank failure actually are
useful in explaining the survival time of failing banks.

Page 13

Have Small Banks
Been Caught
Off-Balance?
Robert R. Moore and
Karen Couch

While traditional deposit-based lending remains a
core business, the banking industry increasingly has
turned to off-balance-sheet activities for revenue and
growth. This article looks at the effects of off-balancesheet activities on competition within the banking
industry. Robert Moore and Karen Couch find that
under an expanded definition of the banking market
that includes off-balance-sheet activities, small banks
have had an even greater loss of market share than
suggested by traditional analyses of balance-sheet
assets. In addition, reductions in the market share of
small banks in the Eleventh District have been much
smaller than nationwide.

When Are Failing
Banks Closed?
Rebel A. Cole
Economist
Board of Governors
of the Federal Reserve System
and
Jeffery W. Gunther
Senior Economist and Policy Advisor
Financial Industry Studies Department
Federal Reserve Bank of Dallas

ecent financial-sector difficulties have
increased the legislative and regulatory
attention given to the resolution of bank
failures. On the legislative front, concerns
over perceived shortcomings in the resolution process have culminated in the passage of the Financial Institutions Reform,
Recovery, and Enforcement Act of 1989
(FIRREA) and the Federal Deposit Insurance Corporation Improvement Act of 1991
(FDICIA), both of which contain provisions
to improve the regulatory treatment of
troubled financial institutions.
Many of the shortcomings perceived in
the resolution process are related to the
promptness with which failing banks are
closed, and many factors are thought to contribute to unnecessary and potentially expensive resolution delays. In response to
these concerns, much of FDICIA is aimed at
speeding up the resolution process in general and reducing the potential effects of
case-specific attributes, such as bank size, on
the regulatory treatment of troubled banks.
Despite these far-reaching legislative
changes, little evidence exists regarding the
degree to which different factors actually
have influenced the survival time of failing
banks. While numerous studies have attempted to identify the causes of bank
failures,1 few have analyzed the potential

determinants of the timing of bank failures.
Knowledge of the factors determining
the timing of bank failures is valuable for
several reasons. From a policy perspective,
knowledge of the degree to which regulatory factors have influenced how long
failing banks survive would be useful in
designing new resolution policies and procedures. In addition, knowledge of the
financial characteristics that influence the
survival time of failing banks would benefit
bank regulators, bank investors, and other
parties interested in assessing the financial
condition of individual banks. In particular,
such knowledge could facilitate a type of
regulatory triage. Based on expected survival times, bank regulators could seek to
rehabilitate those financially impaired institutions that possess sufficient lead times
for corrective measures and enforcement
actions to take effect. Similarly, failing institutions with financial characteristics associated with short expected survival times
could be targeted for prompt closure.
This article summarizes the findings of
a recent study in which we provide new
evidence on the factors influencing the
survival time of failing banks.2 Specifically,
we use data from the December 1985 call
report to predict bank failures and the
survival time of failing banks during the
period from the first quarter of 1986 through
the second quarter of 1992. This period
covers most of the recent banking downturn. Survival time is defined as the number
of quarters, starting with the first quarter of
1986, in which a bank operates before it is
resolved by regulators. Our results suggest
that only a limited number of the variables
typically used to explain bank failure also
are useful in explaining the survival time of
failing banks. And, interestingly, we find
little evidence to suggest that a failing bank's
size has been an important determinant of
its survival time.

See Demirguc-Kunt (1989b) for a literature review.

See Cole and Gunther (1995, 1993).

Potential Determinants of Bank Survival Time
For the purposes of this analysis, we
group the factors that might influence bank
survival time under four main headings:
financial factors, managerial factors, regulatory factors, and economic factors. Because variables in each category have been
shown to influence the likelihood of bank
failure, it is reasonable to suspect that the
same variables also might be important
determinants of the survival time of failing
banks. Although many of the variables we
employ are related to more than one heading, we attempt to categorize each variable
under its most relevant heading.
Financial Factors. Broadly speaking, we
expect that measures of a bank's financial
condition should be useful in explaining
both the likelihood of bank failure and the
survival time of failing banks. All else being
equal, financial weakness should increase
the likelihood of bank failure. Similarly, a
severely impaired bank would be expected
to fail sooner than a moderately troubled
bank.
The financial factors we analyze include
proxies for capital adequacy, asset quality,
earnings, and liquidity. These factors are
defined in Table 1, which also shows the
expected influence of each factor on the
likelihood and timing of bank failure. The
entry "Increase" indicates that high values
of a variable are expected to be associated with a high likelihood of survival or
a long survival time, whereas the entry
"Decrease" indicates that high values of a
variable are expected to be associated with
a low likelihood of survival or a short survival time. When the nature of a variable's
expected effect on bank survival or survival time is ambiguous, as in Table 2, a
question mark (?) appears.
We measure capital adequacy by the
ratio of equity capital and loan loss reserves
to gross assets. Because capital serves as a
buffer against losses, a high value for the
ratio of capital to assets is expected to increase both the likelihood of survival and
the expected survival time of failing banks.
2

Asset quality difficulties are measured by
the ratio of loans past due ninety days or
more, nonaccrual loans, and other real
estate owned to gross assets. Banks typically must provide for losses on a significant portion of their troubled assets, which
reduces net earnings and, ultimately, capital. Therefore, a high value for the troubled
asset ratio is expected to reduce both the
probability of survival and the expected
survival time of failing banks.
We measure the effects of earnings using
the return on bank assets. Strong earnings
enable a bank to boost capital and signal
to regulators that a bank is viable. As a
result, the ratio of net income to average net
assets is expected to be positively related
to both the probability of survival and the
expected survival time of failing banks.
The ratios of investment securities to
gross assets and large certificates of deposit
to gross assets serve as indicators of bank
liquidity. Liquid assets enable a bank to
respond quickly to unexpected demands
for cash, so that a high value for the ratio
of investment securities to gross assets
should increase both the probability of
survival and the expected survival time of
failing banks. For troubled banks, large
certificates of deposit, portions of which
are not insured explicitly, are a less stable
and potentially more expensive funding
source than retail deposits. As a result, a
high value for the ratio of large certificates
of deposit to gross assets is expected to
reduce both the likelihood of survival and
the expected survival time of failing banks.
Because bank management can, in many
instances, control the level of bank liquidity
fairly closely, the liquidity variables discussed here in the context of financial
factors also can be viewed as measures of
managerial influence. In particular, low
liquidity often is associated with aggressive
strategies and a high-risk profile. However,
this alternative interpretation does not
change the expected relationships between
the liquidity variables and both bank survival and bank survival time.
Managerial Factors. In addition, we expect

Table
2
Managerial Factors Associated with Bank Survival and Survival Time
Expected effect*
Variable
Capital

Definition
Ratio of equity capital and loan loss
reserves to gross assets

Survival

Survival time

Increase

Troubled assets

Ratio of loans past due 90 days
or more, nonaccrual loans, and other
real estate owned to gross assets

Decrease

Net income

Ratio of net income to average net assets

Increase

Securities

Ratio of investment securities
to gross assets

Increase

Large
CDs

Ratio of large certificates of deposit (CDs)
($100,000 and greater) to gross assets

