Directory UMM :Data Elmu:jurnal:J-a:Journal Of Banking And Finance:Vol24.Issue5.2000:

Journal of Banking & Finance 24 (2000) 709±734
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Determinants of bank growth choice
Ken B. Cyree
a

a,*

, James W. Wansley b, Thomas P. Boehm

b

Department of Economics, Finance, and International Business, University of Southern Mississippi,
314-I Joseph Greene Hall, Hattiesburg, MS 39406, USA
b
Department of Finance, The University of Tennessee, Knoxville, TN 37996, USA
Received 5 June 1998; accepted 24 March 1999

Abstract
We study the determinants of bank growth in a two-stage logistic regression model.

We ®rst compare banks that branch, Bank Acquire, or Product Expand with banks that
do not grow externally. Banks that are federally chartered, in states with higher income
growth, and with higher labor prices are less likely to grow externally. Larger banks are
more likely to grow externally. In the second stage, we study determinants of growth
activity for banks that expand products, branch, or acquire other banks. Depending on
the time period, bank structure, regulatory environment, performance, and balance
sheet characteristics determine bank growth choices. Ó 2000 Elsevier Science B.V. All
rights reserved.
JEL classi®cation: G21; G34
Keywords: Bank growth; Mergers and acquisitions; Branching

1. Introduction
Perhaps the most important decision that managers of ®nancial institutions
make is how to grow their organization. Until recently, growth choices for

*

Corresponding author. Tel.: +1-601 266-6420; fax: +1-601 266-4630.
E-mail address: Cyree@cba.usm.edu (K.B. Cyree).

0378-4266/00/$ - see front matter Ó 2000 Elsevier Science B.V. All rights reserved.
PII: S 0 3 7 8 - 4 2 6 6 ( 9 9 ) 0 0 0 4 9 - 7

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banks were limited by the Glass±Stegall Act or interstate branching laws, but
over the past decade restrictions on expansion have gradually been eroded. For
example, the recent passage of the Riegle±Neal Banking Act allows banks to
branch across state lines. In addition, banks can more easily acquire banks
across state lines and convert them to branches without costly multi-bank
holding company structures, or build de novo branches across state lines.
Banks can also grow through product expansion into other related areas, such
as real-estate or consumer lending. 1 Due to weakening of the Glass±Steagall
Act, banks have and are more likely to enter into underwriting securities
thereby expanding the bankÕs products. Deregulation as well as non-bank and
global competition has made the growth choice decision more critical in this
new era of banking.
Prior research has studied the e€ect of branching, bank acquisition, or

product expansion as independent choices and activities, and not as a part of a
consolidated growth strategy. In this paper, determinants of bank growth
choices are reviewed in a two-stage model of choice. The ®rst stage models the
choice to grow versus not to grow externally , and the second stage models the
choice of growth type. With the recent legislative changes concerning bank
expansion activity, it is important to determine what operating, regulatory,
competitive, structural, and asset/liability characteristics are likely to cause
choice of one growth strategy versus another. If, for example, capital precludes
a bank from growth or from growing via one method of expansion, this information is important to regulators and bank managers.
Recent research has integrated the three separate bank growth choices into a
single model of performance. Cyree et al. (1998) ®nd that during the 1989±1993
period, banks that acquire other banks or Product Expand have lower changes
in performance than branching banks. These ®ndings suggest that during some
time periods there is di€erential performance of growth strategies. In this
study, we explore bank speci®c variables and their relationship to the bank
growth choice and type of growth choice. Given that di€erent strategies can
have performance implications for the banking ®rm, it is important to discover
the determinants of bank growth and for those that choose external means of
growth, the determinants of the particular growth choice.

2. Literature
Research in the area of bank growth has examined growth choices in isolation, such as studying bank mergers. We review the literature from separate

1

Banks in this paper is used generally and can indicate either a bank or bank holding company.

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

711

growth areas, although our paper is concerned with these areas as related to
choices of growth in a single model.
2.1. Branching literature
Most studies of branching in the banking industry examine branch cost
functions. The approach has limitations since no consensus exists on the type
of cost function to use, or on the appropriate bank inputs and outputs. Benston et al. (1982) use a translog cost equation and ®nd economies of scale for
branch banks and diseconomies for unit banks. The economies of scale for
branch banks disappear above US$25 million in deposits. Nelson (1985) ®nds
the addition of a branching convenience proxy in a cost equation model implies

that banks may operate branches at levels of output above minimum cost.
Nelson's ®ndings suggest that if convenience were not a factor, banks should
have one branch and operate at US$200 million in output.
2.2. Merger and acquisition literature
The study of bank acquisition related variables as determinants in takeovers
has largely, been ignored, except for Cheng et al. (1989), Madura and Wiant
(1994), and Benston et al. (1995). Most studies that study bank takeovers, use
event study, operating performance, or X-eciency methodology.
Event study results are mixed for acquirers with some research indicating
positive gains to acquirers on announcement of a takeover (see Cornett and
De, 1991), and some indicating negative returns to bidders (see Baradwaj et al.,
1992; Madura and Wiant, 1994). Together, event study results indicate acquirers can gain or lose in takeovers depending on the time period of study and
other factors such as in-market mergers or FDIC assistance.
Operating performance studies typically compare pre- and post-merger
performance variables for merging banks to a control group of non-acquiring
banks. The conclusion from these studies is that M&A activity improves performance only in a few limited cases. 2 For example, Rose (1987) ®nds that for
federally chartered banks engaged in mergers from 1970 to 1980, post-merger
pro®tability did not increase as compared to pre-merger pro®tability.
Several studies use purchase price-to-book or similar ratios of takeovers to
study the e€ects of bank mergers. Beatty et al. (1987) review the purchase priceto-book ratio of bank takeovers in 1984 and 1985. They ®nd purchase price-tobook is related positively and signi®cantly to ROE of the target, the Her®ndahl

index, and a binary variable that equals one for unit banking states. The

2

For a summary, see Rhoades (1994).

