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Federal Reserve Bank of Chicago

The Past, Present, and Probable
Future for Community Banks
Robert DeYoung, William C. Hunter and
Gregory F. Udell

WP 2003-14

The Past, Present, and Probable Future for Community Banks

Robert DeYoung*
Federal Reserve Bank of Chicago
William C. Hunter
University of Connecticut
Gregory F. Udell
Indiana University

* The views and opinions expressed in this paper are not necessarily those of the Federal Reserve Bank of
Chicago or the Federal Reserve System. The authors thank Robert Avery, Allen Berger, Paul Calem, Don
Hester, Elizabeth Mays, Jim McNulty, Dan Nolle, Tara Rice, and Larry Wall for helpful comments.
Please address correspondence to Robert DeYoung, Economic Research Department, Federal Reserve
Bank of Chicago, 230 South LaSalle St., Chicago, IL 60604, phone: 312-322-5396, email:
robert.deyoung@chi.frb.org.

The Past, Present, and Probable Future for Community Banks

Abstract: We review how deregulation, technological advance, and increased competitive rivalry have
affected the size and health of the U.S. community banking sector and the quality and availability of
banking products and services. We then develop a simple theoretical framework for analyzing how these
changes have affected the competitive viability of community banks. Empirical evidence presented in
this paper is consistent with the model’s prediction that regulatory and technological change has exposed
community banks to intensified competition on the one hand, but on the other hand has left well-managed
community banks with a potentially exploitable strategic position in the industry. We also offer an
analysis of how the number and distribution of community banks may change in the future.

JEL Codes: G18, G21, L11, O33
Key Words: community banks, small business lending, banking industry consolidation.

The Past, Present, and Probable Future for Community Banks

In most developed countries, the majority of banks and savings institutions continue to be small
and community-based. But advances in information technology, new financial instruments, innovations
in bank production processes, deregulation, and increased competition have created a less hospitable
environment for community banks. The number of community banks is shrinking in most countries, as
are their shares of loan and deposit markets. For example, by some measures both the number and market
share of community banks in the U.S. have approximately halved since 1980.
Given these trends, it is natural to wonder if the community bank business model will continue to
be viable in the future. The specter of a declining, or perhaps a disappearing, community banking sector
has potentially serious implications for the U.S. economy. Most obviously, the small business sector – an
historically crucial source of innovation and new job creation – has traditionally relied on small local
banks for credit.
This paper presents a comprehensive view of the community banking sector in the U.S. in three
parts. Each of these three sections includes numerous citations to the recent academic literature, and each
is supported by a variety of data from the U.S. banking industry. First, we review the past three decades
of change in the U.S. banking system, with a special focus on how deregulation, technological advance,
and increased competitive rivalry have affected the size and health of the community banking sector.
Second, we use a strategic map approach to develop a theory of how deregulation and
technological change have affected the competitive viability of community banks. The theory suggests
that regulatory and technological change has exposed community banks to intensified competition on one
hand, but on the other hand has left well-managed community banks with a potentially exploitable
strategic position in the industry. We show that data drawn from the U.S. banking industry over the past
three decades are largely consistent with these characterizations.
Third, we consider the number of community banks that will remain viable in the future.
Projecting the future number and size distribution of commercial banks after the U.S. banking industry
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has fully adjusted to deregulation is a treacherous exercise, and we do not pretend to be able to make
accurate point estimates. Rather, we consider the recent financial performance of community banks
relative to large banks, and, based on straightforward market principles, suggest which types of
community banks, and how many of each type, are most at risk and least at risk going forward. Of
course, this approach leaves the ultimate questions unanswered – How many community banks will exist
in the future? What will these community banks look like? Who will these community banks serve? – so
we close with a discussion of useful areas for future research on community banks.
Although our analysis focuses on community banks in the U.S., our major findings about the
effects of technology, deregulation, and competition on community banking are likely to hold for other
developed nations as well. New information, communication, and financial technologies travel easily
across geographic boundaries, and ongoing financial deregulation in Europe and Asia are similar in spirit
to the recent deregulation of U.S. financial institutions and markets. However, our analysis may be less
appropriate for banks in developing economies.1

1. What is a community bank?
Before we can begin our analysis we need to define a community bank. Industry participants
have little trouble distinguishing a community bank from, say, a regional bank or a money center bank.
Based on their experiences, they can use an “I know one when I see one” test. But for someone trying to
establish how community banks differ en masse from other types of commercial banks, establishing a
definition of “community bank” is not an easy – and perhaps not even a fully solvable – proposition.
In practice, most research economists, industry analysts, and even some regulators simply
establish an upper size threshold – typically around $1 billion in bank assets – and refer to all banks lying
below that threshold “community banks.”

Although bank size may be the best single proxy for

identifying a community bank, this uni-dimensional approach will fail to identify some large community
banks and will misidentify some small non-community banks.

Community banking is a complex

phenomenon, and bank size is really just an instrument for identifying banks with a richer set of

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characteristics.

The following qualitative definition captures some of these characteristics: “A

community bank is a financial institution that accepts deposits from and provides transactions services to
local households and businesses, extends credit to local households and businesses, and uses the
information it gleans in the course of providing these services as a comparative advantage over larger
institutions.” We also find the following more applied definition useful: “A community bank holds a
commercial bank or thrift charter; operates physical offices only within a limited geographic area; offers a
variety of loans and checkable insured deposit accounts; and has a local focus that precludes its equity
shares from trading in well-developed capital markets.”
Limited data availability restricts us from following either of these two definitions to the letter.
However, we are able construct a multi-dimensional filter that employs a number of these definitional
characteristics to identify community banks and separate them from non-community banks. For our
purposes, a community bank (a) holds less than $1 billion in assets (2001 dollars); (b) derives at least half
its deposits from branches located in a single county;2 (c) is domestically owned; (d) has a traditional
product mix that includes portfolio lending, transactions services, and insured deposits; and (e) is either
an independent bank, the sole bank in a one-bank holding company, or an affiliate in a multibank holding
company (MBHC) comprised solely of other community banks. Further details of our data selection
methods are included below.

2. That Was Then, This is Now
In this section we first take a look at the world of community banks in the U.S. in the 1970s.
Then we examine the changes over the intervening decades that radically altered this world. As we will
see, much of the impetus for change came from outside the banking industry. These external factors
exposed the entire banking industry to new competitive forces that affected both the lending and deposittaking sides of the balance sheet, and transformed the income statements of many banking companies as
well. Fueled by innovation and deregulation the industry was dramatically transformed. In the process

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the world of community banking has been redefined. We conclude this section with a look at community
banking today – the new world of community banking.

2.1 An Idyllic World for Community Banks in the 1970s.
It is impossible to describe the U.S. commercial banking environment in the 1970s without first
noting that it was very much a protected industry. Government regulations shielded the industry from
geographic competition, from product competition and, at least on part of its business, from pricing
competition. Indeed, banking – and particularly community banking – was a comfortable place to be.
Protection from geographic competition was anchored by The McFadden Act of 1927 that
prohibited interstate branch banking. The only loophole in the McFadden Act was cross-border banking
through multibank holding companies. However, exploitation of this loophole required state approval
and not a single state in the 1970s permitted the out-of-state ownership of one of its banks by a multibank
holding company.3
intrastate branching.

In addition to these interstate restrictions, most states imposed restrictions on
Some states at the time, most notably Illinois and Texas, prohibited any

branching.4,5
On the product dimension banks were insulated from competition from investment banks,
insurance companies, and brokerage firms by the Glass-Steagall Act that effectively isolated commercial
banking as a separate and highly regulated financial sector. Moreover, depository institutions such as
savings and loans and credit unions were not permitted to compete with banks for their main line of
business, commercial loans. On the deposit dimension, banks were prohibited throughout most of the
1970s from competing on interest rates by Regulation Q which imposed interest rate ceilings on all
deposit rates except negotiable CDs above $100,000.
By 1980 there were still 14,434 chartered commercial banks in the U.S., and 14,078 of these
banks held less than $1 billion (2001 dollars) of assets – as discussed above a standard crude definition of
a community bank.6 By this measure, community banks represented 33.4 percent of the industry’s assets.
The banking industry was still the largest category of financial intermediary in the U.S. with over 35

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percent of the nation’s intermediated assets and when combined with thrifts (including credit unions),
depository institutions as a whole had nearly 60 percent of intermediated assets.7 Nevertheless, GlassSteagall assured that financial markets were quite segmented and that the business of banking was
focused on offering deposits and loans. However, within these product categories, the banking industry
was a major player and in some markets the dominant player. For example, the industry’s deposit
franchise made it the dominant provider of transactions services through checkable deposit accounts.
Depository institutions were also a major provider of low risk relatively liquid investments (savings
accounts) and low risk short- and intermediate-term investments (time deposit accounts). As a result
depository institutions were an extremely important investment vehicle for consumers. This is reflected
in Table 1 which shows consumer financial assets based on the Federal Reserve’s Survey of Consumer
Finance (SCF) in 1983, the first year this data was available. Lines 1, 3 and 4 are the fraction of total
financial assets that consumers allocated to depository institutions. In 1983 consumers allocated 22.7
percent of their assets to depository institutions.
Another important feature of the 1970s deposit franchise was the fact that the payments system at
the time was predominantly paper-based. In a banking world that emphasized brick and mortar delivery,
community banks’ enjoyed substantial market position because large commercial banks were constrained
from competing in local markets. In states with limited or no branch banking this advantage was
especially significant, because large banks simply could not branch into local markets. In addition, at
least in the first half of the 1970s, ATM machines had not been widely adopted, thus further shielding
local community banks from competition from larger banks.
With respect to lending, banks and thrifts were not the only players. However, loan markets were
generally segmented and in some of these markets banks and thrifts were the dominant players. For
example, at the beginning of the 1970s the residential mortgage market was mostly a banking and thrift
market. Some residential mortgages were held by insurance companies and finance companies and some
were held in securitized pools. These holdings, however, were relatively small compared to banks and
thrifts. In particular the securitization market was still in its infancy and limited mostly to Ginnie Mae

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passthroughs. Community banks were significant players in the residential mortgage markets at the close
of the decade, allocating about 40 percent of their loan portfolio to real estate loans in 1980 (see Table A5 in the Appendix).
Banks and thrifts competed with finance companies for consumer loans although, even here, there
appears to have been considerable market segmentation. Consumer finance companies tended to attract
the higher risk and subprime consumer borrowers while banks, thrifts and possibly captive auto finance
companies (e.g., FMAC, GMAC) tended to attract the prime consumer borrower.8 Again, because of the
extensive limitations on branch banking, community banks enjoyed an advantage in consumer lending
over larger banks with their local market power. Community banks allocated about 30 percent of their
loan portfolio to consumer loans in 1980 (see Table A-5).
Table 2 provides an overall picture of consumer debt from the perspective of the consumer. It
shows the allocation of consumer debt by institution in 1983, the first year that this data was available.
The data clearly show the importance of depository institutions in providing consumer finance.
Consumers obtained 59.8 percent of their debt from depository institutions in 1983, much of it in the form
of residential mortgages, with the remainder spread out among a number of different sources.
Commercial lending in the 1970s reflected some segmentation both across financial institutions
and within the banking industry itself, although larger commercial banks made loans to business
borrowers of all sizes. During most of the 1970s large commercial banks were still the major source of
short-term financing to large businesses. Life insurance companies were also active in business finance
but their activities were confined to longer term financing to medium-sized businesses and some large
businesses. Small businesses are generally unable to get long term financing other than to finance
specific fixed assets such as equipment and real estate (see Carey et al 1993). Community banks,
constrained by legal lending limits, focused on lending to smaller businesses.9

Community banks

allocated on average between 20 and 30 percent of their loan portfolio to commercial loans in 1980 (see
Table A-5).

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Banks, including community banks, faced some competition from commercial finance companies
that were active in small and middle market commercial lending. Again, however, there was considerable
segmentation. Commercial finance companies tended to focus on higher risk borrowers by providing
asset-based loans or by factoring receivables.10 Asset-based loans are loans that are primarily based on
collateral, specifically accounts receivable, inventory and equipment, that involve a high intensity
monitoring technology. This monitoring can include daily submission of new invoices, collection of
receivables through lock-box arrangements, daily calculation of loan availability, and periodic field audits
conducted by the lender. Factoring involves the purchase of receivables by a financial intermediary
usually without recourse.11 Some large banks and some community banks provided asset-based lending
and/or factoring but in the 1970s this segment of the market was mostly supplied by commercial finance
companies including some large ones such as Commercial Credit, Walter Heller and Talcott.

Not

infrequently, however, banks including some community banks would participate with commercial
finance companies with the understanding that if their borrower’s risk profile improved in the future the
commercial finance company would return its share of the loan to the bank.

2.2 Three Decades of Change
The idyllic world of community banking relied on a set of strict federal and state banking
regulations that shielded local banks from outside competition, prevented the entry of nonbank financial
institutions into traditional banking product markets (and vice versa), and prevented price competition
among banks for transactions deposits. Volatile economic conditions, technological change, and an antiregulation evolution in political and economic thought in the 1970s, 1980s, and 1990s led to the
dismantling of these banking regulations, and brought an end to the comfort zone of community banks.

The 1970s: Volatile interest rates and the beginnings of technological change. In the late 1960s and
early 1970s money market interest rates regularly exceeded the Regulation Q ceiling on interest rates.
The gap became huge after the Federal Reserve changed monetary policy in 1979 with the 90 day

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Treasury Bill rate at one point exceeding the passbook savings account ceiling by over 1000 basis points.
As a consequence, disintermediation became a problem for the banking industry in the late 1970s, as
deposits flowed out of low-yielding bank deposits and into higher yielding investments offered by
nonbank institutions not constrained by Regulation Q.

Community banks and thrifts were more

dependent on retail deposits and less dependent on large denomination CDs than large banks. As shown
in Table A-5, large banks relied on large denomination deposits for over 30 percent of their funding in
1980 while large, medium and small community banks relied on this source for only 20 percent, 15
percent and 9 percent respectively. Thus, the potential threat from disintermediation was arguably more
acute for these smaller institutions because they were more dependent on the types of deposits that were
generally constrained by Regulation Q ceilings.
The threat from disintermediation in the late 1970s was much more serious than it was when
interest rates spiked earlier in the decade when retail customers had few alternatives to bank deposits for
liquid investments. The minimum denominations on money market instruments such as negotiable CDs
and commercial paper were too high for the small investor. However, money market mutual fund
(MMMF)s, a monumentally important financial innovation first introduced in 1971, would inalterably
change the financial landscape in the U.S. MMMFs combined two features that gave them a big
competitive advantage over Regulation Q-constrained bank deposits:

1) money market investment

returns and 2) checkability. Later in the decade Merrill Lynch took this innovation one step further with
its Cash Management Account by adding a third dimension, a brokerage account. In the mid-1970s
market interest rates receded some and as a result the flow of funds into MMMFs did not reach a level
that threatened the banking industry’s deposit franchise, but in the late-1970s MMMFs once again began
to grow dramatically. Moreover, innovations elsewhere in the financial services sector created even more
alternatives to bank deposits such as the universal life insurance policy. Universal life combined term life
insurance with a money market-linked savings component. The impact on banks and thrifts was acute.
Other innovations had an equally powerful impact on retail banking. One of the most important
was the Automated Teller Machine (ATM), which had an impact on both the cost (cheaper to produce)

8

and the quality (limited options, but more convenient) of transactions services. Banks had initially hoped
that the ATM, as its name implies, would be a substitute for human tellers, and by extension perhaps even
a partial substitute for bank branches. However, as seen in Figure 1, as the number of ATMs has
increased, so has the number of bank branches. These data suggest a number of important strategic roles
for bank delivery systems, e.g., increased customer convenience, revenue generation via third-party ATM
fees, person-to-person contact with customers at brick-and-mortar branches.
Other alternatives to brick and mortar banking began to appear after the ATM. Some banks
began offering pre-Internet retail computer banking in the 1980s where customers with a computer and
modem could pay bills and transfer money between accounts over telephone lines. Credit cards and debit
cards expanded rapidly in the 1970s and 1980s represented yet another alternative to the traditional bank
delivery system.12

The 1980s: Regulatory reaction and further technological innovation. During the 1980s it became
increasingly difficult to maintain a regulatory environment that could protect the banking industry from
product competition, interregional competition and interest rate competition – and at the same time insure
a vibrant and healthy banking industry. Market conditions and financial and technological innovation
simply conspired against preservation of the old regime. Regulatory change became inevitable and
necessary to rationalize the new reality.
On some dimensions this change came quickly. For example, the huge spike in interest rates
beginning in 1979 led to the relatively rapid legislative dismantling of Regulation Q that culminated with
the passage of the Garn-St. Germain Depository Institutions Act in 1982. This Act also allowed, among
other things, for thrift institutions to compete directly with community banks by making commercial
loans.
Next came the dismantling of the McFadden Act both at the intrastate and the interstate levels.
This process took more time than the dismantling of Regulation Q, but its effect was nevertheless equally
dramatic. At the intrastate level 32 states liberalized their in-state geographic restrictions on banking

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between 1980 and 1994.13 At the interstate level, regulatory reform began in the early 1980s with state
legislative initiatives to exploit the multibank holding company loophole in the McFadden Act. States
entered into reciprocity agreements where participating states agreed to allow cross-border bank
ownership through multibank holding companies. By the end of the decade, all but six states allowed
some sort of interstate banking with most being part of large regional pacts (Berger, Kashyap and Scalise
1995).
Like geographic liberalization, expansion of banking powers occurred somewhat more
incrementally than interest rate liberalization. Nevertheless, after two decades the result was the same: a
substantial elimination of most barriers. On the retail side, the first major change was arguably the
creation of the money market deposit account (MMDA) by the Garn-St. Germain Act in 1982. MMDAs
gave banks a vehicle to compete directly with MMMFs. Until the end of the 1990s most of the other
changes were facilitated by Federal Reserve Board rulings. The Federal Reserve was given the authority
under the 1956 Bank Holding Company Act and the 1970 Amendments to the Act to determine what
activities could be conducted by banking organizations subject to the condition that these activities are
“closely related to banking”. This and additional legislation imposed some fairly strict limitations on
some product lines such as insurance. Although even here, state chartered banks were able to exploit
some state-level opportunities for regulatory arbitrage. Delaware, for example, offered the opportunity
for large bank holding companies to established state-chartered subsidiaries with their own insurance
affiliates.14
Banks challenged these restrictions on a wide variety of fronts including municipal bond
underwriting, commercial paper underwriting, discount brokerage, managing and advising open- and
closed-end mutual funds, and underwriting mortgage-backed securities. On some challenges banks were
successful and on some they were not, with many of the challenges being adjudicated in the courts with
opposition of trade groups such as the Securities Industry Association. Then in 1987 the Federal Reserve
allowed banks to form investment banking subsidiaries (i.e., Section 20 subsidiaries) and in 1989 the

10

Federal Reserve granted limited corporate securities underwriting privileges to a select group of banks.
The limitations and the number of authorized banks increased in the following years.
Not only did innovation conspire to drive changes in the regulation of the financial services
industry, it also fundamentally changed the nature of many aspects of the business of banking beyond just
the ATM machine discussed above. This is not entirely surprising given that banking is the most ITintensive industry in the U.S. as measured by the ratio of computer equipment and software to value
added (Triplett and Bosworth 2002, Table 2).
One of the most dramatic examples of innovation was securitization. Unlike the changes just
discussed that involved the government getting out of the way (i.e., the dismantling of Regulation Q,
McFadden, and Glass Steagall), securitization is a story about government intervention right from the
beginning.