Decrease

* "Increase" indicates that high values of the variable are expected to be associated with a high likelihood of survival or a long
survival time. "Decrease" indicates that high values of the variable are expected to be associated with a low likelihood of
survival or a short survival time.
DATA SOURCE: FFIEC Report of Condition and Income.

that risk-taking might play an important
role in determining both whether an individual bank fails and the timing of its failure.
In a theoretical article about financial deregulation and bank risk-taking, Marcus
(1984) predicts a tendency for individual
banks to gravitate toward either a high-risk
or low-risk posture, depending on the
magnitude of their charter value.3 Such a
process could give rise to a split among
banks between high-risk and low-risk institutions, so that a period of adverse economic conditions would result in an industry
shakeout, during which the high-risk institutions fail while the low-risk institutions
survive. As a result, measures of a bank's
risk posture should be useful in explaining
the likelihood of its failure. And, similarly,
a high-risk posture might work to shorten
the life of a failing bank.
As proxies for managerial decisionmaking, we include in the analysis information on seven categories of bank loans,
which are identified in Table 2. Insofar as
a high proportion of assets in any of these
lending categories reflects high credit risk,

we expect the loan portfolio variables to
reduce the probability of survival.
However, predicting the relationship of
the lending variables with bank survival
time is more complicated. While the credit
risk associated with bank lending could
shorten the expected life of a failing bank,
certain peculiarities of bank lending and
the institutional arrangements surrounding
it could work to extend, rather than reduce,
a failing bank's expected survival time.
The role of banks in monitoring borrowers implies that banks possess information
about the financial condition of their borrowers superior to that available to other
parties (Diamond 1984). In this regard, the

An article by Ritchken, Thomson, DeGennaro, and
Li (1993) extends the analysis of Marcus (1984) to
allow for portfolio adjustments between examination
dates. While these authors find that bank portfolio decisions may not b e extreme, their results also suggest
that the flexibility to adjust asset allocations increases
the range of capital ratios for which the optimal portfolio decision places a bank's charter at risk.

Table 2
Managerial Factors Associated with Bank Survival and Survival Time
Expected effect*
Variable

Survival

Definition

Survival time

C&l loans

Ratio of commercial and industrial (C&l)
loans to gross assets

Decrease

Agricultural
loans

Ratio of agricultural production loans
to gross assets

Decrease

Commercial
real estate loans

Ratio of construction loans and loans
secured by multifamily, nonresidential,
or farm real estate to gross assets

Decrease

Residential
real estate loans

Ratio of loans secured by one- to
four-family residential properties
to gross assets

Decrease

Consumer loans

Ratio of consumer loans to gross assets

Decrease

Other loans

Ratio of all other loans to gross assets

Decrease

Insider loans

Ratio of insider loans to gross assets

Decrease

Salary expense

Ratio of salaries and employee benefits
to average net assets

Decrease

Premises
expense

Ratio of expenses for premises and
fixed assets to average net assets

Decrease

Other noninterest
expense

Ratio of all other noninterest expenses
to average net assets

Decrease

?
?
?

* "Increase" indicates that high values of the variable are expected to be associated with a high likelihood of survival or a long
survival time. "Decrease" indicates that high values of the variable are expected to be associated with a low likelihood of
survival or a short survival time. When the nature of a variable's expected effect on bank survival or survival time is ambiguous,
a question mark (?) appears.
DATA SOURCE: FFIEC Report of Condition and Income.

importance of on-site bank examinations to
the regulatory process can be viewed as
deriving from regulators' efforts to mitigate
their informational disadvantage relative to
banks. In the event of an impending default
by its borrowers, a bank could exploit the
information asymmetries associated with its
lending activities by concealing knowledge
of the borrowers' true financial condition
from regulators. The success of this strategy
would be enhanced to the extent that resource constraints or other institutional
features hampered the efforts of regulators
to obtain accurate information about the
4

market value of the bank's loans. If such a
strategy were successful, a high proportion
of assets invested in a certain category of
loans could extend a troubled bank's expected survival time, reflecting regulatory
costs in the resolution process. Given the
potential importance of these regulatory
factors, the expected effect of the lending
variables on bank survival time is ambiguous.
An additional complication arises in the
interpretation of the effects of the loan
variables on survival time. The estimated
effects of the loan variables may reflect
differences in the timing of economic down-

turns across industries. For example, a high
proportion of assets in agricultural production loans could reduce expected survival
time if the effects of the downturn in the
agricultural sector were most pronounced
in the early part of the sample period. Such
considerations suggest that, in this particular regard, the estimation results should be
interpreted in the context of the events
peculiar to our sample period.
Besides risk-taking, an additional potential managerial influence involves efficiency.
A low cost structure would be expected to
increase the likelihood of bank survival.
Similarly, efficient banks would be expected
to survive longer than inefficient ones.
To capture the effects of these managerial
factors on the likelihood and timing of bank
failure, we include measures of banks' cost
structures. For this purpose, three measures
of noninterest expense are used, each expressed relative to average net assets. Because excessive overhead costs can reduce
a bank's competitive position, we expect
high levels of salaries and employee benefits, expenses of premises and fixed assets,
and other noninterest expenses to reduce
both the likelihood of survival and the
expected survival time of failing banks.
Regulatory Factors. While the potential influence of regulatory factors on the relationship between the lending variables and
bank survival time is somewhat subtle, other,
more clearly recognizable avenues of potential regulatory influence also exist. In
this section, we single out certain readily
identifiable aspects of bank structure that
may be more directly related to the regulatory process of resolving banking difficulties.
Many recent academic contributions to
the explanation of bank failures have analyzed the considerations regulators face
when deciding whether to close failing
depository institutions (Gajewski 1988; Cole
1994, 1990; Demirguc-Kunt 1991, 1989a;
and Thomson 1992). This focus on regulatory closure rules was motivated largely by
the ideas of Kane (1989, 1986) and others
who drew attention to the sharp distinction
that exists between the economic solvency

of a financial institution and its survival.
The closure or, more generally, resolution,
of a troubled financial institution is a regulatory action that may depend on factors
other than the institution's economic net
worth. As a result, a financial institution
that has failed in the economic sense can
avoid resolution if its regulators, for whatever reason, choose not to act.
While many different regulatory factors
have been identified as potential contributors to delays in the closure of failing banks,
the consideration that seems to have drawn
the most attention has to do with the potential influence of bank size on the resolution process. If regulators perceive that the
failure of a large bank might greatly disturb
the financial system, they might attempt to
insulate that bank from the impact of its
financial difficulties. Under what has come
to be known as the policy of "too big to
fail," the resolution process has been perceived as favoring large banks.
The favorable treatment extended under
too big to fail could include such subsidies
as special access to the discount window,
the protection of creditors not explicitly
covered by deposit insurance, or the lenient
regulatory valuation of bank assets. Under
the latter type of subsidy, the value of the
assets of a financially impaired bank would
be overstated, so that the bank's capital
would remain above levels generally associated with closure. Such actions would be
expected to enhance the survivability of
large banks. In addition, they also could
extend the survival time of large banks that
eventually fail. Hetzel (1991), for example,
argues that the policy of too big to fail has
resulted from the institutional need of regulators to control, and often delay, bank
closures.
The potential effects of bank structure
on the regulatory treatment of financially
impaired banks are captured by variables
measuring bank size and holding company
affiliation, as shown in Table 3. To the
extent that the policy of too big to fail has
enhanced the survivability of large banks,
we expect that, all else being equal, bank
5