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premium is negatively related to the ratio of Treasury securities to total assets,
the ratio of loans to total assets, the ratio of loan loss provision plus equity to
total assets, and two measures indicating payment method. Benston et al.
(1995) use purchase price less the price one-month before the merger. Benston
et al. (1995) ®nd that as the variance of target and acquirer assets increases, the
purchase premium declines. They also ®nd that as target to asset size falls, the
purchase premium increases. Their ®ndings are consistent with the hypothesis
that managers attempt to diversify earnings and not with the hypothesis that
managers attempt to maximize the deposit insurance put option.
The X-eciency and economy of scale and scope literature typically tests

bank mergers for improvement in costs, revenues, or pro®ts from takeover
activity. Most studies ®nd little or no improvement in cost eciency as measured by the distance from the best-practice cost eciency frontier (see Berger
and Humphrey, 1992; Berger et al., 1993). Akhavein et al. (1997) use a frontier
pro®t function to study eciency and price e€ects of mergers and ®nd an increase in pro®t eciency for large banks. Peristiani (1997) ®nds that X-eciency declines in 2±4 years after a merger using a control group of nonmerging
banks as a comparison. The majority of the improvement in pro®t eciency is
from increasing revenues due to changes in output towards more lending.
2.3. Product expansion literature
Wall (1987) reviews the e€ect of a nonbank subsidiary on the return on
equity of a bank and the standard deviation of the return on equity. Return on
equity is substantially higher for nonbank subsidiaries. The average standard
deviation is also much higher for nonbanking subsidiaries. Wall ®nds that the
risk of failure is much greater for nonbank subsidiaries. He shows that risk is
reduced with the addition of nonbanking subsidiaries for the riskiest banks, but
that risk is increased for the least risky banks. The mean e€ect on the BHC is
statistically insigni®cant in most cases.
Liang and Savage (1990) ®nd that nonbank subsidiaries have higher pro®t
margins, although they represent a relatively small part of BHC total assets.
An analysis of the probability of insolvency suggests that commercial ®nance,
mortgage banking, consumer ®nance, and leasing nonbank subsidiaries are
riskier than aliated bank subsidiaries. Boyd et al. (1993) ®nd that BHCs in

simulated mergers with securities, real-estate, real-estate development, and
insurance agent/broker ®rms increase the risk of failure at virtually any portfolio weight. The simulation indicates that mergers with life insurers or
property/casualty insurers with portfolio weights from 16% to 20% reduces
risk.
Bhargava and Fraser (1998) use a multivariate regression model to study
prior e€ects of four Federal Reserve Board decisions that allowed commercial
banks to participate in investment banking through Section 20 subsidiaries.

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713

When the less expansive powers to underwrite mortgage securities and municipal revenue bonds were initially proposed in April 1987, banks that participated, experienced positive and signi®cant abnormal returns. When these
powers were expanded in January 1989 to include corporate debt and equity, as
well as the subsequent decision to double the amount allowed in August 1996,
banks experienced negative abnormal returns and increases in risk. The 1996
decision by the Federal Reserve Board to increase the allowable Section 20
revenue to increase to 25% and ®nd no wealth e€ects for commercial banks or
investment banks. Their ®ndings indicate that bank expansion into investment
banking may not o€set the risks.

3. Data, hypotheses, and methodology
Prior research has not directly looked at the growth decision or the determinants of growth choice in a single model. However, research in this area
suggests that di€erent growth strategies can a€ect pro®ts, costs and equity
values. These prior independent ®ndings across strategies suggest the following
testable hypothesis: there are no regulatory, structural, market, bank-speci®c,
or performance characteristics that in¯uence bank growth choice. Since this
study is concerned with factors that in¯uence the choice to grow and the type
of growth choice conditional on growth, a model that can estimate the likelihood a bank chooses a given strategy is important. One such model that allows
estimation of the probability of choices in a utility maximizing framework is a
multinomial logistic model. Rational agents use a multinomial logistic model
to represent economic choices. The multinomial model has been used for many
years in economics, but is used less often in ®nance. McFadden (1973) developed the model's application in a study of consumer choice. The empirical
results can be used in rejecting or con®rming theories without the complications of many techniques, such as the appropriate cost function for a bank. The
methodology models choices considering the three growth choices under study,
as compared to only pairwise choices using traditional dichotomous logit
models.
Since the growth decision is contingent on the initial decision to grow, we
employ a Heckman (1976, 1979) correction that allows for estimation of the
probability of a particular strategy, given that a bank chooses to grow. 3 In the

second stage, the Heckman correction variable is used as a regressor as

3
We wish to thank an anonymous referee for suggesting a Heckman correction which explicitly
recognizes the two sequential choices of growth/no-growth and next, what type of growth.