Securitization began in the 1960s with the creation of the Ginnie Mae passthrough and

exploded in the 1980s with the development of the collateralized mortgage obligation. Securitization was
an innovation that had both a financial and a technological component. On the financial dimension,
securitization involved the synthetic creation of a liquid traded security from a pool of illiquid nontraded
assets where often the payoff characteristics are altered significantly from those of the underlying assets.
Securitization also had an important technological component as new information technology allowed for
the efficient compilation, computation and dissemination of information related to the performance and
operation of the asset pools. Securitization spread from the residential mortgage market to many other
types of financial assets including consumer loans and accounts receivable. Since the 1970s the growth in
securitization has been phenomenal with the stock of asset-based securities growing from several hundred
billion dollars to almost $4.5 trillion in 2001. This is almost as big as the entire assets of the banking
industry ($5.7 trillion) (Berger 2003). Securitization has also become an important tool for community
banks to geographically diversify their otherwise locally-concentrated loan portfolios.
An important feature of the securitization market today is the role of two government-sponsored
enterprises (GSEs), the Federal National Mortgage Association (Fannie Mae) and the Federal Home Loan
Mortgage Corporation (Freddie Mac), in the residential. At year-end 2000, investors held $1.2 trillion of

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mortgages securitized by Fannie Mae and Freddie Mac. In addition, Fannie Mae and Freddie Mac held
another $1 trillion dollars of mortgages and mortgage-backed securities directly in their own portfolios
(Passmore, Sparks and Ingpen 2001).
Fannie Mae and Freddie Mac receive an “implicit” government subsidy because investors treat
their debt as if it were backed by a guarantee of the U.S. government. A key public policy issue is
whether this government subsidy affects the competitive structure of the residential mortgage market.
The evidence suggests that Fannie Mae’s and Freddie Mac’s policies have slightly lowered residential
mortgage rates (due to the implicit GSE subsidies) and as a result have encouraged a housing stock that is
inefficiently large (e.g., Hendershott and Shilling 1989, ICF 1990, Cotterman and Pearce 1996, Passmore,
Sparks and Ingpen 2001, White 2003).15
Technology has also had an impact on consumer and micro business lending in terms of the
increasing dependency of these types of lending on credit scoring. First introduced in the 1950s credit
scoring has become widely used in consumer and mortgage lending over the past 30 years (Mester 1997).
According to the Federal Reserve’s 1996 Senior Loan Officer Survey, those banks that use credit scoring
in their credit card business virtually always use it in approving applications. About 80 percent of those
who use credit scoring also use it in determining from whom to solicit applications, and about 20 percent
use it in setting loan terms. The type of model employed will depend on how it is used. Banks increasing
rely on “bureau scores” to solicit and pre-screen applicants. Bureau scores are credit scores based solely
on the history of individuals as reflected in credit bureau reports as opposed to “application scores” that
weigh other factors (e.g., income, employment) in addition to credit bureau information (Avery, Bostic,
Calem and Canner 1999).
Given the paucity of research in this area, it is difficult to quantify the economic impact of credit
scoring on the consumer loan market. For example, we are not aware of any rigorous study that has
examined the improvement in the power of credit scoring since the 1970s. From a statistical standpoint,
the methodology used today (i.e., regression, logit and discriminant analysis) was generally available in
the 1970s. Computational costs are certainly lower today and the data sets are certainly better. However,

12

in the absence of hard empirical evidence, it is not obvious the extent to which these factors would have
been sufficient to drive an economically meaningful improvement in the predictive power of the models
since the 1970s. More fundamentally, it is still an open question whether credit scoring does a better job
of risk assessment than human analysis and by how much. There does not appear to be sufficient
definitive research on this.16

It does seem safe to assert, however, that credit scoring has significantly

reduced the unit cost of underwriting an individual consumer loan, and as a result has increased the
efficient size of consumer loan underwriting operations. It is quite possible that the benefits from credit
scoring are dominated by these cost saving and scale effects.

The 1990s: Industry consolidation, nationwide financial services markets, and widespread adoption of
new banking technology. Banking industry deregulation reached its zenith during the 1990s. In 1994
Congress rationalized the patchwork of state-by-state geographic rules by passing the Riegle-Neal
Interstate Banking and Branching Efficiency Act which effectively repealed the McFadden Act. The
immediate response was the highest-ever five-year run of bank mergers in U.S. history, in terms of both
the number and the value of the banks acquired (Berger, Buch, DeLong, and DeYoung forthcoming).
Only a relatively small number of these M&As were ‘megamergers’ (i.e., combinations of two banks each
with over $1 billion in assets). The majority of mergers were between two community banks, and in the
vast majority of mergers the merger target was a community bank (DeYoung and Hunter 2003).
Feeling pressure from a series of rulings by the Office of the Comptroller of the Currency that
granted increased product powers to national banks, and an announced merger between the largest bank in
the U.S. and one of the world’s largest insurance companies (CitiBank and Travelers), Congress followed
nationwide geographic deregulation with broad-based deregulation of banking powers. Specifically, in
1999 Congress passed the Graham-Leach-Bliley Act which effectively repealed the Glass-Steagall Act.
These congressional acts ratified the decades-long deregulation movement, and as such they
marked the culmination of story lines that began in the 1970s and 1980s. But the true breaking story of
the 1990s was the widespread adoption of new financial and information technologies by almost all U.S.

13

banks. These technologies have been applied differently by, and had different strategic and competitive
implications for, large banks and small banks.
In the 1990s credit scoring was adopted by many large banks in their micro business lending.
Banks have different definitions for this class of lending but the ceiling loan size generally lies between
$100,000 and $250,000. Some banks have used their own proprietary models and others have purchased
credit scoring models from outside venders. In general these models rely on information about the
entrepreneur (e.g., credit bureau reports), mercantile credit information from third party information
exchanges (e.g., Dun and Bradstreet), as well as firm specific information.17 Recent research indicates
that this technology has been associated with an increase in overall lending and that it has enabled banks
to reach a more marginal class of borrowers. This seems to be particularly true when banks use
automated acceptance/rejection and pricing decisions based on credit scores rather than more
discretionary decisionmaking where credit scoring is not the only input. These results could obtain even
if micro-business credit scoring was no better at predicting failure (or even worse) but significantly less
expensive than human due diligence (Frame, Srinivasan, and Woolsey 2001, Berger, Frame and Miller
2002). 18
Information and financial technology has also likely lowered the cost and increased the quality of
third party information exchange, although hard empirical evidence on this is lacking. Research on the
effectiveness of exchange information at the macro level indicates that in countries that have either
private exchanges or public credit registries interest rates and economic growth are higher (Jappelli and
Pagano 1999). Certainly, on both the consumer side (credit bureaus) and on the business side (mercantile
credit information exchanges such as Dun and Bradstreet) the data bases have grown significantly. The
delivery system has also changed. Credit reports and D&B reports can now be sent instantly over the
Internet. As a result lenders can promise quicker turnaround on credit applications which is important in
consumer lending and micro-business lending.
Financial technology has also had a significant effect on how banks manage risk. After the runup in interest rates in the 1970s caught many banks with an asset-liability miss-match, the banking

14

industry began to adopt interest rate risk management techniques to measure their interest rate exposure.
These began with simple GAP-based programs and later evolved into more sophisticated duration-based
programs.19 Advances in financial engineering and the development of new and wider derivatives
markets had a very positive effect on the ability of banks to implement interest rate risk management
strategies. Following some highly visible financial fiascos including Barings PLC, Orange County and
Metallgesellshaft, banks began to implement market risk management tools to measure and manage their
trading risk in the mid-1990s. In the latter half of the 1990s banks began to adopt similar value-at-risk
based tools for managing credit risk.20 The proposed new Basle Capital Accord takes this one step further
using these new credit tools in linking capital requirements to credit risk. An important aspect of all of
these initiatives is the heightened demands they have placed -- and will place in the future under the new
Basle Capital Accord -- on information technology. Banks who opt for the advanced version of the new
Basle capital requirements, for example, will be required to estimate the probability of default (the “PDs”)
and the likely loss given default (the “LGDs”) on all of their loan portfolios over the business cycle (Basle
Committee 2002).21
It is quite possible that the biggest impact of technology on the banking system may have been on
the payments system.

Over the past three decades electronic payments technologies have been

implemented that involve transferring funds electronically with little paperwork.22

One study, for

example, found that the number of checks paid during the second half of the 1990s was falling at a rate of
about 3 percent per year while credit card payments and debit card payments were increasing during this
period by 7.3 percent per year and 35.6 percent per year respectively (Gerdes and Walton 2002, Table 2).
These results indicate that the share of payments by checks to total payments (payments by checks plus
credit cards plus debit cards) fell from 80.8 percent to 64.6 percent. Another study found that while check
use overall continued to rise modestly in the 1990s, it fell dramatically in retail payments (Humphrey
2002). Data in this study also indicate that the share of check payments has been falling - over the ten
years from 1990 to 2000 the share of payments by check to total payments has fallen from 87.8 percent to
72.3 percent.

15

The technology-driven switch from paper-based payments to electronic-based payments is also
reflected in the steep increase in the use of the automated clearing house (ACH). This system is used for
regular payments such as monthly mortgages, direct deposits, etc. ACH volume handled by the Federal
Reserve increased at a 14.2 percent annual rate from 1990 to 2000 (Berger 2003). The impact on cost
reduction for ACH payments was dramatic. Over the 1990-2000 interval the cost declined in real 1994
dollars from $0.959 to $0.158, a reduction of 83 percent (Berger 2003). Reductions in the cost of
processing electronic payments has generally been greater than cost reductions from technology in
processing checks and cash payments where the reductions have been more modest (Bauer and Ferrier
1996, Bohn, Hancock, and Bauer 2001, Gilbert, Wheelock, and Wilson 2002).
Internet banking has been a more recent effect of technology on the banking industry. It is
changing the landscape of the financial services industry by reducing the importance of geography and
reducing the cost of transactions. Banks today offer Internet services in a wide variety of forms including
full transaction sites that allow customers to make deposit and loan transactions on line. Most banks
employ a “click and mortar” distribution model that combines a transactional Internet site with their
traditional brick-and-mortar offices and/or ATM networks.

In its most extreme form, there are a

relatively small number of Internet-only banks that offer their services exclusively on the Internet. As of
July 2002 there were just 20 such Internet-only operations. Approximately another dozen Internet-only
institutions have failed, been acquired or voluntarily liquidated, and in addition several large banks
integrated their Internet-only units into the main bank after poor stand-alone performance.23 Figure 1
suggests a complementarity among all types of bank distribution channels. All three major distributional
channels – ATM machines, bank branches, and transactional Internet banking websites – have increased
over time.
In general, however, Internet banking has become widespread in its “click-and-mortar” form. It
appears that a substantial majority of banks have at least an informational website and close to a majority
now offer transactional Internet sites with virtually all large banks offering them (Furst, Lang, and Nolle
2001, 2002, Sullivan 2001, Berger 2003). Because the basic Internet banking transaction has low variable

16

costs, there are economies of scale associated with this production process and distribution channel
(DeYoung forthcoming).

However, this does not preclude community banks from offering this

technology, because they can outsource both the development and the maintenance of their Internet sites
to website vendors. There is some evidence to indicate that banks, except for the smallest, that have
adopted Internet services are more profitable than those that have not. However, this likely reflects the
type of banks that have chosen the technology rather than the technology itself given that Internet banking
is still a small contributor to overall bank output for most banks (Furst, Lang, and Nolle 2001, 2002,
Berger 2003). The evidence also suggests that the performance of Internet-only bank start-ups was
inferior to traditional de novo bank start-ups, although the former appear to be improving faster than other
banks suggesting that they gain scale and as they ride the learning curve of this technology (DeYoung
forthcoming).
Overall, the increased efficiency that results from a shift from paper-based to electronic-payments
should reduce the amount of transactions balances required by consumers. The data reflected in Table 1
appear to be consistent with such an effect. Over past two decades consumers have reduced the fraction
of their financial assets allocated to transactions accounts, from 7.3 percent in 1983 to 4.6 percent in
2001.
Moreover, the increased efficiency that results from a shift from full service head offices to more
specialized delivery channels (branches, ATMs, websites) should reduce the number of inputs that banks
require to produce a given amount of banking services. The data displayed in Figures 2, 3, and 4 are
consistent with this notion. The number of offices (bank branches plus the head office) per bank has
nearly quadrupled since 1970, while assets per office, deposits per office, and transactions per office have
steadily increased, and FTEs per office has declined.
In general it appears that larger banks have been quicker to adopt new technology than smaller
banks. They have generally been the first, for example, to adopt electronic payments technologies,
transactional websites, small business credit scoring (Berger 2003), ATMs (Hannan and McDowell
1984), securitization and off-balance sheet activities (Berger and Udell 1993). In addition to bank size,

17

however, other factors such market competition and concentration play a role in the adoption of banking
technologies (e.g., Hanna and McDowell 1984, Akhavein, Frame and White 2001, Courchane, Nickerson,
and Sullivan 2002, Gowrisandaran and Stavins 2002, Hauswald and Marquez forthcoming).24

2.3 A Competitive and Rivalrous World for Community Banks in the 2000s
Where does all this change in the banking industry leave the community bank today? In the
1970s community banks arguably had an advantage in a number of different areas. Much of this
advantage stemmed from their local monopoly power. This was particularly true in those states that had
some restriction on state-wide branching – which was a majority of the states in the 1970s. For many
consumers community banks were the portal to the payments system. They also played an important role
as an investment vehicle for consumers.

In addition, community banks were a primary source of

consumer finance. Finally, community banks were the key provider of services to small businesses.
However, the overall role of community banks and the role they play in many of these markets is quite
different today for several reasons.
First, due primarily to thousands of mergers involving community banks in the aftermath of
industry deregulation, there are simply fewer community banks today. The number of banks in the U.S.
with assets less than $1 billion (2001 dollars) has declined from 14,078 banks at the end of 1980 to just
7,631 banks at the end of 2001, and the share of industry assets held by these small banks fell from 33.4
percent to just 16.0 percent. This approximate halving of the presence of community banks in the U.S.
banking industry occurred despite the birth of 4,336 de novo banks during the same time period.25
Second, the revolution in payments technology that we discussed above has disadvantaged
community banks relative to large banks. The payments system has become much more electronic,
diminishing the importance of location. Alternatives to the checking account such as debit cards and
credit cards have reduced the need for bank transactions balances that have historically given community
banks a funding advantage. However, this does not mean that banks – large and small – will not pursue
location for strategic purposes, as we shall discuss below.