Table 3
Regulatory Factors Associated with Bank Survival and Survival Time
Expected effect*
Variable

Definition

Survival

Survival time

Asset size

Logarithm of gross assets

Increase

Holding company
affiliation

Indicates that a bank is a subsidiary
of a bank holding company

Increase

size, as measured by the logarithm of gross
assets, should be positively related to the
likelihood of survival. We should be careful
to note, however, that other factors besides
differences in regulatory treatment also
might work to increase the survivability of
large banks. For example, large banks tend
to possess more flexibility in financial markets than small banks and also are better
able to diversify credit risk. These factors
also might contribute to a positive relationship between bank size and bank survival.
In addition to enhancing survivability,
the complications and costs associated with
the regulatory resolution of large failing
banks also could result in resolution delays.
To the extent that the resolution of large
failing banks has been delayed, our measure
of bank size should be positively related to
the survival time of failing banks. However,
we should again be careful to note that
other factors besides differences in regulatory treatment also might work to extend
the life of large failing banks. For example,
among large banks that fail, relative flexibility in the short-term funding market may
work to extend their survival time.
Any complications and costs associated
with the resolution of a bank belonging to
a holding company also could work to
increase both the likelihood of survival and
expected survival time. To the extent that
significant regulatory costs are associated
with the resolution of a subsidiary bank,
6

we expect our variable measuring holding
company affiliation to increase both the
probability of survival and expected survival time. However, as was the case with
bank size, factors other than differences in
regulatory treatment also could work to
enhance the survivability and survival time
of subsidiary banks. For example, holding
company affiliation might enhance the financial resources available to troubled banks.
Economic Factors. The economic environment also might exert an important influence on bank survival and bank survival
time. An economic upturn would be expected to increase the likelihood of bank
survival, primarily through associated increases in asset quality. Similarly, all else
being equal, a positive economic shock
would be expected to increase the life of
a financially impaired bank.
We include in our analysis two additional
variables to control for the effects of economic conditions (Table4). During the recent
episode of banking difficulties, states with
oil-dependent economies ranked among
the highest in terms of the bank failure rate.
To control for the lingering effects of the
oil-price shock that occurred in 1986, the
predicted growth in state nonagricultural
employment resulting from a $5 per barrel
oil-price decline, as calculated by Brown
and Hill (1988), is included in the model.
This variable tends to take on large, negative values for states with prominent energy

Table
Managerial

2
Factors Associated with Bank Survival and Survival Time
Expected effect*

Variable

Survival

Definition

Oil-price
sensitivity

Growth in state nonagricultural
employment resulting from a $5
reduction in oil prices

Rural location

Increase

Survival time
?

Indicates that a bank is not located
in a metropolitan statistical area

* "Increase" indicates that high values of the variable are associated with a high likelihood of survival or a long survival time.
When the nature of a variable's expected effect on bank survival or survival time is ambiguous, a question mark (?) appears.
DATA SOURCES: Board of Governors of the Federal Reserve System, Bank Structure Data Base; Brown and Hill (1988).

production sectors, such as Texas. While
we expect that relatively high values of the
oil-price variable should be associated with
a relatively high likelihood of bank survival,
the variable's relationship with survival
time is unclear. To the extent that adverse
economic conditions shorten a failing bank's
expected survival time, banks in regions
hurt by the oil-price shock might be expected to fail relatively early. However, for
regions where the economic impact of the
shock was the most severe, the associated
large number of failing banks may have
overwhelmed the resources available to
regulators, resulting in closure delays.
In addition to the oil-price variable, an
indicator variable for banks in rural counties also is included in the model to help
control for differences in economic conditions. Because many rural counties were
relatively unaffected by the real estate boom
and bust that occurred during the sample
period, and because rural banks may enjoy
a degree of monopoly power in their relatively limited markets, banks in rural areas
may have had a higher probability of survival than urban banks. Problems in the
farm sector during the mid-1980s, however,
may have worked in the opposite direction
to lower the probability of survival for rural
banks. Similar considerations apply to the
effect of a rural location on bank survival
time. As a result, the expected impact of a

rural location on both bank survival and
bank survival time is ambiguous.

Data and Methodology
Most of the data in our study come from
statements filed by FDIC-insured commercial banks in the quarterly Report of Condition and Income ("call report"). We use
data from the December 1985 call report to
predict bank failures and the survival times
of failing banks during the period from the
first quarter of 1986 through the second
quarter of 1992. This period covers most
of the recent banking downturn.
We use FDIC press releases to identify
bank failures, including failures resolved by
open bank assistance. For multibank holding companies, we include only the lead
(largest) bank in our sample. This sample
restriction allows us to avoid the unwieldy
task of attempting to model bank failures
precipitated by the insolvency of a multibank holding company's lead bank. We
also exclude from the sample banks established during 1985 because measurement
of the earnings anci expense variables used
in the analysis requires that each bank
operated for that entire year. The resulting
sample consists of 10,843 banks, of which
811, or 7.5 percent, failed during the sample
period.
The findings we present in the next
7

section are based on the statistical results
produced by a split-population survivaltime model (Cole and Gunther 1995, 1993).4
Because the split-population model allows
the determinants of failure to differ from
the determinants of survival time, it facilitates inferences about the separate effects of
a given variable on failure and the timing
of failure.5

Empirical Findings
Table 5 presents both the expected and
estimated effect of each variable on both
the likelihood of survival for all banks and
the expected survival time of failing banks.
Under the column for the estimated effect,
the entry "Increase" indicates that considerable statistical evidence exists to suggest
that high values of a variable are associated
with a high likelihood of survival or a long
survival time. Conversely, the entry "Decrease" indicates that statistical evidence
suggests that high values of a variable are
associated with a low likelihood of survival
or a short survival time. When the evidence
regarding the nature of a variable's effect
on bank survival or survival time is relatively scant, a question mark (?) appears.
Likelihood of Survival. The results explaining bank survival contain few surprises

Schmidt and Witte (1989, 1984) were among the
first researchers to apply the split-population survivaltime model to economic problems. Subsequently,
Hunter, Verbnigge, and Whidbee (1994) have applied
the split-population survival-time model to the failure
of de novo thrifts, and Dahl and Spivey (1994) have
used the model to examine recoveries of undercapitalized banks. For a detailed discussion of survivaltime models, see Lancaster (1990).
4