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banks that grow choose among branching, bank acquisition, or product expansion. 4
3.1. Data
The initial sample is taken from the Federal Reserve for all banks that are
Fed members or bank holding companies that applied for growth activities
from 1983 to 1994. Cross-sectional variables are obtained from the Call and
Income reports and aggregated at the bank holding company level where
appropriate.
Banks are categorized into growth choice categories using two methods
during two strategic determination periods (1983±1988 and 1989±1994). The
®rst method categorizes banks into a growth category if the bank makes any

application to the Federal Reserve for any particular growth activity. This
method is referred to as the `Any Activity Method'. The second method assigns
banks into a given category if the growth choice is the bankÕs primary growth
method. This method is called the `Primary Activity Method'. The Primary
Activity Method assigns banks into a category only if the proportion of the
activity is 50% or more of all growth activity for single strategies and 25% for
each activity under joint strategies. 5 These two separate strategic categorization methods compare banks that use one growth activity in the case of the
Any Activity Method and those that choose a primary strategy, yet also use
other growth methods where possible in the case of the Primary Activity
Method. Banks in the no-growth sample consist of Federal Reserve member
banks or holding companies that do not ®le an application for growth during
the sample period. 6
The correction for omitted variables, in this case the probability that a bank
chooses growth over no growth, is based on Heckman (1976, 1979) and is
applied in a two-step logistic model by Amemiya (1978). The methodology
allows for estimation of the probability of a particular growth strategy, such as
bank acquisition, contingent on the choice of the bank to grow. The ®rst stage
of the two-step process involves estimating a probit model as suggested by
Heckman, and then utilizing the results in the second stage. In the ®rst stage,
the probit equations in a simple linear case are:

4
Berger et al. (1998) have shown that mergers of bank charters have di€erent e€ects on lending
behavior. While this type of growth could be considered another growth category in a study of a
single event, we chose not to separate this category since our concern is a strategy over a multi-year
period.
5
The de®nition was changed to 40% and 60% for singular strategies and 20% and 30% for joint
strategies and did not materially a€ect the results.
6
Although using only Fed members has potential for introducing bias into the sample, the data
are not available for non-members who choose not to grow.

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

Pi1 ˆ b0;1 ‡ bi;1 Xi;1 ‡


‡ bk;1 Xk ;
Pi2 ˆ b0;2 ‡ bi;2 Xi;1 ‡


‡ bk;2 Xk ;

715

…1†

where P1 is the probability of a bank choosing not to grow, P2 the probability
of bank i choosing some form of growth (branching, bank acquisition, or
product expansion), and X and b are independent variables and coecients,
respectively. 7 The methodology employed by Heckman uses information from
the probability of being in group one or two in the second stage. The relevant
variable from the ®rst stage is Lambda:
ki ˆ

/…Zi †
;
1 ÿ U…Zi †

…2†

where Z is ÿX b=r from Eq. (1) using vector notation and suppressing the
subscript, and / and U are the density and distribution function for a standard
normal variable, respectively.
In the second stage, the multinomial model can be applied to the growth
strategy choice of bank managers, given that a bank chooses to grow. This
assumes that bank managers are rational and are maximizing shareholder
wealth, which is an argument of the manager's utility function. Since shareholder wealth maximization is standard to ®nance theory, this assumption
presents no unreasonable problems for use of the model to study bank growth
strategy choices.
In the case, ignoring the grow/no-growth decision in stage one, the model
would be:
Pi1 ˆ b0 ‡ b1 X1 ‡


‡ bk Xk ;
Pi2 ˆ b0 ‡ b1 X1 ‡


‡ bk Xk ;

…3†

Pi3 ˆ b0 ‡ b1 X1 ‡


‡ bk Xk ;
where Pij is the probability of the ith bank's growth strategy choice and equals
1, 2, or 3 where j ˆ 1 implies branching, j ˆ 2 implies acquiring banks, and j ˆ 3
implies product expansion. The X variable represents vectors of independent
variables and the betas are coecients. The fact that every observation is assigned to only one group can be used since Pi1 ‡ Pi2 ‡ Pi3 ˆ 1 for every i.
Through substitution and taking the log of both sides of the equation, the
following general model emerges for a three-choice situation:

7

We use the same independent variables in the ®rst-stage Probit model with the exception of
omitting the securities to assets variable. Heckman indicates that for the model to be identi®ed, at
least one variable should not be included in both the ®rst and second stage independent variables.

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K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

1
;
1 ‡ expf 1 ‡ expf 2
expf 1
;
P …Y ˆ 2† ˆ
1 ‡ expf 1 ‡ expf 2
expf 2
:
P …Y ˆ 3† ˆ
1 ‡ expf 1 ‡ expf 2
P …Y ˆ 1† ˆ

…4†

In the current case, 1 denotes growth through branching, 2 growth through
acquiring banks, and 3 denotes growth through acquiring nonbanks so each
equation represents the probability of being in group Y relative to the other
choices. The growth choices were limited to the singular strategies as opposed
to joint strategies for several reasons. First, di€erences between growth strategies are more apparent using singular growth strategies. Secondly, as McFadden (1973) discusses, sample size is reduced and the similarities across
choices create higher cross elasticities among alternatives than among dissimilar choices. Thus, only banks that are assigned singular strategies (branch,
Bank Acquire, and Product Expand) enter the multinomial analysis.
The discussion of the multinomial model ignores the sequential nature of
bank manager's decision making. That is, bank managers choose to grow or
not, and then, contingent on the choice to grow, will choose the type of growth.
Hence, the probabilities in the second stage are really conditional probabilities
such that the growth choice in the second stage is contingent upon the decision
to grow. Therefore, the probabilities become: …Pj jPgrowth †, or the probability of
growth strategy j, contingent upon the choice to grow. To incorporate the decision to grow in the ®rst stage, the Lambda from Eq. (2) for every bank is added
to the cross-sectional variables. The functions, f1 , f2 , and f3 , are thus de®ned as:
Strategyj ˆ fj …STATEBR; MBHC; CHARTER; DENOVO;
INCGROW; MKTCONC; SECUR; LNASSETS;
NONPERFM; REALEST; COMMLOAN; INSTALL;
DEPOSITS; PURCHASE; CAPITAL LABOR;
PHYCAP; VROA; ROA; LAMBDA†:

…5†

The cross sectional variables are discussed below. The model is estimated
separately using return on equity, but the results are qualitatively similar and
are not reported here. All continuous variables are averaged over the period of
study.
The model estimates the probability of being in one group versus the other
two groups in a three choice case, given the decision to grow. Coecients are
reported relative to one of the groups that are omitted. Thus, a positive coecient implies that the bank is more likely to choose the activity relative to the
omitted group when all choices are taken into account. For example, if the

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

717

coecient for the bank M&A choice relative to the omitted group of branching
is positive, this implies the bank is more likely to enter bank M&A activity
rather than branching. Similarly, a negative coecient suggests that the bank is
more likely to branch relative to acquiring other banks. In the remainder of the
paper, coecients are discussed in the context of a group relative to the
omitted group.
A discussion of the variables and the expected impact of each of the variables on the probability a bank will choose a particular growth strategy is
shown below. The expectations are based on previous research where possible.
STATEBR is a binary variable that equals one if the bank is in a statewide
branching state and zero otherwise, and measures that bank's regulatory environment. Beatty et al. (1987) ®nd a negative, but insigni®cant, relation between a binary variable coded one for electronic statewide banking and zero
otherwise, and the purchase price to book ratio. The ®ndings of Beatty et al.
imply banks pay less premium for targets in statewide branching states. This
suggests that the expected sign is positive for this variable in the probability
equation for bank M&A activity relative to branching.
MBHC is a binary variable that indicates bank structure and equals one if
the bank is a multi-bank holding company and zero if the bank is a one-bank
holding company. Multi-bank holding companies have typically acquired
banks in the past and maintain the acquired bank in the holding company
structure. Thus, it is expected the coecient on MBHC will be positive in the
Bank Acquire relative to Branch equation and the Product Expand relative to
Branch equation. Since one-bank holding companies can grow only through
product expansion, it is expected the coecient on MBHC will be positive in
the Bank Acquire relative to Product Expand equation.
CHARTER is a binary variable that indicates regulatory environment and
equals one if the bank is federally chartered and zero otherwise. Since federal
regulators are concerned with the overall banking system and not necessarily a
particular state, it is hypothesized that federally regulated banks are more
likely to expand than state regulated banks. This suggests that the coecient in
the growth/no-growth model will be positive. If state regulators are more
concerned with maintaining control at the state level, such as opposing an
acquisition from a bank outside the state, the coecient will be positive in the
Bank Acquire and product expansion equations relative to Branch.
DENOVO is a binary variable equal to one if the bank is 5 years old or less
during the period of study. We expect that it is less likely for de novo banks to
grow and, if these banks choose to grow, they will select branching as their
growth choice due to lower commitment of resources. Thus, the expected sign
for both bank M&A and product expansion relative to the omitted branching
strategy is positive.
INCGROW is a variable that represents the average income growth over the
period of study for the state in which the highest holding company is located.

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K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

This variable is selected because Akhavein et al. (1997) ®nd that growth in state
income is negatively related to growth in ROA and ROE. Further, Akhavein
et al. ®nd correlation between bank prices and suggest that high growth rates in
state income do not necessarily predict ex post success in mergers and acquisitions. In the context of growth in general, we postulate that banks in states
with high income growth rates are less likely to grow, or grow through
branching as compared to bank acquisition or product expansion. Thus, a
positive sign is expected in the growth/no-growth model as well as the bank
acquisition or product expansion model relative to branching. However, the
results of Akhavein et al. suggest a negative equation if state income growth is
correlated to prices.
MKTCONC is the weighted Her®ndahl index, weighted by the proportion
of deposits at the holding company level where appropriate as shown in Berger
(1995). Berger ®nds that the weighted Her®ndahl index is negatively related to
ROA and ROE in 42 of 60 regressions across years and regulatory environments. Berger's results are similar when accounting for scale economies and
X-eciency, suggesting that market power does not necessarily increase
pro®tability. Akhavein et al. (1997) also use the Her®ndahl index to study
market power, however the variable is generally insigni®cant. Berger and
Humphrey (1992) use the Her®ndahl index as a control variable in studying the
eciency gains of megamergers, but the variable is largely insigni®cant. Beatty
et al. (1987) ®nd a positive relation between merger premiums and the Her®ndahl index. Berger et al. (1993) ®nd a positive correlation between total
ineciency and the Her®ndahl index and suggest ineciencies are likely not
due to market power. Collectively, these ®ndings suggest that the Her®ndahl
index as a measure of concentration does not necessarily predict increased
market power or the ability to overcome operating ineciency. We predict
banks that have high concentration, as measured by MKTCONC, are less
likely to grow, or if they grow will choose branching, indicating a negative
coecient for the growth/no-growth model and the Bank Acquire and Product
Expand versus branching equations.
SECUR is the proportion of securities to assets and is a measure of bank
liquidity. Berger et al. (1996) use securities as a measure of `other assets'.
Akhavein et al. (1997) use total securities as a bank input in their study of the
e€ects of bank megamergers. If securities to assets are a source of liquidity for a
bank anticipating growth activity, the coecient for Bank Acquire or product
expansion relative to branching would be positive. On the other hand, if high
proportions of securities indicates low loan demand, the coecient in the Bank
Acquire relative to Product Expand equation would be negative as banks desire to grow out of markets with low loan demand.
LNASSETS is the log of total asset size of the bank. We hypothesize that
larger banks are more likely to grow since they have a lower expenditure as a
proportion of assets. This implies a positive coecient in the growth/no-growth