18

Third, all depository institutions – not just community banks – have also become less important
as an investment option for consumers. As we just noted, increased efficiency in the payments system
has decreased the need for transactions accounts. But, in addition, the proliferation of investment options
over the past three decades has diminished the relative attraction of savings accounts and certificates of
deposit as consumer investment vehicles. This shift is reflected in Table 1. Ideally we would like to
compare consumer financial assets in 1970, the year before the introduction of money market mutual
funds, with the situation today. By the end of 1982 when money market mutual funds broke through the
$200 billion level, their impact was already enormous. However, as we noted above the SCF was first
conducted in 1983, so 1983 is the earliest date available to examine consumer balance sheets.
Nevertheless, Table 1 shows quite dramatically how much the role banking has changed in terms of the
allocation of consumer assets – even after 1983. The fraction of total financial assets that consumers
allocated to depository institutions (lines 1, 3 and 4) dropped from 22.7 percent in 1983 to 10.3 percent in
2001.26
This issue of whether the role of the banking industry has declined has been a visible topic in the
literature. Typically the analysis has centered around the fraction of all intermediated assets that are held
by depository institutions (e.g., Boyd and Gertler 1994), fraction of total debt (e.g., Berger, Kashyap and
Scalise 1995), banking industry employment (e.g., Berger, Kashyap and Scalise 1995) or bank
profitability (Gorton and Rosen 1995). The problem with the metrics used in these studies is that they do
not focus on specific banking activities where banks are believed to have had an advantage over other
financial intermediaries nor are these particularly good measures of the level of bank activity.27 The
measure used here in Table 1, allocation of consumer assets to depository institutions is focused on a very
specific activity and the metric is based on the users of the service (consumers) rather than the providers
(depository institutions). Based on this metric it appears that banks overall have significantly lost part of
their franchise value. The impact on community banks was arguably greater than the impact on larger
banks because part of community banks’ comparative advantage prior to the repeal of McFadden was the
delivery of transactions services.

19

Fourth, there has been a breathtaking amount of commoditization on the lending side of banking
fueled by both technology and government intervention. As we noted above, today the residential
mortgage market is a securitized market in which government-sponsored enterprises (GSEs) like Fannie
Mae and Freddie Mac are the driving force. The student loan market and substantial chunks of other
consumer loan markets have likewise been securitized.

Like other financial and nonfinancial

commodities (where pricing power is nonexistent), returns to production depend on achieving large scale,
and as a result community banks have virtually dropped out of credit card lending and no longer dominate
mortgage or auto lending. This is illustrated clearly in Table A-1. In 2001, the typical large bank
invested 7.39 percent of its loan portfolios in credit card loans; securitized 19.57 percent of its assets; and
earned 4.36 percent of its noninterest income from loan securitizaton fees. In contrast, these figures were
all less than 1 percent for the typical community bank.
The commoditization of mortgage, auto, and credit card lending can also be seen on the liability
side of the consumer balance sheet. Between 1983 and 1997, debt owed to depository institutions fell
from 59.8 percent to 45.7 percent of total consumer debt, while debt owed to mortgage and real estate
lenders – whose business model is based entirely on securitization – increased from 11.6 percent to 38.0
percent of total consumer debt (see Table 2). It should be noted that much of the debt extended by
mortgage and real estate lenders winds up back on bank balance sheets. This will occur when a mortgage
lender sells a mortgage to a securitized pool and the bank purchases the securitized mortgage.
Nevertheless, even if 100 percent of this paper ended up as bank investments, this would still reflect a
significant loss to most banking franchises, because mortgage lenders would have captured a substantial
amount of the loan origination business from depository institutions. Moreover, the existence of a
secondary market where mortgage lenders can sell their originations has likely sapped much of the
pricing power out of the residential mortgage market. Of course it has also enormously benefited
consumers by transforming illiquid residential mortgages into highly liked traded securities.28
Fifth, as a direct result of deregulation and new technologies in lending, payments, and financial
markets, both large banks and community banks now face much more competitive pressure. The Gramm-

20

Leach-Bliley Act eliminated the barriers that had protected commercial banks, investment banks,
brokerage houses, and insurance companies from competition with each other, and the Riegle-Neal Act
exposed both large and community banks to entry from outside their local markets. The combined effect
of the latter of these two federal laws and earlier interstate compacts has been a near 50 percent reduction
in the number of commercial banks in the U.S. since 1980, and an increase in market share of the ten
largest bank holding companies from 28 percent of U.S. banking assets in 1986 to 76 percent of U.S.
banking assets in 2001.29 Increased geographic competition has upsides for society – for instance, entry
by large banks into previously protected local banking markets creates pressure for local banks to operate
more efficiently (see DeYoung, Hasan, and Kirchhoff 1998; Evanoff and Ors 2001; and Whalen 2001) –
but has obvious downsides for marginally profitable banks that cannot respond to the competitive
challenge. Advances in information technology have made financial markets deeper and broader, making
direct finance (equities, high-yield bonds, commercial paper) more accessible for entire classes of
business borrowers that used to be captive customers of the commercial banking sector. Similarly,
advances in electronic payments are reducing the value of the banking franchise as nonbanks (e.g., credit
card networks) play an increasingly important role in the payments system. Finally, credit scoring and
securitization have transformed the consumer loan production process from a relatively noncompetitive
relationship business to a highly competitive, commoditized transactions business.

2.4 A Continuing Comparative Advantage for Community Banks
There is at least one area of banking that appears to have been relatively unaffected by technology
and deregulation – relationship lending to small business. There are a number of reasons why this line of
business may be relatively unassailable by competition from large banks wielding the latest in new
information and financial technologies. In relationship lending information is gathered by lenders beyond
the relatively transparent data available from financial statements, observation of collateral, and other
public sources. This information is acquired over time by lenders through the breadth and depth of the

21

banking relationship and is used in renewing loans, extending additional credit, renegotiation, and setting
loan terms.30
In the relationship lending segment of the market it is not obvious that technology has had an
economically significant impact on the way loans are underwritten and monitored. Some might argue that
computers and communications technology have fundamentally changed the nature of loan
underwriting.31 The reality, however, may be quite different. For relationship loans in the $250,000 to
$15,000,000 range to informationally opaque business borrowers, the fundamental importance of the
borrower-loan officer relationship has not likely changed that much in past three decades.32 Loan officers
still emphasize the critical importance of personal contact with borrowers and other dimensions of “soft
information”.33
Even with respect to the component of underwriting that is based on “hard information,” the
financial tools to assess credit quality are not much different today than they were in the 1970s. Leverage
ratios, coverage ratios, turnover ratios, and profitability ratios are the same today as they were in 1970s.
Computer spread sheet software makes it a little easier to calculate these ratios, but a good credit analyst
in the mid-seventies could spread a set of financial statements relatively quickly (i.e., minutes not hours),
so the economic impact here is likely minimal. As we noted above, information generated by third party
information exchanges (e.g., Dun and Bradstreet), may be somewhat better. However, on any company
borrowing above $250,000, mercantile credit information in the 1970s was generally available, widely
used by commercial lenders, and generally considered by lenders to be quite informative. In addition,
credit scoring which uses trade credit exchange information as an input is not the primary lending criteria
on loans of this size. The delivery of credit reports is much faster today as we have noted. However, this
is much less important for loans above $250,000 where credit approval is rarely made overnight given the
emphasis on personal contact by the loan officer. And finally, the process of negotiation and the
contracting tools available today (collateral, maturity, covenants, guarantees, subordination etc) are
identical to the tools available in the 1970s.

22

Not all small business loans are primarily relationship-based. For example, about 50 percent of
all small business loans are held by large banks (Strahan and Weston, 1998), but many of these loans are
the credit-scored micro-business loans that we discussed earlier. Also, the asset-based lending and
factoring that we discussed earlier are not relationship-based.34 Finally, some small business lending
involves extending credit primarily based of the strength of the financial statements. These would be
businesses whose financial statements are stronger and more informative, and possibly larger and older.35
Micro-business lending, asset-based lending (including factoring), and financial statement lending are all
primarily based on “hard information” as described above.

For these loans “soft information” is

subordinate in importance. Soft information would include qualitative information about the character of
the entrepreneur and the strength of the company culled from the interaction of loan officer with the
entrepreneur, the entrepreneur’s suppliers, the entrepreneur’s customers, community activities, etc.
However, for relationship loans soft information is of primary importance and hard information is less
important in great part because there is less of it for these loans.
Some recent theoretical work finds that community banks may have an advantage in processing
soft information and extending relationship loans.

The basic argument here is that there are

organizational diseconomies that make it problematic for larger institutions to process and communicate
this information (Stein 2002). Empirical evidence seems to support this view including research that
suggests that the contract terms of business lending at large banks are different than at small banks
(Berger and Udell 1996), that small banks are more likely to base loans on soft information and the
strength of the relationship (Cole, Goldberg and White forthcoming, Berger, Miller, Petersen, Rajan and
Stein 2002, Scott 2004), and that large banks tend to lend at a longer distance where hard information
more likely trumps soft information (Berger, Miller, Petersen, Rajan and Stein 2002).36 Also, there is
compelling evidence that small business lending in general (possibly excluding credit-scored microbusiness lending) is not likely to become commoditized like residential mortgage lending and consumer
lending. Specifically, despite the explosion of securitization in other markets, there has not been an
economically meaningful level of securitization in the small business loan market (Acs 1999). In part,

23

this may be due to the high frequency of renegotiation and the intensity of monitoring associated with
small business lending that could be problematic in a securitization environment.37,38

3. A Strategic Analysis of Community Bank Performance and Viability
In this section we model the impacts that deregulation, technological change, and increased
competition have had on the viability of community banks. We adapt a strategic-map framework from
DeYoung (2000) and DeYoung and Hunter (2003), and we test the theoretical framework against
financial and structural data for U.S. commercial banks from the mid-1970s through 2001. We find
considerable empirical evidence consistent with the theoretical framework. The results of our analysis
indicate that while deregulation and technological change created sobering competitive threats for
community banks, the manner in which large banks have responded to these changes has left well-run
community banks with long-run strategic opportunities.

3.1 A strategic map of the banking industry
In Section 2 we described a myriad of ways that deregulation and technological change have
changed the competitive environment for community banks. At the risk of over-simplification, we will
describe the strategic impact of these phenomena using just three basic parameters: bank size, bank unit
costs, and product differentiation. Following DeYoung (2000) and DeYoung and Hunter (2003), we use
these three parameters to construct the strategic maps displayed in Figures 5 through 8.
The vertical dimension in these maps measures bank size, with large banks at the bottom and
small banks at the top. Because the production of banking services tends to exhibit scale economies, the
vertical dimension also measures unit costs, with low unit costs at the bottom and high unit costs at the
top. The earliest banking scale economy studies concluded that scale economies were fully exhausted by
relatively small banks; most of these studies estimated minimum efficient scale for banks to be less than
$1 billion of assets (2001 dollars). More recent studies have yielded somewhat different insights; many
of these studies conclude that scale economies are available for large regional and super-regional banks.39

24

Part of this difference between these two sets of studies is due to the inferior (though state-of-the-art at
that time) methodologies used by the earlier studies, and part of the difference is due to the fact that new
information and financial technology changed bank production processes over time.
Regardless, an important point of agreement among most of these studies is that small banks
using a traditional banking model (i.e., intermediating transactions deposits into loans held-on-portfolio)
can gain substantial reductions in their unit costs without fully exploiting all available scale economies.
Of course, as banks continue to grow larger, they will gain access to additional reductions in unit costs,
albeit at a declining rate. But the degree to which a bank can reduce its unit costs via additional growth
depends not just on its current size, but can also depend on the type of products it produces. Rossi (1998)
shows that unit cost reductions at financial institutions doing less traditional banking (e.g., high volume
origination and securitization of mortgage loans or credit card loans) continue to be substantial even at
very large scale; this precludes community banks from profitably pursuing specialized strategies in
financial commodities.
The horizontal dimension in these maps measures the degree to which banks differentiate their
products and services from those of their closest competitors. Banks that offer differentiated products and
services (e.g., customized loan contracts, personalized private banking) are located on the right, and banks
that offer nondifferentiated products and services (e.g., standardized mortgage loans, discount online
brokerage) are located on the left. Note that not all product differentiation is tangible – it can often be a
perception in the mind of the customer.

For example, community banks attempt to differentiate

themselves by knowing the names of their customers upon sight, large banks attempt to differentiate
themselves using marketing campaigns to create brand images for otherwise undifferentiated products,
and if successfully deployed both of these strategies can support higher prices for retail banking
services.40
The horizontal dimension of standardization versus customization is also consistent with the
distinction between hard and soft information discussed above (Stein 2002; Berger, Miller, Petersen,
Rajan, and Stein 2002, Scott 2004). This spectrum runs from hard information on the left where banks

25

use automated transaction lending technologies to originate and securitize standardized mortgage or credit
card loans and to deliver credit scored micro-business loans. Moving to the right banks emphasize more
traditional lending technologies such as asset-based lending and financial statement lending. Finally, at
the far right banks specialize in relationship lending where loan officers acquire soft information about
the borrower over time, through a variety of products and services, and through interaction with the local
community.
In this framework, banks select their business strategies by combining a high or low level of unit
costs with a high or low degree of product differentiation. The positions of the circles indicate the
business strategies selected by banks, and the relative size of the circles indicate the relative sizes of the
banks. Figure 5 illustrates the commercial banking industry prior to the deregulation and technological
advances we discussed above in Section 2. All banks were clustered near the northeast corner of the
strategy space. Geographic regulation restricted the size of banks and prevented most (and perhaps all) of
them from fully exploiting available scale economies. The available technology for producing and
delivering banking services required interpersonal contact between loan officers and borrowers to collect
soft information; paper-based transactions for payments; and visits to the bank to receive cash and deposit
checks – all of which required brick-and-mortar bank and branch locations staffed by bank employees.
The level of price competition on the deposit side was restricted on one hand by Regulation Q, and on the
other hand by the lack of substitute liquidity and transactions providers. Retail competition, to the extent
that it existed, was non-price competition – person-to-person service, the convenience of having a branch
nearby, and of course free toasters for opening accounts – rather than price competition. And banks faced
relatively little competition from nonbanks or securities markets for supplying credit to businesses.
The characteristics of retail, small business, and (to a large extent) large business banking varied
little across different sized banks. Small banks tended to offer a somewhat higher degree of person-toperson interaction with retail customers, and large commercial accounts by necessity went to large banks,
but small banks and large banks had more commonalties than differences with each other. For the most

26

part, there was a single retail banking strategy – with some variants – and very little strategic difference
among most banks’ approaches to commercial lending.
But deregulation and technological advances created new strategic opportunities for banks, and as
competition heated up banks had incentives to pursue those opportunities. As discussed above, the
average size of commercial banks began to increase – at first due to modest within-market mergers, and
then more rapidly due to market extension megamergers – and the disparity in bank size within the
industry also increased.41 Although increased size yielded scale economies for small, medium, and large
banks, the largest banks gained access to the lowest unit cost structures.
Large banks also became less like community banks because the size of their operations allowed
them to more efficiently apply the new production technologies discussed above (e.g., automated
underwriting, securitization, widespread ATM networks, electronic payments). This had two effects.
First, it reduced their unit costs even further. Second, it changed their retail banking strategy to a highvolume, low-cost, “financial commodity” strategy. Home mortgages, credit cards, and online brokerage
are three examples of financial services that have become dominated by large and very large financial
institutions, which use hard information and automated production and distribution processes to deliver
these services at low unit costs. Because price competition is strong for nondifferentiated products,
pricing pressure keeps margins low, despite these banks low unit costs. High volumes, constant vigilance
to keep expenses in line, and continuous innovation are essential for this strategy to earn satisfactory
returns for shareholders.
Thus, the incentives created by technology and deregulation drove a strategic wedge between the
large and growing banks on one hand and the smaller community banks on the other hand. The result is
shown in Figure 6. Large banks have moved in a southwest direction on the map, sacrificing personalized
service for large scale, and gaining low unit costs by shifting to automated production techniques.
Although many community banks have also grown larger via mergers, they have continued to occupy the
same strategic ground. By virtue of their small size, local economic focus, and person-to-person ethos,
community banks are well suited to gathering the soft information necessary to deliver highly

27

differentiated small business credit products and high-end consumer banking services.42 If well-managed,
this more traditional strategy should allow community banks to charge high enough prices to earn
satisfactory rates of return, despite their higher cost structures. In this view of the banking industry,
community banks are differentiated from large banks by their “high value-added” strategy.
Before moving on, we must make three additional points before our strategic analysis is
complete. First, the four corners of the strategy space represent the only potentially viable strategic
choices for banks; being “stuck in the middle” of such a map indicates the lack of a strategy, and leads to
financial disaster (Porter 1980). Second, the Northwest corner of the strategy space (high cost, low valueadded) is not a viable strategy, for obvious reasons. And finally, although the Southeast corner of the
strategy space (low cost, high value-added) is the most preferred location, it is unlikely to be a viable
long-run strategy. Without some kind of entry barrier (e.g., patents, monopoly rights), the excess profits
generated at this location will invite entry and the resulting competition will compress margins back to a
normal rate of return. However, the mere existence of this strategic ground, and the excess profits that
banks can earn in the short-run or moderate-run by occupying it, creates an incentive for both large and
small banks to innovate. Moreover, banks that do not strive via innovation to reach this strategic ground
are likely to leave the industry in the long-run.