Lane, Looney, and Wansley (1986) and Whalen
(1991) are the only published empirical studies of
which we are aware that explicitly model bank survival time. However, both of these studies use the
Cox proportional hazards model, which assumes implicitly that all banks eventually fail. As a result, these
studies cannot identify any differences that may exist
between the determinants of bank failure and the factors influencing the survival time of failing banks.
5

and largely confirm the results of previous
bank failure studies. As shown in Table 5,
the estimated effect corresponds to the
expected effect in fourteen cases, and an
additional variable for which the expected
effect is ambiguous—location in a rural
county—is estimated to increase the likelihood of survival. Our estimation results
indicate that surviving banks have been
characterized by high capital, low troubled
assets, high net income, high securities,
low large certificates of deposit, low lending levels outside residential real estate,
low premises expense, high asset size, high
insulation from negative oil-price shocks,
and a rural location.
The positive relationship between asset
size and bank survival indicates that, after
controlling for the potential influences of
the other explanatory variables, large banks
have been less likely to fail than smaller
ones. This finding is consistent with the
existence of the regulatory policy of too
big to fail. However, other factors potentially associated with size, such as financial
flexibility and loan diversification, also
would be expected to support the estimated size-survival relationship.
The estimated positive relationship between bank survival and the variable measuring the economic impact of a reduction
in oil prices indicates that the energyinduced declines in regional economic
activity that occurred during our sample
period reduced the likelihood of bank survival. This result underscores the pernicious
and pervasive effects of declining oil prices
on the financial health of banks located in
energy-producing areas.
Only four variables do not possess a
statistically significant relationship with
bank survival—residential real estate loans,
salary expense, other noninterest expense,
and holding company affiliation. The lack
of statistical significance for residential real
estate loans is consistent with the view that
the boom-to-bust lending pattern evident
in the commercial real estate sector did not
carry over to the residential sector. It should
be noted that, while these four variables

Table
3
R e g u l a t o r y Factors Associated with Bank Survival and Survival Time
Survival

Survival time

Variable

Expected*

Estimated*

Expected*

Estimated*

Capital

Increase

Troubled assets

Decrease

Net income

Increase

Securities

Increase

Large CDs

Decrease

C&l loans

Decrease

Agricultural loans

Decrease

Decrease
?

Commercial real estate loans

Decrease

Residential real estate loans

Decrease

Consumer loans

Decrease

Other loans

Decrease

Salary expense

Decrease

Premises expense

Decrease

Other noninterest expense

Decrease

Asset size

Increase

Holding company affiliation

Increase

Oil-price sensitivity

Increase

Rural location

Increase

Insider loans

* "Increase" indicates that high values of the variable are associated with a high likelihood of survival or a long survival time.
"Decrease" indicates that high values of the variable are associated with a low likelihood of survival or a short survival time.
When the nature of a variable's effect on bank survival or survival time is ambiguous, a question mark (?) appears.
DATA SOURCES: FFIEC Report of Condition and Income; Board of Governors of the Federal Reserve System, Bank Structure
Data Base; Brown and Hill (1988).

are not found to have the expected effect
on bank survival, in no case does the estimated effect oppose the expected effect.
Expected Survival Time. Our interest
here focuses on new evidence regarding
the relationship between the explanatory
variables and the survival time of failing
banks. As shown in Table 5, a statistically
significant relationship is found for only
eight of the nineteen variables. For the
other eleven variables, no firm evidence
exists to support a relationship with bank
survival time. In contrast, fifteen of the
nineteen variables we entertain are found

to be useful in explaining bank survival.
Based on these results, it appears that only
a limited number of the variables typically
relied on to explain bank failure actually
are useful in explaining the survival time
of failing banks.
Among the ten variables for which the
expected relationship with bank survival
time is not ambiguous, evidence supporting
the expected relationship is found in only
four cases. The four variables for which the
estimated effect matches the predicted
effect are capital, troubled assets, net income, and holding company affiliation. High
9

capital, low troubled assets, high net income, and holding company affiliation each
lengthen the survival time of failing banks.
Among the lending variables, only C&I
loans, agricultural production loans, and
insider loans are statistically significant in
explaining bank survival time. A high value
for any of these credit variables reduces
the expected survival time of a failing bank.
The estimated negative effect of agricultural production loans on bank survival
time partly reflects the occurrence of the
agricultural loan crisis in the first part of
our sample period. No evidence is found
to suggest that higher concentrations of
assets in lending categories leads to longer
survival times.
The only other significant variable with
regard to bank survival time is the growth
in regional employment resulting from a
reduction in oil prices. The estimated effect
for the oil-price variable is positive, which
suggests that banks in states hurt by the
energy recession failed relatively early in
the sample period. In contrast, the distinction between a rural and urban location is
not significant in explaining the survival
time of failing banks. It should be noted
that, while many of the variables fail to
possess the expected relationship with bank
survival time, in no case does the estimated
effect oppose the expected effect.
Finally, our estimation results suggest
that the closure of large failing banks has
not been delayed relative to the closure of
small banks, as no firm evidence is found
to support either a positive or a negative
relationship between asset size and bank
survival time. This finding suggests that any
regulatory costs associated with the resolution of large bank failures have not been

allowed to extend the survival time of large
failing banks. The policy of too big to fail
does not appear to have affected the timing
of failure resolutions.

Conclusion
Our results indicate that only a select
group of the variables commonly used to
predict bank failure actually help explain
the survival time of failing banks. We find
that basic indicators of a bank's condition,
such as capital, troubled assets, and net income, are related significantly to the timing
of bank failure. However, we do not find
that variables often included in bank failure
models as measures of liquidity, such as
investment securities and large certificates
of deposit, are important determinants of
bank survival time. These findings suggest
that, in attempting to project the survival
time of financially impaired banks, regulators can focus exclusively on basic financial
indicators, such as capital adequacy, asset
quality, and earnings.
In addition, our results suggest that the
survival time of failing banks has not been
related to asset size. This finding casts
doubt on the notion that favorable regulatory treatment has extended the lifetime
of large, failing banks. At the same time,
however, we also find that a large size has
tended to enhance the ability of banks to
avoid failure, everything else equal. Taken
together, these results suggest that, to the
extent that the policy of too big to fail has
been important, it has worked to prevent
the failure of large banks, but has not
extended the survival time of large banks
that eventually fail.

References
Brown, Stephen P. A., and John K. Hill (1988),
"Lower Oil Prices and State Employment,"
Contemporary Policy Issues 6 (July): 60-68.

Diamond, Douglas W. (1984), "Financial Intermediation and Delegated Monitoring," Review of Economic Studies 51 (July): 393-414.

Cole, Rebel A. (1994), "When Are Thrift
Institutions Closed? An Agency-Theoretic
Model," Journal of Financial
Services
Research 7 (December): 283-307.

Gajewski, Gregory Robert (1988), "Bank
Risk, Regulator Behavior, and Bank Closure
in the Mid-1980s: A Two-Step Logit Model,"
Ph.D. dissertation, George Washington
University, Ann Arbor (University Microfilms International).