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

719

model. The expected coecient for the bank M&A equation relative to
branching is positive since larger banks are more likely to enter M&A activity
rather than branching activity. The same rationale holds for the product expansion probability equation relative to branching since it is expected that
larger banks are more likely to expand products. Cheng et al. (1989) ®nd acquirer total asset growth is negatively related to the target purchase price-tobook ratio. This suggests that banks are willing to pay more for growth in
assets in bank acquisitions. However, the implications of these ®ndings are
unclear for growth choice relative to all three growth strategies. Since larger
banks are more likely to expand products, the expected coecient for
LNASSETS in the Bank Acquire relative to Product Expand equation is
negative.
NONPERFM is the proportion of non-performing loans to assets and is a
measure of loan portfolio quality and risk. As such, this measure of risk may be
less subject to manipulation by bank managers than chargeo€s. We expect
banks that have high proportions of nonperforming loans would desire to grow
into other areas for diversi®cation and performance reasons. Thus, the expectation is that the coecient for this variable will be positive in the growth/
no-growth model and positive in the Bank Acquire and product expansion
equations relative to branching. To the extent that this variable measures poor
lending decisions, there could be a desire to move into non-bank products, thus
the coecient for Bank Acquire relative to product expansion would be
negative.
The three lending variables, REALEST, COMMLOAN, and INSTALL, are
the real-estate, commercial loan, and installment loan portfolios scaled by total
assets of the bank. These variables are used since they are generally agreed as
bank outputs in the scale and scope eciency literature (see Berger and
Humphrey, 1992; Berger et al., 1993; Berger et al., 1996). The breaking down of
the loan portfolio into three groups allows us to separate the e€ects of lending
by type, say from a retail focused to commercial lending focused bank. Since
these loan types relative to assets are a proxy for how `loaned-up' a bank is in a
particular type of lending, the expected coecient is positive for the growth/nogrowth equation and the Bank Acquire probability equation relative to
Branch. The expected sign for the Product Expand equation is positive since as
a bank is loaned up, product expansion becomes more likely so the bank can
create new growth opportunities.
DEPOSITS is total deposits divided by total assets. Deposits are used in the
X-eciency literature as a bank output (see Berger and Humphrey, 1992). In
the cross section, Berger and Humphrey (1992) use merging bank ex ante deposits and ®nd a positive relation to total eciency. Cheng et al. (1989) ®nd
acquiring banks pay signi®cantly higher premiums for banks with higher deposit growth. Their ®ndings indicate that banks with low deposit levels
will acquire to raise the level of deposits. We hypothesize that higher deposit-

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to-assets indicates less likelihood of in-market growth, thus the coecient for
the growth/no-growth model is expected to be positive. The previous empirical
results also suggest that banks with high proportions of deposits will choose to
branch, thus the expected sign for the Bank Acquire and Product Expand
relative to Branch equations is negative.
PURCHASE is purchased funds scaled by assets. This is a measure of the
extent to which a bank must leave its home market(s) to fund operations.
Berger et al. (1993) use purchased funds as a variable input in their pro®t
function model of bank eciency. As banks must rely on outside sources to
fund operations, it is likely that the bank would desire to grow and expand out
of its local market. Thus, we expect the coecient to be positive in the growth/
no-growth model and positive in the Bank Acquire and Product Expand
equations relative to branching.
CAPITAL is the bankÕs equity to asset ratio. If banks that have low capital
are excluded from growth, the coecient in the growth/no-growth model will
be positive. However, if banks do not grow because growth reduces the capital
ratio, the coecient will be negative in the growth/no-growth model. If banks
enter into M&A activity to improve capital, then the coecient will be positive
for the Bank Acquire probability equation relative to Branch. If capital precludes a bank from entering into M&A activity due to small size, the coecient
for the Bank Acquire equation relative to Branch will be negative.
LABOR is the salary expense divided by the number of employees and is a
measure of labor `prices'. Berger et al. (1993) use this measure as an input into
the bank pro®t function. Akhavein et al. (1997) use it as an input to study the
e€ects of megamergers, and Berger et al. (1996) use the measure as input prices
while reviewing economies of scope. As eciency falls, it is hypothesized that
bank managers will choose to grow into other areas or product lines. Thus, the
expected coecients in the growth/no-growth model and Bank Acquire and
Product Expand equations relative to branching are positive.
PHYCAP is the physical capital of the bank as a proportion of assets. This
variable is an indicator of branches in place to a certain extent and is a measure of
®xed assets in place. Berger et al. (1993), and Berger et al. (1996) use this measure
as an input while studying the pro®t function and economies of scope, respectively. If large assets in place preclude a bank from growing, the coecient for this
variable will be negative in the growth/no-growth model. This variable may also
indicate the willingness to branch in the past, thus the expected sign is negative in
the Bank Acquire and Product Expand equations relative to branching.
VROA is the variance of return on assets. If earnings are relatively volatile, a
bank may desire to expand products or expand geographically to diversify the
earnings stream. Benston et al. (1995) ®nd that the variance of both target and
acquirer ROA are negatively related to purchase price bid premiums. The
Benston et al. ®ndings suggest that banks acquire other banks to diversify
and smooth out earnings ¯uctuations (i.e., managerial interest). Their ®ndings