3.2 Testing the framework against the data
To be sure, Figures 5 and 6 oversimplify the broad changes in the banking industry over the past
three decades and the effects these changes have had on banking strategies. For example, some large
banks offer customized services to certain sets of clients with idiosyncratic financial needs, such as
corporate investment banking clients and high net worth “private banking” customers. Furthermore,
some small Internet-only banks specialize in providing extremely standardized retail banking services.43
But the simplifications in this framework allow us to isolate the main characteristics of community banks
and large banks – small size, local focus, and more traditional banking technology versus large size, broad
appeal, and highly automated banking technology – and in turn to realize that community bank strategies

28

and large bank strategies rely on different profit drivers. If this framework is indeed representative of the
market structure and firm behaviors found in the U.S. banking industry, then addressing the following
question will go a long way to determining the future facing community banks: Is a customized, highvalue-added approach to retail and small business banking financially competitive with a standardized,
commodity-based approach?
Addressing this question requires us to first expose our simple strategic framework to careful
empirical scrutiny. First, are the assumptions embedded in this framework consistent with the data?
Relative to community banks, do large banks have lower unit costs, lower interest margins, use “harder”
information, and sell financial services that are more standardized? Second, are the dynamics of the
framework supported by the data? Have large banks and community banks grown less alike over time in
terms of size, production methods, output mix, and financial structure? Only after addressing these first
two questions in the affirmative can we address the third set of questions to which the framework
naturally leads us: Is the situation illustrated in Figure 6 an industry equilibrium? Or will further changes
be necessary before the industry is in equilibrium? And will that new equilibrium include community
banks as we currently know them?

Question 1: In what ways do community banks and large banks differ today? We refer to the
data in Appendix Table A-1 to examine whether recent differences between community banks and large
banks are consistent with the industry equilibrium depicted in Figure 6.44 The table displays mean values
for a variety of financial ratios and strategy variables for U.S. banks at year-end 2001. The banks are
separated into six peer group categories: large banks, mid-sized banks, large community banks, medium
community banks, and rural banks. Unless otherwise indicated, we use these same category definitions
throughout the remainder of this study.
To be included in the analysis banks had to meet the following criteria: they held a state or federal
commercial bank charter; they were located in one of the fifty states or the District of Columbia; they
were at least ten full years old (DeYoung and Hasan 1998); and they had reasonably traditional bank

29

balance sheets that included loans, transactions deposits, and insured deposits. Urban banks (i.e., banks
located in MSAs) are organized into five asset size categories: small community banks with assets less
than $100 million; medium community banks with assets between $100 and $500 million; large
community banks with assets between $500 million and $1 billion; mid-sized banks with assets between
$1 and $10 billion; and large banks with more than $10 billion in assets. Rural banks are included as a
separate category because of their special role in providing agricultural credit and because they tend to
face less competition in the rural towns in which they are located; however, rural banks use a business
model very similar to community banks, and for most purposes can be considered to be community
banks. Banks in the rural bank category and the three community bank categories had to meet the
following additional conditions: they were domestically owned; credit card receivables comprised no
more than ten percent of their loan portfolios; they derived at least half of their deposits from branches
located in a single county; and they were organized as either an independent bank, the sole bank in a onebank holding company, or an affiliate in a multibank holding company comprised solely of other
community banks.
Note that these six peer group categories do not collectively contain the full population of U.S.
commercial banks in any given year. For example, at year-end 2001 the FDIC reported that there were
8,080 commercial banks operating in the U.S., while our sample selection process and peer group
definitions exclude 1,416 of these banks, leaving us with a sample of 6,664 banks for 2001. Also note
that our analysis of bank strategies and financial performance is based on bank-level data (largely from
the Call Reports) rather than bank holding company-level data. We choose to compare the performance
of community banks – most of which are not affiliated with multibank holding companies – to the
performance of other community and non-community banks at the same level of organization.
Obviously, community banks by definition are smaller than “large” banks. But the magnitude of
the size disparity displayed in Table A-1 is staggering: the average $60 billion large bank is on the order
of 100 times larger than the average large community bank; 300 times larger than the average medium
community bank; and 1200 times larger than the average small community bank. These huge size

30

differences are consistent with the strategic situation depicted in Figure 6, and suggest that large banks
may have access to a different set of business strategies than community banks. Indeed, the data in Table
A-1 indicate that large banks take advantage of their size to produce a different mix of financial services
than community banks, and use different production, distribution, and corporate organization technologies
to do so. These documented differences are fully consistent with the assumptions embedded in our
strategic framework.
On average, the ratio of loans-to-assets differs very little across the different bank categories,
ranging between 60 percent and 65 percent. The composition of loans varies greatly, however, as does
the manner in which these loans are produced and distributed. The most striking difference can be seen
by comparing credit card loans to small business loans. Credit card loans (also included in consumer
loans) comprise nearly 10 percent of all loans held by large banks, but less than two percent of loans held
by community banks and rural banks.45 In contrast, small business loans (commercial and industrial loans
with principal amounts at origination of less than $1 million) comprise only about 5 percent of all loans
held by large banks, but as much as 17 percent of loans held by community banks. (Small agricultural
loans comprise almost 14 percent of rural bank loans.) This evidence is consistent with the idea that large
banks tend to engage in transactions lending while community banks tend to engage in relationship
lending. Moreover, there is direct evidence that transactions lending is central to the business strategies
of large banks: about 23 percent of large bank assets are sold and securitized (with loan servicing rights
retained, or with recourse or other seller-provided credit enhancements) during the course of the year,
compared to less than 1 percent of community bank assets; and about 6 percent of large bank noninterest
income comes from securitization fees, compared to about 1/10th of 1 percent of noninterest income at
community banks. 46
Large banks and community banks differ substantially on the right-hand-side of the balance sheet
as well. Community banks and rural banks finance between 81 and 86 percent of their assets, on average,
using deposits, compared to only about 56 percent for large banks.47 Large banks make up the difference
by purchasing federal funds from other banks, issuing subordinated and nonsubordinated debt, and selling

31

commercial paper. As opposed to raising funds by issuing deposits, this is pure financing activity with no
possibility of generating service charges or other income generated from depositor relationships. The
composition of deposits also differs systematically across bank categories. Core deposits (transactions
deposits plus small time deposits) comprise only 34 percent of total deposits at large banks, but this ratio
increases steadily and substantially as banks get smaller: 39 percent at mid-sized banks; 44 percent at
large community banks; 57 percent at medium community banks; 65 percent at small community banks;
and 67 percent at rural banks. This pattern is telling – core deposits are largely insured deposits, are
unlikely to leave the bank in the short-run, and as such represent a base of customers with which a bank
can potentially build relationships.
Despite these differences in funding, the ratio of interest expense-to-assets varies very little across
urban banks, declining only slightly with asset size from around 3% for community banks, to 2.90% for
mid-sized banks, to 2.77% for large banks.48 Community banks more than recover this 23 basis point
disadvantage by earning higher ratios of interest income-to-assets: 6.92% for small community banks
versus only 6.064% for large banks. The net result is substantially larger interest margins of between
3.72% and 3.96% for community banks, compared to only 3.29% for large banks.49 Although interest
rates earned vary with the composition of invested assets, high interest margins (all else equal) are
consistent with a “high value-added” personalized banking strategy and low interest margins are
consistent with a “high volume, low cost” transactional banking strategy.
Another telling difference is noninterest income, which is equal to 2.49 percent of assets at large
banks – notable because it makes nearly as large a contribution to paying bank overhead as the net interest
margin, and also because it dwarfs the noninterest income generated by community and rural banks which
ranges between just 0.67 to 1.05 percent of assets. The composition of noninterest income shows that this
disparity reflects a basic strategic difference between these two sets of banks. Service charges on deposit
accounts comprise between 41 percent and 63 percent of noninterest income at community and rural
banks, but amount to only 20 percent of the noninterest income generated by large banks. (And this
disparity is even more substantial than it at first appears, given that the fee structure on deposit accounts

32

works in the opposite direction: income from service charges is only about 2-to-3 cents per dollar of
transactions deposits at community and rural banks, but is in the 4-to-5 cent range at large and mid-sized
banks.) At large banks, service charges are just one part of a broader portfolio of traditional and
nontraditional activities that includes substantial amounts of noninterest income from securitizaton,
investment banking, trading, and fiduciary activities.
This broad mix of financial activities at large banks has implications for organizational form. The
large size and scope of these banks makes a multibank holding company organizational form more
efficient. About 65% of the large banks are affiliates in MBHCs, compared to between 7% and 17% of
rural and community banks. A MBHC structure allows retail banking, credit card banking, investment
banking, insurance activities, etc. to be separately capitalized and managed.
Sales and management of mutual funds is another indicator of the less traditional business
strategy practiced by large banks. About 82 percent of large banks sell mutual fund investments to their
customers (versus 16 percent to 53 percent of community and rural banks) and 50 percent of large banks
manage and sell their own proprietary mutual funds. Mutual funds are a good example of financial
services that can complement both of the strategic approaches depicted in Figure 6: as part of a hightouch customer relationship in which the bank provides personalized investment advice, or as one of
many items on a menu offered by a high-volume, low-frills financial outlet. To be sure, some large banks
have built their franchise on the former approach (e.g., Northern Trust in Chicago and U.S. Trust in New
York). But the latter approach is more consistent with the large amount of transactions-based loans and
purchased deposits found at the average large bank.
The manner in which financial services and products are delivered to customers also varies
substantially between large banks and community banks. Almost all (96 percent) large banks operate
transactional Internet websites, compared to only 7 percent, 32 percent, and 54 percent of small, medium,
and large community banks. A transactional website (at which customers can pay bills, transfer funds,
make investments, apply for loans, etc.) can entirely obviate visits to brick-and-mortar banks for
customers happy with standardized financial services and a low-touch banking “relationship.” But for

33

relationship customers that need more highly customized financial services or prefer a more personalized
approach, an Internet website can only complement, not replace, brick-and-mortar branches. Community
banks operate considerably more physical office locations (branches plus head office) per dollar of assets,
dollar of deposits, and number of deposit accounts, than do large banks. Community banks also employ
more workers relative to these same measures of output than large banks, despite the fact that they
employ considerably fewer employees per office.
It is not clear whether the low ratios of assets-per-FTE, deposits-per-office, and accounts-peroffice at community banks reflect inefficient management or diseconomies of scale.50 Nevertheless, the
issue of economies of scale is central to our strategic framework. Any scale-based reduction in unit costs
provides a competitive advantage for the large bank business strategy, while at some increased size can
hamper the application of a locally focused, highly personalized community banking strategy.
Unfortunately, the data in Table A-1 do not allow us to directly compare the unit costs of large banks and
community banks. Unit costs can vary greatly with business strategy (for example, idiosyncratic small
business loans are more expensive to originate and monitor than transactions loans like credit cards and
home mortgages), and unit costs can vary greatly with how efficiently a bank is operated (the average
large bank is likely to be better run, all else equal, than the average community bank because it has access
to better quality managerial talent and as a publicly traded firm faces pressure from the capital markets to
perform).

Some of the more recent studies on bank scale economies use stochastic cost frontier

techniques, which when used correctly can control for both of these effects. Although this literature
(discussed above) has not yet reached a complete consensus, there is broad agreement across studies that
growth can generate substantial reductions in unit costs for the smallest banks. The extent to which the
trade-off between lower costs and local focus favors large banks over community banks, or large
community banks over small community banks, should show up in bank earnings, which we explore
below in our analysis of Question 3.
For two sets of banks with such different input and output mixes, it is not surprising that large
banks and community banks also have different risk profiles. At the end of 2001 a greater proportion of

34

loans at large banks were nonperforming (about 0.9 percent versus 0.6 to 0.7 percent at community
banks) and the allowance for loan losses was much larger (about 5 percent versus 1 to 2 percent) at large
banks than at community banks. These differences may be attributable to a variety of phenomena,
including differences in underwriting techniques, differences in appetite for risk-taking, large bank
participations in large loans underwritten by other banks, or greater capacity at large diversified banks to
sustain loan losses.51 Despite the larger levels of credit risk – or perhaps because of a greater ability of
large banks to boost loan loss reserves or manage risk with derivative instruments – the Tier 1 risk-based
capital ratio was only 12.61 percent at large banks, compared to 17.52 percent at small community banks
and 18.84 percent at rural banks. The high amount of noninterest income at large banks can also be a
source of increased earnings volatility (DeYoung and Roland 2001, Stiroh 2003, 2004).
A final, telling difference between large banks and community banks in Table A-1 is the amount
of resources they expend on advertising and marketing. Advertising and marketing expenditures at large
banks are equal to 0.11 percent of assets, two-and-a-half to three times more than expenditures at
community banks or even at mid-sized banks. While the local focus and personal touch of community
banks allows them to rely more on word of mouth and local media, the broad focus and transactionsbased practices of large banks requires (ironically) large banks to spend more money to get noticed. A
large advertising budget is also consistent with retail advertising aimed at creating differentiation via
brand image.

Question 2: Have community banks and large banks grown different over time? The data presented in
the previous section provide strong evidence that community banks and large banks use different business
strategies. Have these two types of banks always used such different business strategies?

Or, as

suggested by Figure 6, have large banks and community banks become more different as deregulation and
technological change have driven a “strategic wedge” into the banking industry? We offer support for
this strategic analysis by showing that the major parameters of the strategic framework have been
diverging over the past decade for large banks and community banks.

35

Unlike our very thorough investigation of Question 1, we use only a relative handful of financial
ratios and strategy indicators to address Question 2. Bank regulators only recently began to collect some
of the most interesting strategic characteristics of banks (e.g., mutual fund sales, securitization activities,
advertising expenditures, small business lending). This precludes the construction of the long time series
of ratios necessary to test whether and how these bank characteristics have varied over time.52 But the
relative changes in large banks and community banks over the past decade have been clear and
unmistakable, and only a handful of data are needed to illustrate this point.
The most fundamental strategic difference in Figure 6 is the growing disparity in the size of large
banks and community banks, driven by (a) deregulation allowed banks to grow via acquisition across
geographic borders, and (b) new information and financial technology that allowed banks to produce
loans and other financial services more efficiently at large scale. The 1991-2001 asset size data displayed
in Table 3 are consistent with this. The entire size distribution of banks shifted up during the 1990s, but
increased size among the larger banks dominated. For example, in the bottom half of the size distribution
bank size increased by between 23 and 46 percent, while in the top half of the distribution bank size
increased by between 46 and 76 percent. Moreover, the size differences between the largest and smallest
banks widened, with the largest relative changes occurring at the very top of the distribution. For
example, in 1991 the bank at the 99th percentile was 786 percent larger than the bank at the 95th
percentile, and by 2001 this difference had widened to 931 percent.
As discussed above, the literature on bank scale economies finds that increased asset size
translates into lower unit costs, holding output mix constant. But as we also discussed above, bivariate
comparisons of unit costs across categories of banks do not hold output mix constant. So we cannot,
without performing an analysis well beyond the scope of this investigation, responsibly investigate
whether, and by how, the difference in unit costs between large banks and community banks widened
during the past several decades. However, we can investigate whether more easily measurable bank
characteristics like loans balances, deposit balances, and noninterest revenue have diverged across time
for large banks and community banks.

36

Figures 9, 10, and 11 display indexed time series for, respectively, loans-to-assets, core depositsto-assets, and noninterest income-to-operating income between 1991 and 2001. Each of the figures show
time series for six categories of banks, all of which are indexed to equal 1.00 in 1991. Figure 9 displays
time series for loans held on the balance sheet, the most traditional of all banking products. The figure
clearly shows that community banks and rural banks have invested more heavily in portfolio loans over
time relative to larger banks. Figure 10 displays time series for core deposits, the most traditional of all
banking inputs. The figure clearly shows that large and mid-sized banks – and to a lesser degree, large
community banks – became less reliant on core deposits during the decade, while small community
banks, medium community banks, and rural banks remained very reliant on core deposits. Figure 11
displays time series for noninterest income, which increasingly is an indicator of nontraditional banking
activities. This data in this figure are noisy, but a relatively clear story still emerges for large banks
versus community banks. The figure shows that large banks have become more reliant on noninterest
income over time, while the three categories of community banks have become less reliant on noninterest
income.53 Taken together, the data in Table 3 and Figures 9 through 11 offer clear support for the
“strategic wedge” part of our strategic analysis.

Question 3: Is the community banking strategy profitable in the long-run? We have provided plenty of
evidence that community banks are using a different business strategy than large banks; that the strategies
used by these two types of banks have been diverging over the past decade; and furthermore, that the two
strategies continue to diverge today. Because the evidence suggests that large banks are purposely
moving away from the traditional strategic ground held by community banks – in terms of increased asset
size, less reliance on relationship-based core deposits, and more reliance on nontraditional financial
services as sources of income – it would seem logical that the business strategy being pursued by large
banks is at least as profitable as the one they are abandoning. The crucial question, then, is whether the
more traditional community banking strategy being abandoned by large banks remains a profitable one?