(1990), "Insolvency Versus Closure:
Why the Regulatory Delay in Closing
Troubled Thrifts?" Federal Reserve Bank of
Dallas Financial Industry Studies Working
Paper no. 2 - 9 0 (Dallas, December).
, and Jeffery W. Gunther (1995),
"Separating the Likelihood and Timing of
Bank Failure," Journal of Banking
and
Finance 19, forthcoming.
, and
(1993), "Separating the
Likelihood and Timing of Bank Failure,"
Federal Reserve Bank of Dallas Financial
Industry Studies Working Paper no. 2 - 9 3
(Dallas, December).
Dahl, Drew, and Michael F. Spivey (1994),
"Prompt Corrective Action and Bank Efforts
to Recover from Undercapitalization,"
Journal of Banking and Finance 18, forthcoming.
Demirguc-Kunt, Asli (1991), "PrincipalAgent Problems in Commercial-Bank Failure
Decisions," Federal Reserve Bank of Cleveland Working Paper no. 9106 (Cleveland,
April).
(1989a), "Modeling Large Commercial Bank Failures: A Simultaneous Equations Analysis," Federal Reserve Bank of
Cleveland Working Paper no. 8905 (Cleveland, May).
(1989b), "Deposit-Institution Failures: A Review of the Empirical Literature,"
Federal Reserve Bank of Cleveland Economic Review, Fourth Quarter, 2-18.

Hetzel, Robert L. (1991), "Too Big to Fail:
Origins, Consequences, and Outlook," Federal
Reserve Bank of Richmond Economic
Review 77 (November/December): 3 - 1 5 .
Hunter, William C., James A. Verbrugge,
and David A. Whidbee (1994), "Risk-Taking
and Failure in De Novo Savings and Loans
in the 1980s," Journal of Financial
Services
Research 8, forthcoming.
Kane, Edward J. (1989), The S&L Insurance
Mess: How Did It Happen? (Washington,
D.C.: The Urban Institute Press).
(1986), "Appearance and Reality in
Deposit Insurance: The Case for Reform,"
Journal of Banking and Finance 10 (June):
175-88.
Lancaster, Tony (1990), "The Econometric
Analysis of Transition Data," Econometric
Society Mongraph, no. 17 (Cambridge,
England: Cambridge University Press).
Lane, William R., Stephen W. Looney, and
James W. Wansley (1986), "An Application
of the Cox Proportional Hazards Model to
Bank Failure," Journal of Banking
and
Finance 10 (December): 511-31.
Marcus, Alan J. (1984), "Deregulation and
Bank Financial Policy," Journal of Banking
and Finance 8 (December): 557-65.
Ritchken, Peter, James B. Thomson, Ramon
P. DeGennaro, and Anlong Li (1993), "On
11

Flexibility, Capital Structure and Investment
Decisions for the Insured Bank," Journal
of Banking and Finance 17 (December):
1133-1146.
Schmidt, Peter, and Ann Dryden Witte (1984),
An Economic Analysis of Crime and Justice: Theory, Methods, and
Applications,
(Orlando, Fl.: Academic Press).
(1989), "Predicting Criminal Recidivism Using 'Split Population' Survival Time
Models," Journal of Econometrics 40 (January): 141-59.

Thomson, James B. (1992), "Modeling the
Bank Regulator's Closure Option: A TwoStep Logit Regression Approach," Journal
of Financial Services Research 6 (May):
5-23.
Whalen, Gary (1991), "A Proportional
Hazards Model of Bank Failure: An Examination of Its Usefulness as an Early Warning Model Tool," Federal Reserve Bank of
Cleveland Economic Review, First Quarter,
21-31.

Have Small Banks
Been Caught
Off-Balance?
Robert R. Moore
Senior Economist
Karen Couch
Financial Industry Analyst
Financial Industry Studies Department
Federal Reserve Bank of Dallas

he definitive trend in the epoch of
modern banking has been the shift to
activities conducted off-balance-sheet. Beset
by an increasingly complex and competitive environment, U.S. banks have lost
market share in loans and deposits to
foreign and nonbank financial services
providers. Banks are coping with these
challenges by pursuing growth through a
myriad of nontraditional activities not
captured on the balance sheet.
Although somewhat difficult to measure,
off-balance-sheet activities are more important to the banking industry than ever
before. Boyd and Gertler (1994) and Kaufman and Mote (1994) consider such activities
in their studies of the competitive position
of commercial banks relative to other financial intermediaries. They conclude that the
widespread belief that banking is a declining industry may be more perception than
reality—due, in part, to an underestimation
of the importance of off-balance-sheet
activities. The increasing importance of offbalance-sheet activities suggests that the
banking industry is not retrenching but,
rather, growing with and adapting to the
changing needs of financial customers.
This article looks at the effects of offbalance-sheet activities on competition
within the banking industry. Large banking
organizations were the first to participate in
off-balance-sheet activities on any significant scale. Certain characteristics of these

nontraditional activities tend to favor large
banks. But is the movement off the balance
sheet a race for economic survival that
small banks are destined to lose?
A number of studies conducted in the
mid-1980s examined the competitive position and economic viability of small banks
in a deregulated environment (see, for example, Benston 1985, Fant 1985, Fraser and
Kolari 1985, and Kolari and Zardkoohi 1986).
The consensus appears to have been that,
although increasing competition posed a
serious challenge for small bankers, small
banks would continue to maintain their competitive vigor relative to larger institutions.
However, much of this earlier analysis
focused on banking activity as measured
by the balance sheet. We supplement traditional measures of balance-sheet activity
with newly developed estimates of offbalance-sheet activities to assess the extent
to which small banks have maintained their
importance within the banking industry.

A Traditional Analysis: Balance-Sheet Trends
We begin our study of changes in the
competitive position of small banks by
documenting recent trends in asset growth
and market share for various size classes
of banks, both nationwide and within the
Eleventh District.1 By focusing on the
balance sheet, the analysis in this section
conforms to traditional methods frequently
used in the study of market share.
National Trends. For this analysis, banks
are sorted into five groups based on their
asset size.2 The cutoff points separating the

1 The Eleventh Federal Reserve District comprises
Texas, northern Louisiana, and southern New Mexico.

In this context, w e use the term bank to indicate
either a single insured commercial bank that does not
belong to a holding company or the group of all
insured commercial bank subsidiaries within a given
holding company. W e analyze banking organizations
rather than individual banks because, within a bank
holding company, sister banks do not truly compete
with one another and, to some extent, may act as
branches of the lead bank.
2

Chart 1
Total Assets of U.S. Insured
Commercial Banks
Billions of 1980 dollars

'80 '81 '82 '83 '84 '85

87 '88

89 '90 '91 '92

'93

Bank asset size
•

Less than $200 million

•

$ 1 2 - $ 4 1 billion

•

$200 m i l l i o n - $ 2 . 3 billion

More than $41 billion

E 3 $ 2 . 3 - $ 1 2 billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

five size-groups are constructed so that, in
the 1980 base year, each group represents
about 20 percent of total industry assets. 3
Based on this categorization, the five assetsize groups are as follows: (1) less than
$200 million, (2) $200 million to $2.3 billion,
(3) $2.3 billion to $12 billion, (4) $12 billion
to $41 billion, and (5) more than $41 bil-

It is not possible to form the groups so that each
one contains exactly 20 percent of total industry
assets. Particularly in the case of the largest banks,
moving an organization from one group to another
has a significant effect on group assets.
3

Throughout this study, all statistics measured in
dollars are controlled for inflation. Such adjustments
to reflect the effects of inflation mean that a banking
organization could not grow out of its size category
unless the growth rate of its total assets exceeded the
rate of inflation.