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

721

indicate the likelihood that banks with high ROA variability will choose to
grow through either M&A activity or product expansion. Thus, the expected
sign for VROA is positive in the growth/no-growth as well as the Bank Acquire
and Product Expand relative to branching equations.
ROA is return on assets and is our performance variable along with ROE,
which has similar results that are not reported here. 8 As noted by Berger
(1995, p. 414), `The pro®tability measures, after-tax ROA and ROE, are
standards in bank research'. If performance is a determinant of growth choice,
the coecient will be signi®cant in the growth/no-growth model. For example,
if poor ROA leads a bank to enter into higher margin product expansion (see
Liang and Savage, 1990), the coecient will be positive in the Product Expand
probability equation relative to Branch and the equation relative to Bank
Acquire. If banks are successful using a given strategy, say bank M&A activity,
then the bank is likely to continue that strategy. If this is the case, then the
coecient would be positive in the Bank Acquire relative to Product Expand
probability equation. If the performance is a result of the choice and not a
determinant, the coecient will be insigni®cant.

4. Empirical results
Table 1 shows means of selected variables and pairwise t-statistics by time
period for di€erences in means across all growth and no-growth category
combinations. As shown, banks that do not grow are signi®cantly smaller,
have lower deposits to assets, and have higher capital ratios than banks that
choose to grow. These results hold for both time periods and when comparing
no-growth banks to banks that choose to grow a particular way. No-growth
banks have a signi®cantly higher ROA than branching banks and a signi®cantly lower ROA and ROE versus product expanding banks in the 1983±1988
period.
Comparing means for types of growth activities, branching banks have
lower deposits to assets and ROA, and higher capital ratios that both bank
acquirers and product expanding banks in the 1983±1988 period. Return on
equity is signi®cantly higher for product expanding banks than those which
branch in the 1983±1988 time period. In the 1989±1994 time period, product
expanding banks are signi®cantly larger than both branching banks and banks
that acquire other banks. While these results suggest di€erences between banks
that grow and those that choose a particular type of growth, they should be

8

ROA and ROE were not included together in the model because of high multicollinearity.

722

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

Table 1
Means and pairwise t-statistics by group and period for selected variables
Panel A: Group means by time period 1983±1988
Variable

Nogrowth

Growth

Branch

Bank
Acquire

Product
Expand

Assets (US$000s)
Deposits-to-assets
Capital ratio
ROA
ROE

56930
0.8747
0.1012
0.0054
0.0460

118568
0.8836
0.0880
0.0062
0.6493

71253
0.8527
0.1141
0.0015
0.0320

75937
0.8859
0.0888
0.0064
0.0581

148170
0.8880
0.0827
0.0077
0.0898

1989±1994
Assets (US$000s)
Deposits-to-assets
Capital ratio
ROA
ROE

90848
0.8778
0.0963
0.0073
0.0537

200073
0.8838
0.0885
0.0076
0.0800

111449
0.8864
0.0854
0.0060
0.0609

95669
0.8894
0.08870
0.0082
0.0757

281946
0.8864
0.0876
0.0076
0.0809

Panel B: Pairwise t-statistics for di€erences in group means 1983±1988
Branch
versus
Bank
Acquire

Branch
versus
Product
Expand

Bank
Acquire
versus
Product
Expand

)2.249
)2.933
8.835
)3.500
)4.380

)0.155
)3.182
3.047
)3.108
)0.634

)1.603
)3.198
3.783
)4.280
)3.710

)1.662
)0.417
2.276
)1.547
)0.806

)2.437
)0.506
2.756
)0.298
)1.475

0.539
)0.404
)0.704
)1.471
)0.658

)2.059
)0.884
)0.504
)1.080
)0.994

)2.354
1.573
0.219
0.394
)0.242

Nogrowth
versus
growth

Nogrowth
versus
branch

Nogrowth
versus
Bank
Acquire

Nogrowth
versus
Product
Expand

Assets (US$s)
Deposits-to-assets
Capital ratio
ROA
ROE

)3.662
)3.709
7.694
)1.634
)0.987

)0.555
2.273
)1.740
2.874
0.897

)1.196
)4.050
5.714
)1.388
)0.313

1989±1994
Assets (US$s)
Deposits-to-assets
Capital ratio
ROA
ROE

)3.004
)1.989
3.795
)0.493
)1.770

)0.705
)1.368
3.320
1.161
)0.354

)0.393
)2.597
2.111
)0.816
)1.067

*

Signi®cant at 5% level.
Signi®cant at 1% level.

**

interpreted with caution since they are pairwise results that do not account for
other variables as do the models in the following sections.
4.1. Growth versus no-growth probit model results
The ®rst stage of the multivariate analysis involves the growth/no-growth
probit model as suggested by Heckman (1979). The two stage model is required
to avoid the bias of not accounting for the decision to grow or not to grow.