37

The 4,000 de novo commercial banks that were chartered over the past two decades suggest that
banking entrepreneurs believe the community bank strategy is profitable. Recent studies find that de
novo banks are more likely to start-up in markets where large out-of-market banks have purchased local
banks (Berger, Saunders, Scalise, and Udell 1998; Keeton 2000; Berger, Bonime, Goldberg, and White
forthcoming) and to some extent in markets where two incumbent banks have combined (Seelig and
Critchfield 2003).54 In addition, these new bank start-ups (along with local incumbent banks) are likely to
gain additional small business clients by picking up business jettisoned by the recently acquired target
banks (Berger, Saunders, Scalise, and Udell 1998). All of these results are consistent with our strategic
analysis that large banks have abandoned traditional relationship lending as they have grown larger via
mergers. Figure 7 uses the strategic map to illustrate this “de novo backlash” phenomenon.
The profitability and risk ratios analyzed above suggest that there are systematic differences in
financial performance across banking strategies. Table 4 displays the distributions of average ROE, the
standard deviation of ROE, and the Sharpe Ratio for our six categories of banks, based on annual data for
the banks that operated in every year from 1995 through 2001.55 The community and rural banks
generate lower ROE than the large banks at every point in the distribution. (Note that these are just
numerical differences, not statistical tests.) Of course, these ROE data are not adjusted for risk, and hence
may not be directly comparable across banks with different business models if those strategies require
banks to take different amounts of risk. Interestingly, community bank and rural bank ROE are less
volatile at all points of the distribution than large bank ROE, suggesting that the risk-adjusted returns
earned by community and rural banks may be relatively comparable to returns earned by larger banks.
Indeed, the Sharpe Ratio indicates that risk-adjusted ROE at large community banks and medium
community banks actually exceeds risk-adjusted large bank ROE throughout the distribution. However,
at small community banks risk-adjusted – and to a lesser extent at rural banks – risk-adjusted returns tend
to fall short of large banks. 56
These poor results for small community banks and rural banks do not necessarily indicate that
these business models are less profitable than the large bank business model. The data in Table 4

38

combine two sets of banks within each category: well-managed banks that do a good job of implementing
their chosen banking strategy, and poorly-run banks that implement that strategy inefficiently.57 Table 5
crudely controls for the possibility that banks in the latter group are dragging down the average financial
performance for the entire group. The table separates each group of banks into two halves, above and
below the median ROE for the group. The banks in each of the upper subgroups might be considered
“best-practice” users of their particular business strategy. Nine different financial ratios are calculated for
each subgroup, and the means of these subgroup financial ratios are reported in the table. Best-practices
ROE at large and medium community banks (17.25% and 16.14%) compare favorably to the overall
average ROE at large and mid-sized banks (15.45%), but best-practices ROE at small community banks
and rural banks does not (14.17% and 13.51%). This is at least partly due to less financial leverage
(higher equity-to-assets ratios) at these small banks, as indicated when the earnings comparisons are made
based on ROA rather than ROE: the best-practices ROA for the rural banks and all of the community
banks exceeds the average ROA for the large and mid-sized banks. But the fact that best-practices rural
and small community banks earn lower ROE and ROA than best-practices medium and large community
banks suggests that these small banks are penalized by their low scale of operations.
There is a dramatic disparity in both ROE and ROA between the best-practices and “worstpractices” community banks and rural banks. However, it is instructive that both the best-practices and
worst-practices community and rural banks have high (and quite similar) levels of core deposits and small
business loans – two characteristic elements of the community bank business model. This is strong
evidence that the community bank business model is viable, but that it takes a well-run organization to
make it work. Table 5 also shows that best-practices community banks lend out larger proportions of
their assets; generate higher amounts of noninterest income; earn higher net interest margins; and notably,
have substantially lower accounting efficiency ratios (noninterest expense as a percentage of operating
income).
While the data comparisons in Tables 4 and 5 are crude, they are strongly suggestive that the
community bank business model is economically viable. However, the data also suggest that a large

39

number of community banks are not operating the model in a fully profitable manner, due to a
combination of low scale and poor management practices.

4. Conclusions – Whither the Community Bank?
On balance, the evidence provided here suggests that the community bank business model is
economically viable. But it is important to understand the limitations of this conclusion. This does not
mean that community banks can profitably compete in every segment of the financial services market.
Certainly there are some markets that community banks cannot play in, and never have: capital markets
products (e.g., underwriting corporate debt and equity issues, writing backup lines of credit to support
commercial paper offerings) and large shared commercial credits are two examples. Rather, it suggests
that a community bank business model that emphasizes personalized service and relationships based on
soft information is likely to be viable in the long run. This also does not mean that all community banks
will be financially successful. The data are clear in their indication that efficient community banks can be
viable rivals with larger banks in providing financial services to retail consumers and small business
clients. Finally, the data indicate that size does matter for community banks. Although our analysis does
not compare unit costs across different sizes of banks, combining what we know from the bank scale
economy literature with the profitability analysis performed here indicates that the smallest community
banks (less than $100 million in assets) have to be hitting on all cylinders to overcome their size
disadvantages and earn returns comparable to other community banks, much less comparable to large
banks.
All in all, the data offer strong support for the strategic map analysis depicted above in Figures 5,
6, and 7 and the new industry equilibrium that it suggests. But the banking industry will not stand still.
To a large extent the survival of community banks in the future depends on the ability of large banks to
increase the personalization and customization of their services, while still maintaining their low unit cost
advantage. As illustrated in Figure 8, large banks that can do that will be moving toward the Southeast
corner of the strategic map; a successful move by large banks in that direction will make it very difficult

40

for community banks to compete. How might large banks be able to do this? One real possibility is for
large banks to compete head-to-head with community banks by expanding their networks of brick-andmortar branches into local neighborhoods. This scenario is currently being played out in the Chicago
market, where Bank of America, Bank One, Harris Bank, LaSalle Bank and Washington Mutual are
(combined) in the process of constructing over one hundred new brick-and-mortar branches. Operating at
a larger number of more convenient locations, combined with the advantages of large size, may permit
these large banks to infringe on the “high-value-added” portion of the strategy space currently occupied
by community banks, and is crucial to their ability to cover their high cost structures.
Of course, community banks can take action to move closer to the Southeast corner as well. One
possibility is to take advantage of scale without getting large. Community banks may be able to capture
technology-based scale savings by carefully outsourcing applications – like loan securitization, brokerage,
or their Internet website – to nonbank financial services venders. The key is to act larger, while still
maintaining their high value-added approach – and not losing the customer relationship to the vender in
the process.
To close, we offer an oblique answer to the $64,000 question: How many community banks will
there be in the future? Attempting to answer this question is a fool’s game, of course, so we will be very
circumspect. Earlier (and noble) attempts at projecting the number of banks in the future have all missed
the mark for one unpredictable reason or another (e.g., Nolle 1995; Berger, Kashyap, and Scalise 1995).
These studies were based on extrapolations of industry structural trend lines into the future.58 We propose
a different methodology that is based in part on the logic of our strategic map analysis. We start with the
current population of community banks, and gradually remove the least profitable community banks from
the data set, until the average ROE of the community banks remaining is at least equal to the average
ROE of the current population of large banks.
This is an extremely simple approach, and it relies on two basic assumptions. First, that the
current population of large banks is stable, but that more community banks still need to exit the industry.
Second, that bank investors will move their capital out of relatively unprofitable banks – in the banking

41

industry this typically happens via acquisition by a different bank. Neither of these assumptions is fully
realistic, but neither is it pure fantasy.
Table 6 shows the results of this approach. It is necessary to remove the least profitable 40
percent of large community banks before the median ROE of this group of banks becomes equal to the
median ROE at large and mid-sized banks. Similarly, 60 percent of the medium-sized community banks,
70 percent of the small community banks, and 80 percent of the rural banks must be removed before these
groups hit the large bank profitability threshold. Scale economies are obviously an issue here, combined
with X-inefficiency.
This exercise is meant to be instructive rather than predictive. Its results can be extremely
sensitive to the manner in which it is parameterized. For example, when we replace ROE with the Sharpe
Ratio in Table 6, only 40 percent of the small community banks, and only 20 percent of the rural banks,
exit the industry. The results will also be sensitive to the existence of X-inefficiency among large banks;
the degree to which small banks, and especially rural banks, face the competitive pressure necessary to
force poor performers from the market; the non-profit-maximization motives of many community bank
owner-operators; and the appropriate risk-adjustments to make across different banking strategies. Future
research on the viability of community banks may be able to better identify some of these parameters. On
the other hand, depending on the relative pace of industry consolidation and the production of new bank
research, the marketplace may simple provide these answers for us.

42

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49

Table 1
Value of Consumer Financial Assets
Value of Financial Assets (Percent)
1. Checking and money market deposit accounts
2. Money market mutual funds, and brokerage call accounts
3. Savings accounts
4. Certificates of deposit
5. Savings bonds
6. Bonds
7. Stocks
8. Mutual funds (excluding MMMFs)
9. Retirement accounts *
10. Cash value of life insurance policies
11. Other managed assets
12. Other

1983
7.3
8.9
5.2
10.2
0.8
8.7
26.1
2.9
9.5
7.7
11.8
0.9

2001
4.6
4.5
2.6
3.1
0.7
4.5
21.5
12.2
28.2
5.3
10.9
2.0

Total
100.0
100.0
Source: Federal Reserve Board Survey of Consumer Finance
* Includes money market accounts that are held in 401(k) and other retirement accounts.

Table 2
Consumer Debt
Amount of Debt (Percent)
1. Commercial bank
2. Thrift institution
3. Credit union
4. Total depository institutions
5. Finance or loan company
6. Brokerage
7. Mortgage or real estate lender
8. Individual lender
9. Other nonfinancial
10. Government
11. Credit card and store card *
12. Pension account
13. Other

1983
28.5
29.1
2.2
59.8
3.6
3.1
11.6
12.3
1.9
4.7
0.0
NA
2.8

2001
34.1
6.1
5.5
45.7
4.3
3.1
38.0
2.0
1.4
1.1
3.7
.3
.5

Total
100.0
100.0
Source: Federal Reserve Board Survey of Consumer Finance
* Credit card and store card debt for 1983 was 0.03 percent of consumer debt.

50

Table 3
Selected percentiles from the asset size distribution of U.S. commercial banks, 1991-2001.

Asset size percentiles (2001 dollars)
1991
2001
%
change

99th
$5,993,780
$10,523,563

95th
$676,367
$1,019,929

90th
$312,681
$486,744

75th
$132,358
$201,085

50th
$62,349
$91,244

25th
$31,659
$46,058

10th
$18,366
$25,169

5th
$13,763
$18,017

1st
$7,559
$9,333

75.57%

50.80%

55.67%

51.93%

46.34%

45.48%

37.04%

30.91%

23.47%

Percentage differences across asset size percentiles

1991
2001

99th to
95th
786.17%
931.79%

95th to
90th
116.31%
109.54%

90th to
75th
136.24%
142.06%

75th to
50th
112.28%
120.38%

50th to
25th
96.94%
98.11%

25th to
10th
72.38%
82.99%

Source: Authors’ calculations using data from the Call Reports (FDIC).

51

10th to
5th
33.45%
39.70%

5th to
1st
82.06%
93.05%

Table 4
Distribution of Mean Return on Equity (ROE), Standard Deviation of ROE, and Sharpe Ratio.
All calculations based on 7 years of annual data from 1995-2001. All data is in 2001 dollars. Sharpe
Ratio = (mean ROE – mean one-year constant maturity T-Bill rate)/standard deviation of ROE. # (*)
indicates number is higher (lower) than number in first row for large banks. Large banks have more than
$10 billion in assets. Mid-sized banks have between $1 billion and $10 billion in assets. Large
community banks have between $500 and $1 billion in assets. Medium community banks have between
$100 and $500 million in assets. Small community banks have less then $100 million in assets.

10%

Distribution percentiles
25%
50%
75%

90%

N

ROE
Large banks
Mid-sized banks
Large community banks
Medium community banks
Small community banks
Rural banks

0.1085
0.0919*
0.0936*
0.0762*
0.0510*
0.0603*

0.1259
0.1224*
0.1176*
0.0966*
0.0776*
0.0825*

0.1527
0.1507*
0.1419*
0.1204*
0.1001*
0.1044*

0.1804
0.1815#
0.1637*
0.1504*
0.1328*
0.1313*

0.2226
0.2274#
0.1934*
0.1859*
0.1820*
0.1605*

52
147
91
689
825
2979

Standard deviation of ROE
Large banks
Mid-sized banks
Large community banks
Medium community banks
Small community banks
Rural banks

0.0183
0.0109*
0.0089*
0.0081*
0.0098*
0.0082*

0.0260
0.0170*
0.0122*
0.0122*
0.0154*
0.0128*

0.0407
0.0298*
0.0187*
0.0198*
0.0253*
0.0199*

0.0612
0.0471*
0.0338*
0.0343*
0.0427*
0.0319*

0.0878
0.0736*
0.0517*
0.0550*
0.0771*
0.0504*

52
147
91
689
825
2979

Sharpe Ratio
Large banks
Mid-sized banks
Large community banks
Medium community banks
Small community banks
Rural banks

0.6140
0.9361#
1.5505#
0.8372#
-0.1238*
0.2980*

1.4595
1.9241#
2.7718#
1.9503#
0.8305*
1.3232*

2.5615
3.3830#
4.6071#
3.5087#
2.1576*
2.6394#

4.0340
5.8626#
7.2858#
5.5110#
3.6891*
4.2766#

5.4199
9.0639#
9.2595#
8.3077#
5.4660#
6.6798#

52
147
91
689
825
2979

Source: Authors’ calculations using data from the Call Reports (FDIC).

52

Table 5
Mean financial ratios for above median and below median ROE subsamples in 2001.
Large banks have more than $10 billion in assets. Mid-sized banks have between $1 billion and $10
billion in assets. Large community banks have between $500 and $1 billion in assets. Medium
community banks have between $100 and $500 million in assets. Small community banks have less then
$100 million in assets. For rural banks, small farm production loans are used in place of small business
loans.

Mean for large and mid-sized banks

Large community banks
Medium community banks
Small community banks
Rural banks

ROE

ROA

Equity/Assets

0.1545
below
above
median
median
ROE
ROE
0.1725
0.1115
0.1614
0.0888
0.1417
0.0597
0.1351
0.0699

0.0125
below
above
median
median
ROE
ROE
0.0140
0.0106
0.0143
0.0094
0.0128
0.0070
0.0131
0.0089

0.0864
below
above
median
median
ROE
ROE
0.0833
0.0961
0.0895
0.1055
0.0940
0.1110
0.0992
0.1253

0.6156
above
below
median
median
ROE
ROE
0.6360
0.5672
0.6320
0.5738
0.5967
0.5441
0.5970
0.5198

Noninterest Income/
Operating Income
0.2853
above
below
median
median
ROE
ROE
0.2349
0.1935
0.1801
0.1610
0.1639
0.1487
0.1395
0.1222

Noninterest Expense/
Operating Income
0.6083
above
below
median
median
ROE
ROE
0.5773
0.6351
0.5921
0.6721
0.6361
0.7498
0.5941
0.6759

Net Interest Margin

Core Deposits/Assets

0.0371
below
above
median
median
ROE
ROE
0.0380
0.0380
0.0429
0.0392
0.0426
0.0404
0.0404
0.0384

0.3267
below
above
median
median
ROE
ROE
0.4558
0.4255
0.5215
0.5179
0.5858
0.5721
0.5909
0.5908

Loans/Assets
Mean for large and mid-sized banks

Large community banks
Medium community banks
Small community banks
Rural banks

Mean for large and mid-sized banks

Large community banks
Medium community banks
Small community banks
Rural banks

Source: Authors’ calculations using data from the Call Reports (FDIC).

53

Small Business
Loans/Total Loans
0.0505
below
above
median
median
ROE
ROE
0.1201
0.1375
0.1520
0.1409
0.1702
0.1467
0.1479
0.2005

Table 6
Median ROE and Sharpe Ratio comparisons for subsamples of community banks.
# (*) indicates number is higher (lower) than the benchmark number for Large and Mid-sized banks. All data is in 2001 dollars. Large banks have
more than $10 billion in assets. Mid-sized banks have between $1 billion and $10 billion in assets. Large community banks have between $500 and
$1 billion in assets. Medium community banks have between $100 and $500 million in assets. Small community banks have less then $100 million
in assets.
Benchmark: Median ROE for combined large
and mid-sized bank samples
Median ROE for community bank
subsamples:
Large community banks
Medium community banks
Small community banks
Rural banks
Benchmark: Median Sharpe Ratio for
combined large and mid-sized bank samples
Median Sharpe Ratio for community bank
subsamples:
Large community banks
Medium community banks
Small community banks
Rural banks

0.1511

0.1511

0.1511

0.1511

0.1511

0.1511

0.1511

0.1511

0.1511

all

top 90%

top 80%

top 70%

top 60%

top 50%

top 40%

top 30%

top 20%

0.1419*
0.1204*
0.1028*
0.1076*

0.1439*
0.1249*
0.1069*
0.1121*

0.1461*
0.1312*
0.1136*
0.1166*

0.1493*
0.1382*
0.1199*
0.1217*

0.1545#
0.1449*
0.1272*
0.1268*

0.1637#
0.1506*
0.1359*
0.1339*

0.1696#
0.1600#
0.1483*
0.1409*

0.1773#
0.1687#
0.1614#
0.1502*

0.1938#
0.1859#
0.1855#
0.1634#

3.1037

3.1037

3.1037

3.1037

3.1037

all

top 90%

top 80%

top 70%

top 60%

4.6071#
3.5087#
2.3055*
2.8216*

5.2035#
3.8248#
2.5706*
3.0484*

5.5925#
4.1384#
2.7837*
3.3257#

6.1930#
4.5375#
3.0539*
3.6948#

6.8114#
5.0604#
3.4665#
4.0513#

Source: Authors’ calculations using data from the Call Reports (FDIC).