Wheelock (1993) provides a long-term perspective
on bank consolidation, with an analysis of market
structure for the period 1 9 0 0 - 9 2 .
5

lion. For each year after 1980, banks are
categorized into the various size-groups
according to the size of their inflationadjusted assets relative to the cutoff points. 4
As indicated by Chart 1, total inflationadjusted banking assets increased 19 percent over the 1 9 8 0 - 9 3 period. Growth was
uneven across institutions grouped by asset
size, however, as small banks lost ground
to their larger competitors. After adjusting
for inflation, banks in the two groups of
smaller banks had a decline in total assets
of 19 percent over the 1 9 8 0 - 9 3 period,
while those in the two large-bank classes
had an increase of 68 percent. The banks
in the middle group showed little change.
The gains made by the largest organizations exceeded the losses sustained by the
smallest organizations, as the large institutions were not only acquiring market share
from the small ones but also were participating in an expansion of the overall market. By year-end 1993, the banks in the
two large-bank groups had increased their
share of total industry assets from 40 percent in 1980 to 57 percent, while the banking organizations in the two groups of
smaller banks saw their market share slide
from 40 percent to 27 percent.
The number of entities within each size
category is another indicator of the industry's changing structure.5 The massive consolidation that occurred within the industry
centered around the smallest institutions.
Between 1980 and 1993, the number of
U.S. banking organizations declined 32
percent, from 12,363 to 8,415. The group
comprising the smallest banks contained
11,653 institutions in 1980 and only 7,761
in 1993, a decline of 33 percent. In contrast, the group with the largest banks,
which was composed of five banks in
1980, increased to nine by 1993.
Despite external pressures, then, the U.S.
banking industry has steadily expanded in
terms of asset size. Turbulence within the
industry has taken a toll on the smaller
banking organizations, however, as they
have lost considerable market share to the
larger banking organizations. Furthermore,

far fewer small organizations existed in
1993 than in 1980.
Regional Trends: The Eleventh District.
Data from the Eleventh District reflect a
significantly different trend than that exhibited at the national level. The rapid expansion of the District banking industry in the
early 1980s and its subsequent difficulties
in the latter part of the decade have been
the subject of extensive study.6 Total District
banking assets, which were $128 billion in
1980, declined 9 percent to $117 billion in
1993, after adjusting for inflation.
As can be seen from Chart 2, the District's smaller institutions were less affected
as a group by the 1980s boom and bust
than were the larger organizations. Using the
same method as was used for the analysis
of the U.S. banking industry, the District's
banks are divided into five groups, with
each group holding about 20 percent of
the District's total banking assets in 1980. 7
The banks in the two groups of smaller
banks had an 11-percent decline in the
inflation-adjusted value of their assets from
1980 to 1993. But, despite a decline in their
market share to a low of 32 percent in
1983, by 1993 they almost regained the 40percent market share they had in 1980. On
the other hand, the banks in two groups of
large banks saw their market share peak at
60 percent in 1983. By 1993, their market
share of 45 percent represented only a
modest gain over the 1980 level.
Over this period, the number of banking
organizations in the District declined 26
percent, from 1,342 to 998. The decline
was concentrated among the institutions in
the smallest-size category, which fell from
1,113 banking organizations at the end of
1980 to 751 at the end of 1993. No change
occurred in the largest-size category, which
contained two organizations in both 1980
and 1993- An increase in the average asset
size of banks in the smallest-size group
enabled that group to maintain its market
share of assets, even as the number of
banks in the group was sharply declining.
To summarize, Eleventh District banking
industry assets have contracted, running

Chart 2
Total Assets of Eleventh District
Insured Commercial Banks
Billions of 1980 dollars

'80

'81

'82

'83

'84

'85

'86

'87

'88

'89

'90

'91

'92

'93

Bank asset size
•

Less than $60 million

E I $ 4 - $ 1 1 billion

•

$ 6 0 - $ 4 0 0 million

ES More than $11 billion

$ 4 0 0 m i l l i o n - $ 4 billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

counter to the national trend of slow industry growth. And, while small banking
organizations here have become fewer in
number, they have not suffered the same
decline in share of total assets as their
counterparts at the national level. The
enduring strength of the District's small
banks partly reflects the shakeout of the
District's largest institutions that occurred
during the tumultuous 1980s.

See, for example, Short (1991), Gunther (1989), and

Robinson (1990).
Our analysis of Eleventh District banking organizations is complicated by the fact that some bank holding companies operate in multiple Districts. For multiDistrict bank holding companies, only the assets held
in subsidiary banks in the Eleventh District are included in the analysis. For example, suppose that XYZ
Bankshares, Inc. controls three $20 million banks, one
in Oklahoma and two in Texas. For the analysis of
the Eleventh District, XYZ Bankshares, Inc. is considered to b e a $40 million bank.

Beyond the Balance Sheet
Total assets provide a simple and easily
obtainable measure of banking activity, but
traditional balance-sheet analysis does not
reveal the full scope of the importance of
banks as financial intermediaries.8 Sparked
by advances in computer and communications technology and the proliferation of
competition in the financial services industry, commercial banks are turning to other
ways to increase their profitability. While
traditional deposit-based lending remains
the core business of most commercial
banks, recent data show an increasing
reliance on noninterest income from offbalance-sheet activities. An associated trend
is the expansion of asset securitization,
which, like loan participations and outright
sales, is a means of removing loans from
the balance sheet while generating noninterest income. Securitization has afforded
banks additional business opportunities as
issuers and purchasers of asset-backed
securities, as well as originators and servicers of the underlying assets.
Many fee-based services, such as fiduciary activities and mutual fund sales, do
not correspond to assets or liabilities subject to entry on banks' balance sheets.
Trust department assets, for example, are
managed for a fee but are not owned by
the banks. Similarly, mutual funds do not
appear on the balance sheet, but banks
receive sales-related fees and, in some
cases, additional compensation for their
services as investment advisors, custodians,
and administrators of the funds.
Credit enhancement is another fee-based
service that isn't reflected on the balance
sheet. Two of the most common transac-

Some material in this section was derived from
internal Federal Reserve System examination guidelines issued May 25, 1990.