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

723

These results are interesting in and of themselves because the decision to expand or not is one of the most important in the growth process. Since all
growth requires relatively large expenditures of real resources, the decision is
important to managers, shareholders and bank regulators.
Table 2 contains the results from the probit model with 0 ˆ no-growth and
1 ˆ growth in the 1983±1988 and 1989±1994 time periods. As shown, banks
Table 2
Probit model from 1983 to 1988 and 1989±1994 for banks that grow versus do not growa
Variable

INTERCEPT
STATEBR
MBHC
CHARTER
DENOVO
INCGROW
MKTCONC
LNASSETS
NONPERFM
REALEST
COMMLOAN
INSTALL
DEPOSITS
PURCHAS
CAPITAL
LABOR
PHYCAP
VROA
ROA

1983±1988, No-growth (N ˆ 2713), 1989±1994, No-growth (N ˆ 2694),
relative to growth (N ˆ 521)
relative to growth (N ˆ 266)
Estimate

p-value

Estimate

p-value

)1.0711
)0.0069
0.0814
)1.4219
)0.2878
)3.5551
)0.0851
0.2323
114871
)0.0575
0.0701
0.3338
0.0949
0.1556
)2.6780
)4.5119
2.3339
)58.0520
)6.4423

0.2634
0.9277
0.2188
0.0001
0.0234
0.0446
0.0001
0.0001
0.0036
0.8639
0.8714
0.4743
0.8936
0.2418
0.0266
0.0001
0.3337
0.5432
0.1900

)2.9693
)0.2124
0.3381
)1.5612
0.1523
)16.6391
)0.0294
0.2973
)463486
0.3588
)0.1557
0.1154
1.2349
0.3841
0.6252
)3.4659
)0.1921
)327.5878
)8.9720

0.0244
0.0302
0.0002
0.0001
0.2049
0.0080
0.1462
0.0001
0.1345
0.2985
0.8004
0.8401
0.2135
0.3277
0.7156
0.0001
0.9588
0.1862
0.1820

a
STATEBR is one if the bank is in a statewide banking state and zero otherwise; MBHC is one if
the bank is organized as a multi-bank holding company and zero otherwise; CHARTER is one if
the bank is federally chartered and zero otherwise; DENOVO is one of the bank is 5 years old or
less; INCGROW is the average growth rate in state income for the home state; MKTCONC is the
Her®ndahl Index weighted by the proportion of deposits for each bank in the holding company;
SECUR is securities divided by assets; LNASSETS is the log of average bank assets; NONPERFM
is the average of nonperforming loans to assets; REALEST is the average real-estate loan portfolio
size divided by assets; COMMLOAN is the average commercial loan portfolio size scaled by assets;
INSTALL is the average installment loan portfolio size divided by assets; DEPOSITS is the average deposit to asset ratio; PURCHASE is the average amount of purchased funds divided by
assets; CAPITAL is the average capital ratio; LABOR is the average of salaries divided by number
of employees; PHYCAP is the average physical capital; VROA is the variance of return on assets;
ROA is the average return on assets; LAMBDA is inverse of the Mills Ratio from the ®rst stage
Probit Model which accounts for the decision to grow versus the decision not to grow. T-statistics
are beneath in parentheses. Overall Chi-square statistic ˆ 251.06.
*
Signi®cant at 5% level.
**
Signi®cant at 1% level.

724

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

that are federally chartered are less likely to grow in both time periods. This
result is contrary to expectations, and suggests state chartered banks are
more likely to grow. Plausible explanations are that state chartered banks are
smaller and have higher capital, although we leave the testing of these to
future research. Banks in states with higher income growth are less likely to
grow in both time periods as expected. This also suggests that banks in states
with lower income growth are likely to grow, perhaps to expand to areas with
better economic prospects. In both time periods, larger banks are more likely
to grow, as expected, and shown by the positive coecient for LNASSETS.
The coecient for LABOR is negative and highly signi®cant in both time
periods indicating an increase in labor prices reduces the likelihood of
growth. The ®nding of a negative coecient is not as expected and could
indicate that banks that have high labor costs or low eciency are unable to
expand.
In the earlier time period (1983±1988) but not the latter, the coecients for
DENOVO, MKTCONC, NONPERFM and CAPITAL are signi®cant. The
DENOVO result indicates that de novo banks are less likely to grow, as expected. The market concentration variable shows that banks in highly concentrated markets are less likely to grow. The ®nding of MKTCONC is
consistent with previous research on market power and eciency. The positive
coecient for NONPERFM shows that banks with relatively poor performing
loan portfolios are more likely to grow, as expected. The ®nding in the earlier
period of a negative and signi®cant coecient on CAPITAL indicates that
capital does not preclude a bank from growing. This result for capital also
suggests that banks which have not grown may have higher capital than those
which choose to use capital to fund growth.
The coecients for STATEBR and MBHC in Table 2 are signi®cant in the
1989±1994 time period, but not in the earlier time period although they have
the same sign. The negative coecient for STATEBR indicates that banks in
statewide branching states are less likely to grow as expected. The positive
coecient for MBHC shows that multibank holding companies are more likely
to grow, ceteris paribus, as expected.
4.2. Type of bank growth multinomial logistic model results
The multinomial model is used to indicate which variables in¯uence the type
of growth decision, while incorporating the growth/no-growth decision from
the ®rst stage Probit model. The model estimates probabilities relative to an
omitted group, while taking into account all the available choices under study.
The model is used to test for ®nancial, competitive, and regulatory determinants on bank growth choice.
Table 3 shows the results from the multinomial model from Eq. 4 using the
Any Activity Method to assign strategies. The model determines if there are

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K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

Table 3
Multinomial logistic model from 1983 to 1988 for banks that branch only, acquire banks only, and
Product Expand only using the Any Activity Method to assign strategiesa
Variable