54

Figure 1
Distribution channels for U.S. Commercial Banks, 1991-2001.
14,000

350,000

12,000

300,000
Banks (left)

0

2001

50,000

2000

2,000

1999

100,000

1998

4,000

1997

ATMs (right)

1996

150,000

1995

6,000

1994

200,000

1993

8,000

Transactional
Websites (left)
Branches (right)

1992

250,000

1991

10,000

0

Source: Data on banks and branches from FDIC website. Data on transactional websites from
internal FDIC records. Data on ATMs from Bank Network News annual Data Book.

Figure 2
FTEs and Offices at U.S. Commercial Banks, 1970-2001.
30

10

28

8

26

6
FTEs per Office (left)
Offices per Bank (right)

2000

1997

1994

1991

1988

1985

0

1982

20

1979

2

1976

22

1973

4

1970

24

Offices = number of full service physical locations (branches plus the head office).
FTEs = number of full time equivalent employees.
Source: Authors’ calculations using data from FDIC website and Call Reports.

55

Figure 3
Output per office, U.S. Commercial Banks, 1970-2001.

$100,000

$6,000
Assets per Office

$75,000

$4,500

1997
2000

1988
1991
1994

$1,500

1982
1985

$25,000

1973
1976
1979

$3,000

1970

$50,000

Deposits per Office
Operating Income per
Office (right)

Source: Authors’ calculations using data from FDIC website and Call Reports.
Figure 4
Number of payments transactions per office in the U.S., 1987-2000.
Offices include branches and main offices of banks, thrifts, and credit unions. Transactions include
checks, debit cards, credit cards, direct debits, and direct credits.

millions of transaction

1.2
1.0
0.8

transactions per office

0.6

transactions per office
excluding checks

0.4

1999

1997

1995

1993

1991

1989

0.0

1987

0.2

Source: “Statistics on Payments and Settlement Systems in Selected Countries,” Bank for International
Settlements, 2002.

56

Figure 5

Before Deregulation & New Technology
small

high

SCALE

COSTS

large

low
standardized
hard

PRODUCTS & SERVICES

personalized

INFORMATION

soft

Figure 6

After Deregulation & New Technology
high

small

COSTS

SCALE

low

large
standardized
hard

PRODUCTS & SERVICES
INFORMATION

57

personalized
soft

Figure 7

De Novo Entry in Response to Mergers
high

small

COSTS

SCALE

low

large
standardized
hard

PRODUCTS & SERVICES

personalized

INFORMATION

soft

Figure 8

Using Technology to Enhance Position
high

small

COSTS

SCALE

Higher
profits
low

large
standardized
hard

PRODUCTS & SERVICES
INFORMATION

58

personalized
soft

Figure 9
Change in loans-to-assets for groups of U.S. commercial banks, 1991-2001. (1991 = 1.00)

125%
large banks
120%
mid-sized banks
115%
large community banks
110%

medium community
banks

105%

small community banks

100%

rural banks

95%
1991

1993

1995

1997

1999

2001

Source: Authors’ calculations and FDIC Call Reports.

Figure 10
Change in core deposits-to-assets for groups of U.S. commercial banks, 1991-2001. (1991 = 1.00)

110%
large banks
100%
mid-sized banks
90%
large community banks
80%

medium community
banks

70%

small community banks

60%

rural banks

50%
1991

1993

1995

1997

1999

2001

Source: Authors’ calculations and FDIC Call Reports.

59

Figure 11
Change in noninterest income-to-operating income groups of U.S. commercial banks, 1991-2001.
Two year moving averages. (1991 = 1.00)

110%

large banks

105%

mid-sized banks

100%

large community banks
medium community
banks

95%

small community banks
90%
rural banks
85%
1991

1993

1995

1997

1999

2001

Source: Authors’ calculations and FDIC Call Reports.

60

Appendix Tables A-1 through A-5
Selected Financial Ratios for U.S. Commercial Banks
in 1980, 1985, 1990, 1995 and 2001.
All data are expressed in thousands of 2001 dollars, unless indicated otherwise. There are more financial
ratios displayed in the later years because regulators began to collect more data over time and/or because
earlier versions of some data are not available in electronic formats. These tables display data for less
than the full population of commercial banks in any given year. To be included in these tables, banks had
to hold a valid state or federal commercial bank charter; be located in one of the fifty states or the District
of Columbia; at least ten full years old; and have positive amounts of loans, transactions deposits, and
insured deposits on its balance sheet. Urban banks (i.e., banks located in MSAs) are organized into five
asset size categories: small community banks with assets less than $100 million; medium community
banks with assets between $100 and $500 million; large community banks with assets between $500
million and $1 billion; mid-sized banks with assets between $1 and $10 billion; and large banks with
more than $10 billion in assets. Rural banks are included as a separate category, regardless of asset size.
To be included in either the rural bank category or in any of the community bank categories, banks had to
meet the following additional conditions: they were domestically owned; their credit card receivables (if
any) comprised no more than ten percent of their loan portfolios; they derived at least half of their
deposits from branches located in a single county; and they were organized as either an independent bank,
the sole bank in a one-bank holding company, or an affiliate in a multibank holding company comprised
solely of other community banks.
Sources: FDIC Call Reports (1980, 1985, 1990, 1995, 2001); FDIC Summary of Deposits Database
(1995, 2000, 2001); and internal FDIC records on transactional banking websites (2001).

61

Table A-1
2001

number of banks
assets
affiliate in a MBHC
affiliate in a OBHC
independent bank
FTEs
number of offices

Large
Banks

Mid-sized
Banks

Large
Community
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

72
$61,015,633
65.28%
33.33%
1.39%
1407058.00%
41125.00%

254
$2,803,139
49.21%
46.85%
3.94%
78509.06%
3986.22%

144
$669,793
17.36%
72.92%
9.72%
21132.64%
1112.50%

942
$222,919
10.19%
72.40%
17.41%
8478.56%
539.70%

767
$55,535
9.13%
57.63%
33.25%
2398.57%
216.17%

3189
$99,664
7.40%
72.97%
19.63%
3364.10%
275.82%

Asset items as % of total assets
cash
securities
federal funds sold
loans
allowance for losses
trading assets
premises
other assets

6.12%
17.26%
4.84%
62.49%
-4.78%
2.17%
1.03%
10.87%

4.59%
24.13%
4.31%
61.84%
-2.46%
0.11%
1.38%
6.11%

4.53%
23.93%
2.45%
65.64%
-1.86%
0.04%
1.60%
3.67%

5.04%
22.48%
4.10%
64.60%
-1.66%
0.01%
2.04%
3.39%

6.66%
23.77%
6.74%
59.27%
-1.16%
0.01%
2.05%
2.65%

5.72%
27.08%
5.27%
58.61%
-1.09%
0.01%
1.53%
2.88%

Composition of securities
held to maturity
for sale

5.43%
94.57%

11.80%
88.20%

17.99%
82.01%

18.86%
81.14%

24.44%
75.56%

22.58%
77.42%

Composition of loans (sums to more than 100%)
real estate
45.41%
agricultural
0.52%
small agricultural production
0.23%
commercial and industrial
24.53%
small business
5.09%
consumer
17.06%
credit cards
7.39%

59.02%
1.17%
0.64%
20.78%
8.41%
13.66%
3.23%

68.52%
1.36%
0.83%
18.96%
11.84%
8.46%
0.49%

70.28%
1.54%
1.34%
17.47%
13.86%
9.17%
0.47%

61.69%
5.99%
5.88%
16.99%
16.17%
14.14%
0.33%

54.27%
15.46%
14.85%
14.45%
13.62%
14.40%
0.37%

Liabilities and equity as % of assets
deposits
federal funds purchased
trading liabilities
other borrowings
liabilities on acceptances
other liabilities
subordinated debt
equity

56.48%
9.55%
0.96%
12.69%
0.06%
3.11%
1.66%
9.14%

72.42%
7.00%
0.02%
7.96%
0.02%
1.60%
0.40%
9.35%

81.14%
3.70%
0.00%
4.98%
0.02%
0.81%
0.02%
9.13%

84.14%
1.93%
0.00%
3.73%
0.01%
0.75%
0.02%
9.38%

86.29%
0.72%
0.00%
2.01%
0.00%
0.72%
0.01%
10.25%

84.69%
0.77%
0.00%
2.81%
0.00%
0.79%
0.01%
10.94%

Composition of deposits (sums to more than 100%)
transactions
18.19%
demand
15.88%
nontransactions
81.81%
savings
49.71%
MMDAs
36.43%
small time
15.74%
large time
16.36%
insured deposits
59.96%
core deposits
33.93%
average account size
$38,785

16.80%
13.34%
83.20%
44.60%
29.60%
22.41%
16.19%
67.59%
39.21%
$72,895

18.77%
12.39%
81.23%
39.48%
25.10%
25.57%
16.18%
74.02%
44.34%
$21,217

29.03%
17.66%
70.97%
27.86%
16.66%
28.00%
15.12%
77.92%
57.03%
$19,438

32.09%
18.20%
67.91%
22.39%
11.22%
32.48%
13.04%
85.09%
64.57%
$11,218

29.15%
14.35%
70.85%
18.97%
9.07%
37.51%
14.37%
85.72%
66.66%
$9,821

62

Table A-1 (continued)
2001
Income statement as % of total assets
interest income
interest expense
net interest income
noninterest income
provisions
noninterest expense
tax expense
net income (ROA)

Large
Banks

Mid-sized
Banks

Large
Community
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

6.06%
2.77%
3.29%
2.49%
0.67%
3.37%
0.62%
1.19%

6.57%
2.90%
3.67%
1.74%
0.45%
3.26%
0.61%
1.14%

6.72%
3.00%
3.72%
1.05%
0.29%
2.85%
0.49%
1.19%

6.85%
2.98%
3.87%
0.96%
0.25%
3.17%
0.37%
1.08%

6.92%
2.96%
3.96%
0.92%
0.25%
3.52%
0.24%
0.90%

6.96%
3.23%
3.74%
0.67%
0.25%
2.90%
0.27%
1.02%

Composition of noninterest income
fiduciary
service charges
trading income
nontrading gains
investment bank income
venture capital income
loan servicing income
securitization income
insurance income
other noninterest income

10.26%
20.06%
22.32%
6.23%
4.78%
-0.30%
0.73%
4.36%
1.89%
29.66%

12.13%
35.63%
0.31%
4.00%
4.48%
-0.14%
2.65%
1.24%
3.27%
36.43%

12.64%
41.38%
0.16%
10.03%
2.51%
0.12%
0.31%
0.17%
1.25%
31.43%

4.79%
51.40%
0.09%
6.62%
2.21%
0.04%
2.24%
0.01%
1.41%
31.20%

1.15%
61.77%
0.00%
3.37%
0.75%
0.00%
1.65%
0.00%
2.39%
28.91%

2.04%
63.41%
0.00%
1.41%
1.23%
0.00%
1.26%
0.06%
4.47%
26.11%

Composition of noninterest expense
salaries and benefits
premises expenses
other noninterest expenses

42.86%
11.96%
45.18%

45.72%
13.42%
40.86%

53.78%
14.92%
31.30%

54.33%
14.81%
30.86%

54.74%
14.05%
31.20%

56.25%
13.38%
30.37%

Other performance, risk, and strategy ratios
ROE
14.10%
accounting efficiency ratio
58.92%
service charges/transactions deposits
5.00%
assets/FTEs
$5,405
deposits/FTEs
$3,073
number of accounts/FTEs
295.87
assets/offices
$2,351,459
deposits/offices
$605,613
number of accounts/offices
68,084.05
FTEs/offices
549.65
operates transactional Internet site
95.83%
advertising expense/assets
0.11%
assets securitized/assets *
19.57%
sells mutal funds
81.94%
sells proprietary mutual funds
50.00%
Tier 1 risk-based capital ratio
12.61%
nonperforming loans/assets
0.89%
letters of credit/assets
4.50%
credit derivatives/assets
1.47%
trading derivatives/assets
156.48%
nontrading derivatives/assets
31.55%

13.25%
60.45%
4.54%
$4,623
$3,137
225.12
$240,962
$115,952
4,910.37
56.84
75.59%
0.05%
5.34%
64.96%
14.17%
13.22%
0.60%
1.80%
0.00%
0.65%
4.41%

13.27%
59.68%
2.92%
$3,773
$3,074
204.47
$107,012
$86,310
4,761.85
26.02
54.17%
0.05%
0.12%
53.47%
4.86%
13.59%
0.61%
1.05%
0.00%
0.03%
0.71%

11.62%
66.19%
2.02%
$3,039
$2,478
204.95
$55,288
$45,795
3,574.36
18.75
31.53%
0.04%
0.14%
30.04%
2.23%
14.79%
0.65%
0.59%
0.06%
0.00%
0.38%

9.16%
71.74%
1.85%
$2,508
$2,156
237.70
$30,923
$26,567
2,831.22
12.60
7.43%
0.04%
0.00%
10.95%
0.52%
17.52%
0.68%
0.26%
0.00%
0.00%
0.05%

9.60%
65.64%
1.76%
$2,791
$2,347
267.85
$39,262
$32,176
4,942.25
12.56
8.78%
0.04%
0.14%
15.84%
1.29%
18.84%
0.73%
0.25%
0.00%
0.01%
0.23%

* includes only assets sold and securitized with servicing retained or with recourse of other seller-provided credit enhancements.

63

Table A-2
1995

number of banks
assets
affiliate in a MBHC
affiliate in a OBHC
independent bank
FTEs
number of offices

Large
Banks

Mid-sized
Banks

Large
Community
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

75
$32,823,803
85.33%
14.67%
0.00%
8,275.04
219.57

302
$3,181,607
73.51%
23.51%
2.98%
1,019.43
45.73

129
$692,650
30.23%
55.81%
13.95%
277.30
13.24

989
$210,081
12.34%
63.50%
24.17%
93.62
5.03

1251
$50,769
9.43%
52.84%
37.73%
25.59
1.94

3891
$67,033
7.48%
64.41%
28.12%
28.14
2.25

7.88%
15.98%
4.17%
64.18%
-1.34%
3.22%
1.31%
4.61%

6.81%
22.26%
4.72%
62.82%
-1.20%
0.22%
1.42%
2.95%

5.86%
29.85%
2.92%
58.29%
-0.97%
0.06%
1.72%
2.28%

5.47%
29.82%
4.45%
57.13%
-0.89%
0.05%
1.90%
2.06%

6.27%
29.10%
6.34%
55.12%
-0.85%
0.01%
1.94%
2.05%

5.17%
33.87%
4.91%
53.44%
-0.88%
0.01%
1.41%
2.06%

Composition of loans (sums to more than 100%)
real estate
35.31%
agricultural
0.33%
small agricultural production
0.14%
commercial and industrial
30.27%
small business
4.74%
consumer
19.89%
credit cards
9.22%

49.43%
1.20%
0.59%
23.03%
8.21%
21.21%
5.69%

59.85%
1.50%
1.00%
21.01%
12.84%
14.75%
1.08%

63.73%
1.66%
1.50%
19.49%
16.09%
13.68%
0.75%

58.27%
6.92%
6.87%
17.27%
16.30%
16.84%
0.53%

48.38%
19.14%
18.90%
14.42%
13.98%
17.23%
0.39%

Liabilities and equity as % of assets
deposits
federal funds purchased
trading liabilities
other borrowings
liabilities on acceptances
other liabilities
subordinated debt
equity

57.29%
10.89%
0.51%
8.21%
0.49%
13.48%
1.35%
7.78%

74.44%
9.06%
0.22%
4.11%
0.09%
3.27%
0.38%
8.42%

82.35%
4.88%
0.16%
1.46%
0.09%
1.95%
0.06%
9.04%

87.24%
1.34%
0.07%
0.81%
0.02%
0.93%
0.02%
9.57%

87.76%
0.73%
0.03%
0.50%
0.01%
0.88%
0.01%
10.07%

86.90%
0.65%
0.03%
0.60%
0.00%
0.93%
0.00%
10.89%

Composition of deposits (sums to more than 100%)
transactions
33.08%
demand
26.36%
nontransactions
66.92%
savings
33.75%
MMDAs
22.85%
small time
21.79%
large time
11.38%
insured deposits
66.58%
core deposits
54.88%
average account size
$26,221

31.45%
21.88%
68.55%
29.91%
18.82%
26.91%
11.73%
74.95%
58.36%
$29,948

32.35%
19.56%
67.65%
27.41%
14.35%
30.49%
9.76%
81.18%
62.83%
$137,625

32.23%
18.70%
67.77%
26.39%
13.27%
31.04%
10.34%
84.62%
63.27%
$12,097

33.08%
18.69%
66.92%
23.22%
10.21%
34.49%
9.21%
89.86%
67.57%
$10,410

29.62%
13.85%
70.38%
19.14%
8.28%
41.02%
10.22%
90.43%
70.65%
$8,678

Asset items as % of total assets
cash
securities
federal funds sold
loans
allowance for losses
trading assets
premises
other assets