For additional discussion of these derivative instruments, see Siems (1994).

tions are loan commitments and letters of
credit. Loan commitments are irrevocable
obligations made by a bank to advance
funds at a future date in the form of loans
or participations in loans, lease financing
receivables, or similar transactions. Usually
extended for working capital or seasonal
or cyclical needs, loan commitments allow
the customer to obtain credit from the bank
under prearranged terms. Generally, the
bank receives an upfront fee and fees on
the unused balance.
Letters of credit are documents issued
by a bank for a fee on behalf of its customer authorizing a third party, the beneficiary, to draw drafts on the bank up to a
specified amount, with certain terms and
conditions. Under the terms of a standby
letter of credit, drafts are drawn only when
an underlying event fails to occur. Financial standby letters of credit back direct
financial obligations, such as the customer's
payment of commercial paper. Performance standby letters of credit back the
customer's completion of a specific contract, such as the delivery of merchandise
or the completion of construction.
Financial standby letters of credit are
often used to support U.S. commercial
paper issues. Because the bank performs
the credit analysis and assumes the credit
risk, commercial paper backed by letters
of credit may be sold at more attractive
interest rates and achieve a wider distribution than issues sold without the benefit of
such credit enhancement.
Other important off-balance-sheet items
are interest rate contracts and foreign
exchange rate contracts, which are used to
hedge against risk. These include swaps,
options, futures, and forward contracts.9
Asset securitization also has had a significant impact on bank activities. Securitization is the packaging of similar loans
into marketable securities for sale to investors. These securities are most often
backed by residential mortgages, although
other securitized assets include automobile
loans, boat loans, commercial real estate
loans, student loans, home equity loans,

credit card receivables, lease receivables,
nonperforming loans, and other loans.
Small business loans may soon be added
to this list, as legislation signed into law on
September 23, 1994, was designed to encourage the formation of a secondary market for small-business-backed securities.
Banks may participate in many facets of
the securitization process. They may originate, package, or service the assets; serve
as trustee for the pool; provide credit enhancement; underwrite the issue; or invest
in the securities. In addition to providing
a source of income, asset securitization
enhances an institution's liquidity, as loans
are converted into marketable securities
and can be used as a tool in asset/liability
management, helping reduce interest rate
risk. Further, purchases of asset-backed
securities allow a bank to diversify its asset
base both geographically and by industrial
sector. This feature is particularly attractive
to small banks, whose access to a wide
variety of top-quality borrowers may be
limited.
Some of the characteristics of off-balancesheet activities tend to provide large banks
with a competitive advantage relative to
small banks. To the extent that off-balancesheet activities involve large fixed costs,
these activities have to operate on a large
scale to be profitable. Proprietary mutual
funds are one such example. Other offbalance-sheet activities, such as mortgage
servicing, require a high volume of activity
to produce significant income because of
their slim margins. Financial standby letters
of credit to support commercial paper issues
are generally large due to the large size of
typical commercial paper issues. Derivative
contracts also tend to involve large notional
amounts. Certain other off-balance-sheet
activities, however, are accessible to smaller
institutions, including some types of asset
securitization and fiduciary activities.

cluded in any accurate measure of banking
activity. However, the notional value of
these nontraditional activities is not comparable to the total asset value of traditional
banking activities. For example, an unused
loan commitment of $1 million is not equivalent to a loan of $1 million because it may
never be funded, it does not entail the
same risks, and it does not provide equal
income to the lender. Thus, dollar-fordollar comparison of the notional value of
off-balance-sheet activities to balance-sheet
assets is not meaningful.
An alternative approach would gauge
the importance of off-balance-sheet activities by the income they generate. Two
methodologies, discussed in detail below,
are based on the simple idea that the
greater the amount of income generated
off-balance-sheet, the greater the importance of these activities to the business of
banking.
The Capitalization-of-Income Approach.
The first method of measuring off-balancesheet activity is predicated on the notion
that the rate of return on off-balance-sheet
activities may be comparable to the rate of
return on balance-sheet activities. To maximize profits, banks can be expected to
adjust their participation in balance-sheet
and off-balance-sheet activities until the
payoffs from the two activities are equal at
the margin. Under certain assumptions,
this implies that the rates of return on the
balance-sheet and off-balance-sheet activities would be equal. The income generated
by off-balance-sheet activities could then
be used to estimate their value.10 Data
obtained from the Consolidated Reports of
Condition and Income (or call reports)
collected by federal bank regulatory agencies provide the return on balance-sheet
assets and an estimate of income received
from off-balance-sheet activities, which are

Measurement of Off-Balance-Sheet Activities
Because of their growing importance,
off-balance-sheet activities have to be in-

10 See Boyd and Gertler (1994) for further discussion
of this approach.

used to calculate a dollar value for offbalance-sheet activities.11 12
The Value-Added A p p r o a c h . A second
method of measuring the importance of
off-balance-sheet activities is based on the
concept of value added. Broadly speaking,
an industry's value added is equal to the
revenue generated from the sale of its products or services, minus the value of the
raw materials that it purchases. The value
added of a given industry can be used to
assess the contribution of that industry to
overall economic activity, which is represented by the gross domestic product (GDP).
The banking industry's value added
stems from two components: the value
added by balance-sheet activities and the
value added by off-balance-sheet activities.13 The value added by balance-sheet
activities is estimated in this study as the
sum of interest income and service charges
on deposits less the sum of interest expense and the provision for loan losses.
The value added by off-balance-sheet

Chart 3
Adjusted Assets of U.S. Insured
Commercial Banks
Billions of 1980 dollars

3,500 -|

80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93
•

Balance-sheet assets

H Off-balance-sheet assets

DATA SOURCE: Consolidated Reports of Condition and
Income.

activities is estimated as noninterest income
exclusive of service charges on deposit
accounts.

A Comprehensive Analysis: Combined
Balance-Sheet and Off-Balance-Sheet Trends
The return on assets used in the calculations here
is not the conventional measure, which compares
net income with total assets. Instead, the return on
balance-sheet assets is the ratio of interest income
and service charges on deposits less interest expense
and the provision for loan losses to total assets. Noninterest income exclusive of service charges on deposits is excluded from the numerator because it is
attributed to off-balance-sheet activities for purposes
of this study.
11

12 The best estimate of income received from offbalance-sheet activities that can b e obtained from the
call reports is noninterest income less service charges
on deposits. This measure may understate the income
actually generated by off-balance-sheet activities, however. Some of the income received from off-balancesheet activities is included in interest income instead
of noninterest income. For example, under certain
circumstances, loan commitment fees are included in
interest income, even though loan commitments are
an off-balance-sheet activity.

13 Value added in the banking industry is discussed in
detail by the American Bankers Association (1994).

We now reassess the competitive position of small banks by documenting recent
trends in balance-sheet and off-balancesheet activities for various size-classes of
banks, both nationwide and within the
Eleventh District. By accounting for offbalance-sheet activities, the analysis presented in this section goes beyond the
traditional methods used in previous studies
of market share.
National Trends. The estimates generated
by the capitalization-of-income approach
for the dollar value of off-balance-sheet
activities for U.S. banks appear in Chart 3The upper portion of each bar shows the
estimate of off-balance-sheet activities, and
the lower portion shows actual balancesheet assets. The sum of balance-sheet
assets and off-balance-sheet assets is hereafter referred to as "adjusted assets."