CONSTANT
STATEBR
MBHC
CHARTER
DENOVO
INCGROW
MKTCONC
SECUR
LNASSETS
NONPERFM
REALEST
COMMLOAN
INSTALL
DEPOSITS
PURCHASE
CAPITAL
LABOR
PHYCAP
VROA
ROA
LAMBDA

Bank Acquire only
(N ˆ 248), relative to
Product Expand only
(N ˆ 132)

Bank Acquire only
(N ˆ 248), relative to
branch only (N ˆ 49)

Product Expand only
(N ˆ 132), relative to
branch only (N ˆ 49)

Estimate

t-statistic

Estimate

t-statistic Estimate

t-statistic

7.04590
0.97711
0.53307
5.54350
)1.82210
)35.96500
)0.06641
)0.23310
)0.59616
)66759
0.04377
)1.59660
)2.44060
5.67550
0.10004
5.95740
2.95420
)19.04400
)45.23600
12.83300
)2.08940

0.561
1.542
0.802
0.636
)1.033
)1.609
)0.142
)0.095
)0.511
)0.117
0.016
)0.505
)0.645
1.116
0.151
0.341
0.119
)0.894
)0.067
0.310
)0.269

6.47230
1.80870
2.20710
10.54800
)0.00303
)19.85200
0.38335
)3.56940
)0.97393
)385260
)0.49656
)6.20660
)7.80690
7.72690
)0.57664
13.74400
21.06500
)25.92000
)399.48000
70.06800
)6.59220

0.513
2.677
3.113
1.164
)0.002
)0.839
0.783
)1.338
)0.793
)0.634
)0.172
)1.773
)1.839
1.584
)0.822
0.731
0.808
)1.108
)0.349
1.521
)0.812

0.081
)2.382
)4.613
)1.411
)1.858
)1.449
)2.060
2.119
0.683
1.159
0.331
2.130
2.143
)0.459
2.003
)0.772
)1.592
0.513
0.364
)1.904
1.349

0.57351
)0.83155
)1.67410
)5.00420
)1.81910
)16.11300
)0.44977
3.33630
0.37777
318500
0.54033
4.61000
5.36630
)2.05140
0.67668
)7.78670
)18.11100
6.87650
354.22000
)57.23500
4.50290

a

STATEBR is one if the bank is in a statewide banking state and zero otherwise; MBHC is one if
the bank is organized as a multi-bank holding company and zero otherwise; CHARTER is one if
the bank is federally chartered and zero otherwise; DENOVO is one of the bank is 5 years old or
less; INCGROW is the average growth rate in state income for the home state; MKTCONC is the
Her®ndahl Index weighted by the proportion of deposits for each bank in the holding company;
SECUR is securities divided by assets; LNASSETS is the log of average bank assets; NONPERFM
is the average of nonperforming loans to assets; REALEST is the average real-estate loan portfolio
size divided by assets; COMMLOAN is the average commercial loan portfolio size scaled by assets;
INSTALL is the average installment loan portfolio size divided by assets; DEPOSITS is the average deposit to asset ratio; PURCHASE is the average amount of purchased funds divided by
assets; CAPITAL is the average capital ratio; LABOR is the average of salaries divided by number
of employees; PHYCAP is the average physical capital; VROA is the variance of return on assets;
ROA is the average return on assets; LAMBDA is inverse of the Mills Ratio from the ®rst stage
Probit Model which accounts for the decision to grow versus the decision not to grow. Overall Chisquare statistic ˆ 224.54.
*
Signi®cant at 5% level.
**
Signi®cant at 1% level.

factors that in¯uence the bankÕs growth choice for the 1983±1988 period (the
strategic determination period). None of the independent variables are significant in the Bank Acquire relative to Branch equation suggesting little di€er-

726

K.B. Cyree et al. / Journal of Banking & Finance 24 (2000) 709±734

ence in this growth decision, contingent on the decision to grow. For the
Product Expand relative to Branch equation, the only signi®cant variables are
STATEBR and MBHC, which are both positive and highly signi®cant. This
equation indicates that product expanding banks are more likely to be in a
statewide branching state and be formed as a multibank holding company as
compared to branching.
The third column in Table 3 indicates Bank Acquire relative to Product
Expand for the Any Activity Method. Bank acquirers are more likely to be in
a non-statewide branching state as shown by the negative coecient for
STATEBR. This result is as expected and indicates regulatory environment
impacts the type of growth chosen. The ®nding of STATEBR coupled with
the negative and signi®cant MKTCONC variable indicates that banks in
highly competitive environments, perhaps through competitors with many
branches, are more likely to grow through product expansion. Multibank
holding companies are more likely to Product Expand, counter to expectations,
and could be an indication of banks with diverse banking operations seeking to
add products to the banking line. Banks with high proportions of securities are
more likely to Bank Acquire relative to Product Expand, which could indicate a
lack of lending business, perhaps due to highly competitive markets, or a liquidity source for future bank acquisitions. Bank acquirers also have higher
proportions of commercial and installment loans which indicates the desire to
grow outside the home market to avoid the lending competition for the market
in which they operate. Those banks with higher reliance on purchased funds are
more likely to acquire other banks than Product Expand as shown by the
positive coecient on PURCHASE. This result suggests that banks which leave
their home market for a funding source are still committed to traditional
banking activity and are not as likely to grow through product expansion, all
else constant.
Table 4 contains the results of the 1983±1988 time period, but
growth strategies are assigned using the Primary Activity Method. For Product
Expand versus Branching, the coecients for STATEBR and MBHC are
positive and signi®cant as is the case in Table 3 when strategies are assigned
using the Any Activity Method. The deposits to assets