64

Table A-2 (continued)
1995
Income statement as % of total assets
interest income
interest expense
net interest income
noninterest income
provisions
noninterest expense
tax expense
net income (ROA)

6.95%
3.43%
3.52%
2.38%
0.35%
3.64%
0.68%
1.24%

7.14%
3.21%
3.93%
1.54%
0.30%
3.33%
0.63%
1.20%

7.04%
3.04%
4.00%
1.06%
0.17%
3.20%
0.52%
1.20%

7.31%
3.00%
4.31%
1.01%
0.19%
3.48%
0.51%
1.15%

7.36%
2.95%
4.41%
1.12%
0.15%
3.87%
0.45%
1.05%

7.33%
3.27%
4.06%
0.64%
0.14%
2.97%
0.46%
1.13%

Composition of noninterest income
fiduciary
service charges
trading income
other fee income
other noninterest income

17.26%
24.34%
6.30%
35.91%
16.20%

17.06%
34.83%
2.16%
31.47%
14.48%

18.62%
42.30%
1.10%
26.34%
11.63%

6.84%
53.61%
0.43%
25.21%
13.91%

0.09%
63.93%
0.03%
23.55%
12.40%

1.21%
63.95%
0.04%
23.35%
11.46%

Composition of noninterest expense
salaries and benefits
premises expenses
other noninterest expenses

41.80%
12.72%
45.48%

40.97%
13.15%
45.88%

50.40%
15.20%
34.40%

51.38%
14.79%
33.83%

52.31%
13.77%
33.92%

54.84%
12.76%
32.40%

16.00%
63.79%
2.07%
$4,182
$2,157
224.21
$1,815,265
$393,742
21,168.29
376.37

14.92%
61.18%
2.27%
$7,950
$5,131
339.88
$262,797
$112,241
6,794.77
60.12

13.49%
62.46%
1.43%
$2,896
$2,220
232.77
$77,210
$56,528
5,330.08
29.00

11.96%
64.62%
1.58%
$2,445
$2,127
238.00
$55,944
$48,462
5,040.86
26.61

10.59%
69.30%
1.96%
$2,148
$1,878
264.07
$31,053
$27,102
3,658.33
16.82

10.51%
63.09%
1.56%
$2,491
$2,152
304.08
$32,340
$27,908
3,757.76
14.48

Other performance ratios
ROE
accounting efficiency ratio
service charges/transactions deposits
assets/FTEs
deposits/FTEs
number of accounts/FTEs
assets/offices
deposits/offices
number of accounts/offices
FTEs/offices

65

Table A-3

1990

number of banks
assets
affiliate in a MBHC
affiliate in a OBHC
independent bank
FTEs

Large
Banks

Mid-sized
Banks

Large
Community
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

64
$28,352,844
78.13%
21.88%
0.00%
8,402.20

324
$3,140,055
75.31%
21.60%
3.09%
1,166.97

125
$672,898
31.20%
52.00%
16.80%
274.02

944
$210,628
16.31%
60.28%
23.41%
97.85

1400
$48,528
9.93%
51.14%
38.93%
25.63

4899
$60,771
6.35%
59.38%
34.27%
25.72

11.59%
14.62%
4.22%
62.89%
-1.97%
1.73%
1.41%
5.51%

8.84%
19.30%
4.43%
63.59%
-1.40%
0.30%
1.54%
3.40%

6.59%
26.42%
3.92%
59.57%
-1.08%
0.12%
1.69%
2.78%

6.29%
27.57%
5.05%
57.41%
-0.86%
0.07%
1.88%
2.58%

7.65%
29.20%
7.13%
52.37%
-0.88%
0.05%
1.85%
2.63%

6.95%
35.57%
6.17%
48.34%
-0.86%
0.04%
1.32%
2.48%

Composition of loans (sums to more than 100%)
real estate
33.62%
agricultural
0.43%
commercial and industrial
37.09%
consumer
13.09%
credit cards
3.44%

42.74%
0.80%
26.88%
22.13%
5.40%

51.82%
1.29%
26.04%
16.40%
1.27%

56.16%
1.48%
22.95%
17.67%
0.74%

51.91%
7.52%
19.03%
20.73%
0.48%

42.95%
20.33%
16.47%
19.37%
0.30%

Liabilities and equity as % of assets
deposits
federal funds purchased
other borrowings
liabilities on acceptances
other liabilities
subordinated debt
equity

63.86%
10.99%
3.95%
1.11%
12.42%
0.94%
5.52%

80.50%
8.15%
1.25%
0.14%
2.61%
0.21%
6.47%

85.90%
4.67%
0.22%
0.04%
1.12%
0.14%
7.42%

89.25%
1.27%
0.10%
0.00%
1.06%
0.05%
7.95%

89.73%
0.39%
0.06%
0.00%
0.99%
0.03%
8.66%

88.86%
0.49%
0.04%
0.00%
1.09%
0.01%
9.40%

Composition of deposits (sums to more than 100%)
transactions
32.07%
demand
25.26%
nontransactions
67.93%
savings
24.94%
MMDAs
17.80%
small time
22.77%
large time
20.22%
insured deposits
64.79%
core deposits
54.84%
average account size
$36,865

29.20%
19.51%
70.80%
25.12%
16.38%
31.07%
14.62%
77.48%
60.26%
$18,184

26.88%
16.91%
73.12%
25.18%
15.34%
32.86%
15.07%
82.34%
59.74%
$17,081

27.83%
16.43%
72.17%
24.05%
13.52%
35.48%
12.64%
87.63%
63.31%
$10,952

28.98%
16.01%
71.02%
21.74%
10.62%
39.49%
9.79%
92.89%
68.47%
$8,224

26.71%
12.75%
73.29%
16.95%
8.59%
46.65%
9.69%
93.67%
73.36%
$8,480

Asset items as % of total assets
cash
securities
federal funds sold
loans
allowance for losses
trading assets
premises
other assets

66

Table A-3 (continued)
1990
Income statement as % of total assets
interest income
interest expense
net interest income
noninterest income
provisions
noninterest expense
tax expense
net income (ROA)

9.18%
6.18%
3.00%
1.71%
1.12%
3.30%
0.12%
0.21%

9.00%
5.35%
3.65%
1.39%
1.13%
3.44%
0.13%
0.36%

8.87%
5.13%
3.75%
0.90%
0.70%
3.06%
0.26%
0.65%

9.05%
5.10%
3.95%
0.97%
0.51%
3.44%
0.27%
0.69%

9.14%
5.04%
4.10%
0.94%
0.46%
3.78%
0.25%
0.55%

9.05%
5.24%
3.82%
0.61%
0.31%
2.96%
0.30%
0.85%

Composition of noninterest income
fiduciary
service charges
trading income
other noninterest income

22.85%
21.50%
6.55%
49.09%

22.57%
33.15%
-3.98%
48.27%

20.42%
40.53%
0.74%
38.31%

8.17%
55.09%
0.30%
36.44%

0.02%
65.90%
0.00%
34.07%

1.04%
61.95%
0.02%
36.98%

Composition of noninterest expense
salaries and benefits
premises expenses
other noninterest expenses

46.49%
15.82%
37.69%

42.72%
14.39%
42.88%

48.89%
15.16%
35.95%

49.37%
15.06%
35.57%

49.83%
14.14%
36.03%

52.25%
12.57%
35.18%

10.79%
69.62%
1.62%
$3,906.02
$2,189.88
189.21

3.19%
68.89%
1.59%
$7,894.31
$2,319.74
251.62

14.67%
65.31%
1.36%
$2,963.30
$2,294.21
251.89

4.13%
69.27%
1.77%
$2,339.49
$2,082.45
290.59

-21.02%
74.35%
2.23%
$2,076.98
$1,856.19
350.60

9.07%
66.87%
1.57%
$2,484.57
$2,199.13
360.51

Other performance and strategy ratios
ROE
accounting efficiency ratio
service charges/transactions deposits
assets/FTEs
deposits/FTEs
number of accounts/FTE

67

Table A-4
1985

number of banks
assets
affiliate in a MBHC
affiliate in a OBHC
independent bank
FTEs

Large
Banks

Mid-sized
Banks

Large
Community
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

49
$34,231,081
61.22%
38.78%
0.00%
10,244.67

336
$2,987,255
68.15%
28.27%
3.57%
1,258.90

130
$683,923
39.23%
43.08%
17.69%
303.38

1170
$201,923
13.16%
55.73%
31.11%
99.79

1794
$48,737
5.63%
45.60%
48.77%
26.52

5881
$56,544
3.54%
50.69%
45.77%
24.69

15.11%
10.86%
2.94%
62.90%
-0.91%
1.95%
1.29%
5.88%

12.30%
18.43%
5.51%
59.69%
-0.79%
0.35%
1.64%
2.88%

9.38%
24.56%
5.66%
57.07%
-0.74%
0.07%
1.76%
2.23%

8.23%
28.01%
5.44%
54.59%
-0.70%
0.05%
1.98%
2.41%

8.65%
29.31%
6.83%
51.44%
-0.67%
0.03%
1.98%
2.42%

7.70%
33.67%
6.92%
48.12%
-0.74%
0.04%
1.43%
2.85%

Composition of loans (sums to more than 100%)
real estate
20.63%
agricultural
0.77%
commercial and industrial
41.12%
consumer
14.34%
credit cards
4.71%

29.76%
1.02%
32.19%
23.16%
5.20%

36.85%
1.36%
31.57%
20.51%
1.87%

42.54%
1.73%
28.52%
23.96%
0.70%

42.05%
8.14%
21.96%
27.10%
0.32%

34.15%
23.04%
19.29%
22.11%
0.14%

Liabilities and equity as % of assets
deposits
federal funds purchased
other borrowings
liabilities on acceptances
other liabilities
subordinated debt
equity

55.52%
12.89%
4.67%
3.21%
17.64%
0.80%
5.26%

77.74%
9.42%
1.74%
0.36%
4.35%
0.30%
6.09%

85.24%
4.84%
0.97%
0.06%
1.63%
0.14%
7.13%

88.75%
1.64%
0.50%
0.01%
1.35%
0.10%
7.65%

89.15%
0.55%
0.23%
0.00%
1.24%
0.06%
8.78%

88.81%
0.41%
0.15%
0.00%
1.33%
0.04%
9.27%

Composition of deposits (sums to more than 100%)
transactions
38.59%
demand
33.52%
nontransactions
61.41%
savings
22.60%
small time
15.97%
large time
22.84%
insured deposits
62.62%
core deposits
54.56%

33.02%
25.93%
66.98%
27.12%
22.43%
17.43%
76.16%
55.45%

29.98%
22.42%
70.02%
26.89%
26.68%
16.45%
80.82%
56.66%

28.33%
19.52%
71.67%
28.02%
30.18%
13.46%
87.41%
58.51%

28.35%
18.12%
71.65%
24.92%
36.42%
10.31%
92.51%
64.77%

26.14%
14.80%
73.86%
18.80%
45.99%
9.08%
93.91%
72.12%

Asset items as % of total assets
cash
securities
federal funds sold
loans
allowance for losses
trading assets
premises
other assets

68

Table A-4 (continued)
1985
Income statement as % of total assets
interest income
interest expense
net interest income
noninterest income
provisions
noninterest expense
tax expense
net income (ROA)

8.61%
5.68%
2.92%
1.22%
0.58%
2.77%
0.18%
0.67%

8.69%
5.24%
3.45%
1.23%
0.49%
3.32%
0.16%
0.75%

9.13%
5.47%
3.66%
0.89%
0.53%
3.15%
0.13%
0.82%

9.59%
5.53%
4.05%
0.83%
0.62%
3.31%
0.21%
0.83%

10.06%
5.66%
4.40%
0.88%
0.70%
3.65%
0.25%
0.77%

10.17%
6.04%
4.13%
0.55%
0.99%
2.92%
0.15%
0.70%

Composition of noninterest income
fiduciary
service charges
trading income
other noninterest income

20.89%
20.34%
9.23%
49.54%

21.66%
30.92%
3.29%
44.13%

24.23%
38.72%
1.71%
35.33%

7.24%
55.12%
0.34%
37.30%

0.00%
62.14%
-0.01%
37.87%

0.74%
57.70%
0.02%
41.54%

Composition of noninterest expense
salaries and benefits
premises expenses
other noninterest expenses

50.42%
16.34%
33.24%

47.66%
15.75%
36.59%

49.96%
15.99%
34.05%

50.18%
15.98%
33.84%

50.57%
15.34%
34.08%

52.24%
13.90%
33.89%

Other performance ratios
ROE
accounting efficiency ratio
service charges/transactions deposits
assets/FTEs
deposits/FTEs

12.59%
66.00%
1.06%
$3,840
$1,900

11.96%
70.66%
1.23%
$2,741
$1,980

15.02%
69.36%
1.24%
$2,468
$2,093

-0.46%
67.17%
1.71%
$2,173
$1,924

5.12%
68.49%
2.07%
$2,023
$1,800

7.83%
62.51%
1.41%
$2,420
$2,145

69

Table A-5
1980

number of banks
assets
affiliate in a MBHC
affiliate in a OBHC
independent bank
FTEs

Large
Banks

Large
Community
Banks

Mid-sized
Banks

Medium
Community
Banks

Small
Community
Banks

Rural
Banks

35
$44,204,656
31.43%
65.71%
2.86%
11,740.34

286
$2,764,589
45.45%
35.66%
18.88%
1,329.49

167
$695,942
17.37%
31.74%
50.90%
361.62

1146
$207,300
5.24%
25.39%
69.37%
113.47

2070
$47,580
1.30%
20.63%
78.07%
27.97

6947
$56,516
0.59%
22.10%
77.31%
27.07

24.03%
10.25%
2.32%
53.57%
-0.56%
1.01%
9.39%

15.71%
21.07%
5.63%
52.41%
-0.60%
1.76%
4.02%

11.77%
26.22%
6.46%
51.42%
-0.56%
2.01%
2.68%

9.36%
29.73%
4.73%
52.60%
-0.53%
2.09%
2.02%

8.94%
30.87%
6.32%
51.21%
-0.49%
1.98%
1.17%

8.34%
31.14%
6.56%
51.27%
-0.48%
1.51%
1.65%

Composition of loans (sums to more than 100%)
real estate
17.23%
agricultural
0.90%
commercial and industrial
46.50%
consumer
11.51%
credit cards
3.01%

31.99%
1.33%
34.37%
25.30%
5.51%

37.71%
1.83%
31.97%
26.60%
2.19%

42.15%
2.07%
27.76%
28.86%
1.01%

40.84%
9.68%
19.50%
31.31%
0.34%

33.62%
25.15%
17.53%
23.89%
0.15%

Liabilities and equity as % of assets
deposits
50.09%
federal funds purchased
10.25%
other liabilities
34.57%
subordinated debt
0.62%
equity
4.47%

76.50%
10.22%
6.55%
0.52%
6.22%

83.05%
6.60%
2.86%
0.45%
7.04%

87.23%
2.57%
2.09%
0.32%
7.80%

89.00%
0.72%
1.33%
0.18%
8.76%

89.17%
0.50%
1.21%
0.11%
9.01%

Composition of deposits (sums to less than 100%)
demand
42.85%
38.59%
large time
32.37%
21.38%

34.43%
19.76%

30.77%
14.60%

30.84%
8.89%

28.83%
7.93%

9.92%
8.75%
0.22%
0.91%

9.81%
8.54%
0.24%
1.01%

9.88%
8.38%
0.27%
1.14%

9.64%
7.96%
0.24%
1.26%

0.09%
0.31%
1.59%
0.49%

0.04%
0.37%
1.74%
0.48%

0.01%
0.22%
1.50%
0.36%

12.45%
$1,988
$1,733

12.87%
$1,894
$1,686

13.34%
$2,298
$2,049

Asset items as % of total assets
cash
securities
federal funds sold
loans
allowance for losses
premises
other assets

Income statement as % of total assets
total revenue
10.57%
total expense
9.74%
provisions
0.26%
net income (ROA)
0.51%

10.00%
9.03%
0.29%
0.77%

Composition of various noninterest income and expense items as % of assets
fiduciary
0.20%
0.23%
0.16%
service charges
0.09%
0.21%
0.23%
salaries and benefits
1.15%
1.59%
1.54%
premises expenses
0.34%
0.51%
0.50%
Other performance ratios
ROE
assets/FTEs
deposits/FTEs

11.17%
$4,019
$1,880

12.20%
$2,252
$1,648

70

13.12%
$2,139
$1,762

Endnotes
1

For a cross-country analysis of the importance of small banks to aggregate economic activity and the

health of small and midsized enterprises in both developed and developing nations, see Berger, Hasan and
Klapper (2004).
2

Hannan and Prager (forthcoming) take a similar geographic markets approach to identify “single-market

banks,” which they define as drawing over 90 percent of their deposits or branches from a single state or a
single Metropolitan Statistical Area (MSA).
3

The only exceptions were cross-border banking organizations that existed under grandfathered

arrangements.
4

See Table B6 in Berger, Kashyap and Scalise (1995).