Chart 3 shows clearly that the banking
industry has relied heavily on off-balancesheet activities to generate growth. Over
the 1 9 8 0 - 9 3 period, the industry's adjusted
assets increased 42 percent, more than
double the 19-percent growth in balancesheet assets during this period. This finding
highlights the importance of accounting for
off-balance-sheet activities in the analysis
of market share.
Similar results are obtained using the
value-added approach. Estimates of the
banking industry's value added appear in
Chart 4. The lower portion of each bar represents the value added by balance-sheet
activities, and the upper portion represents
the value added by off-balance-sheet activities. Together, they represent total value
added. Between 1980 and 1993, total value
added increased 79 percent, while real
GDP increased 36 percent. Thus, the industry's contribution to overall economic
activity was growing as a fraction of total
economic activity between 1980 and 1993,
with nearly half of that growth coming
from off-balance-sheet activities. The value
added from off-balance-sheet activities

Chart 4
Value Added to the Economy by
U.S. Insured Commercial Banks
Billions of 1980 dollars

120 -i
100 -

Charts
Changes in Market Share, Based on Assets
Of U.S. Insured Commercial Banks, 1980-93
Percentage point gain or loss
from 1980 to 1993

15 -i
10 -

Less than
$200 million

$200 million$2.3 billion

$2.3-$12
billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

grew by $25 billion, or 222 percent, while
the value added from balance-sheet activities increased by $27 billion, or 50 percent.
Adding off-balance-sheet activities to
traditional balance-sheet activities slightly
amplifies the market share losses suffered
by small banks over the 1 9 8 0 - 9 3 period.
Chart 5 compares the changes that occurred
over this period in the market shares for
the various size-groups using balance-sheet
assets alone and also with off-balance-sheet
activity as estimated by the capitalizationof-income approach. 14 The share of balancesheet assets controlled by the two groups
of smaller banks fell 13 percentage points
from 40 percent in 1980 to 27 percent in
1993- The share of adjusted assets controlled by the two groups of smaller banks
fell 14 percentage points, from 36 percent
in 1980 to 22 percent in 1993.
Similar market share results are obtained
using the value-added approach. Total
value added by banking organizations in

'80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93
•

Balance-sheet activities

E3 Off-balance-sheet activities

DATA SOURCE: Consolidated Reports of Condition and
Income.

14 These are the same size-group definitions that were
used in the traditional analysis.

Chart 6

Chart 7

Changes in Market Share, Based on
Value Added by U.S. Insured Commercial
Banks, 1980-93

Adjusted Assets of Eleventh District
Insured Commercial Banks

Percentage point gain or loss
from 1980 to 1993

Billions of 1980 dollars

250
200 -

150 -

'80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93
-15

•
Less than
$200 million

$200 million$2.3 billion

$2.3-$12
billion

$12—$41
billion

More than
$41 billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

the two groups of smaller banks was fairly
stable—$31 billion in 1980 and $30 billion
in 1993 in inflation-adjusted dollars. Nevertheless, their share of the industry's total
value added declined 23 percentage points
over this period, from 48 percent in 1980
to only 25 percent in 1993. When only the
value added associated with balance-sheet
activities is considered, the decline in market
share is somewhat less, as shown in Chart
6. The share of balance-sheet value added
controlled by the two groups of smaller
banks fell 20 percentage points, from 51
percent in 1980 to 31 percent in 1993.
To summarize, the estimates obtained
under both the capitalization-of-income
approach and the value-added approach
indicate that off-balance-sheet activities
have become increasingly important for the
U.S. banking industry. Much of the industry's
growth was derived from its tactical shift
into off-balance-sheet activities. Moreover,
the movement to off-balance-sheet activities slightly increased the losses in market
share suffered by small banks over the
1 9 8 0 - 9 3 period.
20

Balance-sheet assets

E3 Off-balance-sheet assets

DATA SOURCE: Consolidated Reports of Condition and
Income.

Regional Trends: The Eleventh District.
Eleventh District banks have not pursued
off-balance-sheet activities to the same
extent as banks nationwide. As shown in

Chart 8
Value Added to the Economy by Eleventh
District Insured Commercial Banks
Billions of 1980 dollars

7 -i

'80 '81 '82 '83 '84 '85 '86 '87 '88 '89 '90 '91 '92 '93
•

Balance-sheet activities

H Off-balance-sheet activities

DATA SOURCE: Consolidated Reports of Condition and
Income.

Chart 7, adjusted assets in the District increased by less than 1 percent over the
1 9 8 0 - 9 3 period. And, as shown in Chart 8,
the value added generated by off-balancesheet activities in the District increased 127
percent over the 1 9 8 0 - 9 3 period, substantially less than in the rest of the nation.
Chart 9 compares changes in market share
for the various size-groups of District banks
using balance-sheet assets and adjusted
assets as estimated by the capitalization-ofincome approach. 15 The combined share of
balance-sheet assets controlled by the two
groups of smaller District banks fell 1 percentage point, from 40 percent in 1980 to
39 percent in 1993- Their combined share
of District adjusted assets declined by 4
percentage points, from 38 percent in 1980
to 34 percent in 1993Using the value-added approach, small
banks' loss in market share was greater
than under the capitalization of income
approach. The two groups of smaller banks
controlled 44 percent of the value added
associated with District balance-sheet activities in 1993, down from 50 percent in

Chart 9
Changes in Market Share, Based on
Assets of Eleventh District Insured
Commercial Banks, 1980-93
Percentage point gain or loss
from 1980 to 1993

Less than
$60 million

$60-$400
million

$400 million$4 billion

$4-11
billion

Chart 10
Changes in Market Share, Based on
Valued Added by Eleventh District Insured
Commercial Banks, 1980-93
Percentage point gain or loss
from 1980 to 1993
20

Less than
$60 million

$60-$400
million

$400 million$4 billion

$4-11
billion

More than
$11 billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

1980. As shown in Chart 10, the share of
total value added controlled by these two
groups of District banks fell 9 percentage
points, from 48 percent in 1980 to 39 percent in 1993.
In summary, our findings indicate that
small banks in the District have suffered a
smaller decline in market share than small
banks in the United States as a whole. At
the national level, the two groups of smaller
banks controlled 22 percent of the total
quantity of assets adjusted for off-balancesheet activities in 1993, down from 36 percent in 1980. In contrast, the District's small
banks controlled 34 percent of the total
quantity of assets adjusted for off-balancesheet activities in 1993, down only slightly
from 38 percent in 1980. At both the District
and national levels, expanding the definition of the banking market to include offbalance-sheet activities reveals an increase
in the loss of market share by small banks.

More than
$11 billion

DATA SOURCE: Consolidated Reports of Condition and
Income.

15 These are the same size-group definitions that were
used in the traditional analysis.

Conclusion
The difficulties faced by small banks
have been well documented by traditional
analyses. The consolidation of the industry
has had by far the most impact on the
smallest banks. Further, small banks have
lost a significant amount of market share,
as measured by total assets.
In light of the industry's shift to offbalance-sheet activities, however, it has
become increasingly important to extend
banking analysis beyond the balance sheet.
Both large and small banks have used offbalance-sheet activities as a vehicle for
growth, although, due to the nature of offbalance-sheet activities, large banks have

pursued them more effectively than have
small banks. When the definition of the
banking market is expanded to include offbalance-sheet activities, small banks have
had an even greater loss of market share
than has been suggested by traditional
analysis.
In the Eleventh District, small banks
have been much more successful in maintaining their competitive position, having
lost a much smaller share of the banking
market between 1980 and 1993 than their
counterparts nationwide. National trends,
however, continue to underscore the potentially fragile position of small banks in the
current banking environment.

References
American Bankers Association (1994), Report
of the Market Share Task Force (Washington, D.C.: American Bankers Association),
June.
Benston, George J. (1985), "What Does
Experience Tell Us about Competition?" in
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Full text of Financial Industry Studies : December 1994

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