5

These states were referred to as unit banking states. Unit banking laws restricted banks to a single

location although in same cases, such as in Illinois, this restriction could be partially pierced by forming
groups of banks with common stockholders.
6

Data based on the total number of FDIC-insured U.S. commercial banks from the FDIC website.

7

Data from the Federal Reserve Flow of Funds Accounts.

8

For a discussion of the relative risk of consumer lending by banks and finance companies see chapter 6

in Cornett and Saunders (1999).
9

Throughout most of the past three decades, commercial banks were prohibited from making individual

loans larger than 15 percent of their book value equity capital. Smaller banks can originate loans larger
than their legal lending limit if they sell a participation in the loan equal to or greater than the amount by
which the loan exceeds the legal lending limit. Such participations were often sold to a community
bank’s correspondent bank. From a practical perspective, however, this arrangement usually complicated
the lending relationship because the loan officer had to obtain approval from two loan committees, her
own and that of the correspondent bank. This additional layer of complexity often reduced flexibility in
negotiating with the borrower and when renegotiation was an issue.
10

Both in the 1970s and today, businesses that use asset-based finance tend to be highly leveraged (Carey,

Post, Sharpe 1998, Udell 2003). The high leverage typically stems from either rapid growth, a leveraged
buyout, or financial distress.
11

For a discussion of asset-based finance including factoring see Udell (2003).

12

Arguably, the McFadden Act was never the kind of binding constraint on wholesale banking that it was

on retail banking.

International banking services could be delivered out of single home office.

Commercial lending could be delivered on a national level through local loan production offices. Loan
production offices were essentially interstate branches for commercial lending. These offices were
permitted during the McFadden Act era so long as they did not engage in deposit-taking (Ritter, Silber

71

and Udell 1999). To solve the checking account problem, large companies would establish checking
accounts at local banks and then transfer these funds on a systematic basis to a primary account(s) with
the company’s main bank. Large banks offered sophisticated cash management systems to minimize the
costs associated with maintaining these local accounts (see Kallberg and Parkinson 1993). For smaller
companies, McFadden was not much of a constraint anyway, because these businesses obtain their
banking business locally (see, for example, Ang (1992).
13

See Berger, Kashyap and Scalise, Table B6 (1995).

14

See Cornett and Saunders, p. 613 (1999).

15

The implicit government subsidy of Fannie Mae and Freddie Mac can directly alter the price of

mortgages purchased and held by these two GSEs for their own portfolios. However, because the subsidy
takes the form of an implicit guarantee of Fannie and Freddie’s own debt, it is not directly related to the
mortgage-backed securities (MBS) that these two GSEs sell to investors. Fannie’s and Freddie’s GSE
status does however indirectly subsidize MBS to the extent that investors view these GSE-originated
MBS as being implicitly guaranteed by the government and thus do not demand from the GSEs the credit
enhancements that investors demand from competing privately originated MBS.
16

There is one published study that has done a more focused analysis on whether human intervention can

improve decisionmaking on applicants who are rejected on the basis of credit scoring. This study, based
on data from one bank with an historically high “override” rate, found that “overrides” of applicants that
would have been rejected just on the basis of the credit score did not do any better on average than their
credit score alone predicted (Mays 2003, Chapter 12).
17

For an analysis of the power of credit scoring as a business lending tool and the use of information

exchange generated information in a credit scoring model see Kallberg and Udell (2003).
18

A form of credit scoring based on the original Altman Z-score model has been available for middle

market and large business lending since the 1970s. However, even today, credit scoring does not appear
to be used as the primary underwriting criteria in these segments of the commercial market although there
is evidence of its adoption by larger banks in the 1980s as an important tool in their loan review activities
(Udell 1987).
19

See, for example, Cornett and Saunders (1999) for a discussion of asset-liability management

techniques.
20

See Saunders (1999) for a discussion of credit risk models.

21

Small banks will be treated differently under the New Basle Capital Accord primarily because it is felt

that it will be infeasible for them to meet the data and technology requirements necessary to calculate
their PDs and LGDs. At this stage it is also appears possible that the U.S. will only adopt the advanced
version of the new capital requirements (the “Advanced Internal Ratings Based Approach”) and that it
72

will only be used by approximately the largest 20 banks.

Even if the U.S. were to adopt the

“Standardized Approach,” it would be on balance only a marginal change from current capital
requirements. There is also an intermediate version, the “Foundation Internal Ratings Based Approach,”
which may not be adopted by the U.S. It is possible that either under current standards, or under the
Standardized or Foundation approaches, that small banks will find themselves with higher capital
requirements than the largest banks who opt for the Advanced Internal Ratings Approach. Given that
small banks have historically had much higher proportionate capital levels, however, it is not clear that
this will affect the competitive position of small banks.
22

See Berger, Hancock and Marquardt (1996), Hancock and Humphrey (1998) and Berger (2003) for a

more extensive review of the literature electronic payments.
23

Based on internal records compiled by the Federal Financial Institutions Examination Council (FFIEC).

24

For more extensive discussions and analyses of the causes and consequences of consolidation in the

banking and financial services industries see Berger, Demsetz and Strahan (1999) and Berger, DeYoung,
Genay and Udell (2000).
25

Data from the FDIC website.

26

Tables 1 and 2 represent an alternative to analyzing the decline in banking by looking at the size of the

banking industry relative to other financial institutions (e.g., Boyd and Gertler 1994). By looking at the
users of financial services (in this case, consumers) as we do in Tables 1 and 2, we avoid problems of
determining the appropriate metric for measuring the size of different financial intermediaries including
such issues as how to weigh off-balance sheet activities.
27

For example, total bank assets are often used as a measure of the size of the banking industry.

However, over the past two decades a considerable fraction of bank activities are not reflected on the
balance sheet, i.e., off-balance sheet activities.
28

The benefit comes in the form of a reduction of in the liquidity premium (e.g., Silber 1991, Longstaff

1995).
29

Based on data from the Federal Reserve Y-9C Bank Holding Company reports and the FDIC Call

Reports.
30

See Berger and Udell (1998) and Boot (2000) for a more detailed discussion and reviews of the

literature on relationship lending.
31

There have been some recent studies that have found that the average distance between lenders and

borrowers has decreased over recent decades suggesting that the technology of small business lending
may have changed and that these changes may have diminished the importance of having a local lender
(Petersen and Rajan 2002, Degryse and Ongena 2002). However, the distances involved are very small as
are the measured transportation costs associated with these distances (Udell 2002).
73

32

The $250,000 figure corresponds to the reported maximum loan level of the micro business loan market

(e.g., Berger, Frame and Miller 2002). The $15,000,000 figure is somewhat more arbitrary but is meant
to correspond with the maximum loan that could be made by the largest bank that could fall under the
most expansive definition of a community bank.
33

See Berger and Udell (2002) and Scott (2004) for analyses of the importance of the borrower-loan

officer relationship in relationship lending.
34

Technology appears likely to have had some impact on asset-based lending and factoring. Because

these technologies involve the daily monitoring of collateral, particularly the receivables, the
computational power of computers and the ability to transmit turnover activity instantaneously has likely
improved the quality of monitoring of these loans and lowered the cost, although there is no hard data on
this (Udell 2003).
35

This description of small business lending as being composed of relationship lending, asset-based

lending, micro-business lending and financial statement lending is based on the taxonomy in Berger and
Udell (2002).
36

There is also evidence that community banks earn a higher risk-adjusted yield on small business

lending than large banks (Carter, McNulty and Verbrugge 2004).

This result is consistent with

community banks having an advantage in assessing the soft information associated with relationship
lending. It is also consistent with relationship lending requiring a higher risk-adjusted yield because of
the increased costs associated with collecting soft information.
37

For a discussion of differences in monitoring and renegotiation across small, medium and large

borrowers, see Carey, Prowse, Rea and Udell (1994).
38

There are theoretical arguments that increased competition in banking might diminish the quality and

nature of relationship lending (Petersen and Rajan 1995, Boot and Thakor 2000, Dinc 2000, and Ceterelli
and Peretto 2000). The empirical evidence that increased competition hinders access to relationship
lending, or lending in general, is quite mixed.

See Beck, Dermirguc-Kunt and Maksimovic 2003 and

Berger, Hasan and Klapper 2004 for recent empirical evidence and a review of the theoretical and
empirical work on this issue.
39

See Berger, Hanweck, and Humphrey (1987), Mester (1987), Clark (1988), Hunter, Timme and Yang

(1990), Hunter and Timme (1991), Evanoff and Israilevich (1991), Clark (1996), and Berger and Mester
(1997) for reviews of the bank scale economy at various points in time.
40

For an analysis of the role of advertising in the commercial banking industry, see Ors (2003).

41

See DeYoung (1999, 2000) for a recent summary of the causes and consequences of bank mergers in

the U.S.

74

42

It should be noted, however, that unlike small business lending, there does not appear to be any

systematic empirical evidence that community banks have an advantage over large banks in delivering
private banking service.
43

It is not yet clear whether Internet-only banking will be a viable business model, and if so, whether it

will feature small, medium, or large banks.

See DeYoung (forthcoming) for some findings and a

discussion.
44

Tables A-2 through A-5 follow the same format for year-end 1995, 1990, 1985, and 1980 data

(expressed in 2001 dollars), although fewer ratios are included for these later years in which bank
regulatory agencies collected less complete information from banks.
45

The reported numbers for credit card loans at community and rural banks are somewhat depressed by

our sample selection method, which excluded community and rural banks at which credit card loans
comprised more than 10 percent of the loan portfolio. We ran these numbers again without this sampling
constraint, which resulted in average credit card loans-to-total loans ratios of between 0.5 percent and 2
percent for these banks.
46

Securitized assets and securitization income refer to definitions from individual lines of the 2001 bank

Call Reports, which are not necessarily inclusive of all securitization activities at commercial banks.
47

Consistent with the implication of these aggregate statistics, Craig and Thomson (2003) find evidence

that community banks are not constrained in their small business lending by a lack of deposit funding.
On this basis they reject the funding-driven market failure justification for allowing Federal Home Loan
Bank lending to community banks.
48

Rural banks are outliers here with an average cost of funds of 3.23%. This likely reflects a relative

scarcity of funds in rural towns coupled with a strong dependence by rural banks on local deposit
relationships, especially small time deposits.
49

Carter, McNulty and Verbrugge (2004) take this analysis further and find that the risk adjusted yield on

small business lending is higher at smaller banks than larger banks.
50

The enormous discontinuous leaps in these ratios between mid-sized banks and large banks imply that

the largest banks are using a very different business model, e.g., different production processes,
distribution channels, and output mixes.
51

Risk-reduction from diversification is typically associated with large banks, who can hold large loan

portfolios and operate in multiple geographic and product markets. But diversification benefits also occur
at community banks. Emmons, Gilbert, and Yeager (2004) performed a simulated bank merger exercise
using data from community banks in the 1990s, and found that post-merger risk reductions stemmed more
from increased bank size (reduced exposure to idiosyncratic risk) than from geographic diversification
(reduced exposure to local market risk).

Stiroh (2004) finds that community banks benefit from
75

diversification within broad activity classes like traditional lending, but do not benefit from diversification
across broad activity classes.
52

Although it is virtually impossible to trace changes in bank technology over time using publicly

available data, there are some recent empirical studies that investigate technology adoption by banks, and
for the most part the results of these studies are consistent with our strategic framework. For example,
Furst, Lang, and Nolle (2002) and Courchane, Nickerson, and Sullivan (2002) both study the diffusion of
Internet websites at commercial banks; both studies find that large bank size is a strong indicator of
adoption, but they also find a number of environmental and strategic determinants. Both White and
Frame (2002) and Berger (2003) have reviewed the literature on technology and technology adoption in
commercial banking.
53

The figure shows that rural banks greatly increased their reliance on noninterest income during the

1990s, but this is likely because they started at such a low ratio of noninterest income-to-operating
income in 1991 (just 12 percent).
54

Similarly, Avery and Samolyk (2004) find that incumbent community banks tend to gain market share

in local markets that experience consolidation by merger.
55

We calculate the Sharpe Ratio as the excess return over the risk-free rate (average ROE minus the

average annual rate on constant maturity one-year T-Bills) divided by the standard deviation of ROE.
56

Accounting practices at small owner-operated banks may cause the results in Table 4 to understate the

relative earnings of small community banks and rural banks. These banks sometimes reduce their
recorded profits (and hence reduce their corporate income taxes) by paying owner-managers high salaries
and bonuses. However, such practices would also tend to smooth reported earnings over time, which
reduces the standard deviation of those earnings and increases the Sharpe Ratio. We explored this
possibility by recalculating Table 4 after excluding small community banks and rural banks that were
organized independently. The results did not materially change.
57

It may also be inappropriate to compare ROE at owner-operated community banks to ROE at other

banks because the owner-managers of these banks sometimes pay themselves higher salaries and bonuses
to avoid double taxation of the owner’s earnings, which reduces reported ROE. See DeYoung, Spong,
and Sullivan (2001).
58

A more recent attempt along these lines was made by Robertson (2002).

76

Working Paper Series
A series of research studies on regional economic issues relating to the Seventh Federal
Reserve District, and on financial and economic topics.
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Edward J. Green and Ruilin Zhou

WP-00-1

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Subordinated Debt and Bank Capital Reform
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Bank Capital Regulation With and Without State-Contingent Penalties
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Puzzles in the Chinese Stock Market
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Idiosyncratic Risk and Aggregate Employment Dynamics
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1

Working Paper Series (continued)
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The Pitfalls in Inferring Risk from Financial Market Data
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Data Revisions and the Identification of Monetary Policy Shocks
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Supplier Relationships and Small Business Use of Trade Credit
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What are the Short-Run Effects of Increasing Labor Market Flexibility?
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WP-01-01

2

Working Paper Series (continued)
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Do Regulators Search for the Quiet Life? The Relationship Between Regulators and
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Jonas D. M. Fisher and Andreas Hornstein

WP-01-05

WP-01-06

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Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy
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Deregulation, the Internet, and the Competitive Viability of Large Banks
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3

Working Paper Series (continued)
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Is the United States an Optimum Currency Area?
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A Note on the Estimation of Linear Regression Models with Heteroskedastic
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The Mis-Measurement of Permanent Earnings: New Evidence from Social
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Regulatory Incentives and Consolidation: The Case of Commercial Bank Mergers
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WP-02-06

WP-02-07

4

Working Paper Series (continued)
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The Costs and Benefits of Moral Suasion: Evidence from the Rescue of
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On the Cyclical Behavior of Employment, Unemployment and Labor Force Participation
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Do Safeguard Tariffs and Antidumping Duties Open or Close Technology Gaps?
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Technology Shocks Matter
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Optimal Fiscal and Monetary Policy: Equivalence Results
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Real Exchange Rate Fluctuations and the Dynamics of Retail Trade Industries
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Bank Procyclicality, Credit Crunches, and Asymmetric Monetary Policy Effects:
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Robert R. Bliss and George G. Kaufman

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The Value of Banking Relationships During a Financial Crisis:
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On the Distribution and Dynamics of Health Costs
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WP-02-22

5

Working Paper Series (continued)
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Elijah Brewer III and William E. Jackson III

WP-02-23

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David A. Marshall and Edward Simpson Prescott

WP-02-24

Local Market Consolidation and Bank Productive Efficiency
Douglas D. Evanoff and Evren Örs

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Life-Cycle Dynamics in Industrial Sectors. The Role of Banking Market Structure
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Private School Location and Neighborhood Characteristics
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WP-02-27

Teachers and Student Achievement in the Chicago Public High Schools
Daniel Aaronson, Lisa Barrow and William Sander

WP-02-28

The Crime of 1873: Back to the Scene
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WP-02-29

Trade Structure, Industrial Structure, and International Business Cycles
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WP-02-30

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Louis Jacobson, Robert LaLonde and Daniel G. Sullivan

WP-02-31

A Proposal for Efficiently Resolving Out-of-the-Money Swap Positions
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WP-03-01

Depositor Liquidity and Loss-Sharing in Bank Failure Resolutions
George G. Kaufman

WP-03-02

Subordinated Debt and Prompt Corrective Regulatory Action
Douglas D. Evanoff and Larry D. Wall

WP-03-03

When is Inter-Transaction Time Informative?
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WP-03-04

Tenure Choice with Location Selection: The Case of Hispanic Neighborhoods
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Distinguishing Limited Commitment from Moral Hazard in Models of
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WP-03-06

Resolving Large Complex Financial Organizations
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WP-03-07

6

Working Paper Series (continued)
The Case of the Missing Productivity Growth:
Or, Does information technology explain why productivity accelerated in the United States
but not the United Kingdom?
Susanto Basu, John G. Fernald, Nicholas Oulton and Sylaja Srinivasan

WP-03-08

Inside-Outside Money Competition
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WP-03-09

The Importance of Check-Cashing Businesses to the Unbanked: Racial/Ethnic Differences
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A Structural Empirical Model of Firm Growth, Learning, and Survival
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WP-03-11

Market Size Matters
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WP-03-12

The Cost of Business Cycles under Endogenous Growth
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WP-03-13

The Past, Present, and Probable Future for Community Banks
Robert DeYoung, William C. Hunter and Gregory F. Udell

WP-03-14

7