Determinants of bank growth choice
Ken B. Cyree
a,*, James W. Wansley
b, Thomas P. Boehm
ba
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.Ó2000 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
www.elsevier.com/locate/econbase
*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 matterÓ2000 Elsevier Science B.V. All rights reserved.
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.1Due 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 eect 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 in-formation 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 dierential 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 dierent 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 iso-lation, such as studying bank mergers. We review the literature from separate
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. Ben-ston 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, orX-eciency 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 ac-quirers 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 per-formance only in a few limited cases.2For 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 eects of bank mergers. Beatty et al. (1987) review the purchase book ratio of bank takeovers in 1984 and 1985. They ®nd purchase price-to-book 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
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-eciency 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 eciency as mea-sured by the distance from the best-practice cost eciency frontier (see Berger and Humphrey, 1992; Berger et al., 1993). Akhavein et al. (1997) use a frontier pro®t function to study eciency and price eects of mergers and ®nd an
in-crease in pro®t eciency 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 eciency is from increasing revenues due to changes in output towards more lending.
2.3. Product expansion literature
Wall (1987) reviews the eect 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 eect 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 aliated 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 port-folio weight. The simulation indicates that mergers with life insurers or property/casualty insurers with portfolio weights from 16% to 20% reduces risk.
When the less expansive powers to underwrite mortgage securities and mu-nicipal revenue bonds were initially proposed in April 1987, banks that par-ticipated, 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 eects for commercial banks or investment banks. Their ®ndings indicate that bank expansion into investment banking may not oset the risks.
3. Data, hypotheses, and methodology
Prior research has not directly looked at the growth decision or the deter-minants of growth choice in a single model. However, research in this area suggests that dierent growth strategies can aect 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 likeli-hood 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) devel-oped the model's application in a study of consumer choice. The empirical results can be used in rejecting or con®rming theories without the complica-tions 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.3In the second stage, the Heckman correction variable is used as a regressor as
banks that grow choose among branching, bank acquisition, or product ex-pansion.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.5These two separate strategic categoriza-tion 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 dierent eects 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 aect the results.
Pi1b0;1bi;1Xi;1 bk;1Xk;
Pi2b0;2bi;2Xi;1 bk;2Xk;
1
whereP1is the probability of a bank choosing not to grow,P2the probability
of bank i choosing some form of growth (branching, bank acquisition, or
product expansion), and X andb are independent variables and coecients,
respectively.7The 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
/ Zi
1ÿU Zi
; 2
where Z is ÿXb=r from Eq. (1) using vector notation and suppressing the
subscript, and/andUare 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 share-holder 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:
Pi1b0b1X1 bkXk;
Pi2b0b1X1 bkXk;
Pi3b0b1X1 bkXk;
3
wherePijis the probability of theith bank's growth strategy choice and equals 1, 2, or 3 wherej1 implies branching,j2 implies acquiring banks, andj3
implies product expansion. The Xvariable represents vectors of independent
variables and the betas are coecients. The fact that every observation is as-signed to only one group can be used since Pi1Pi2Pi31 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:
P Y 1 1
1expf1expf2;
P Y 2 exp
f1
1expf1expf2;
P Y 3 exp
f2
1expf1expf2: 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 Yrelative to the other
choices. The growth choices were limited to the singular strategies as opposed to joint strategies for several reasons. First, dierences between growth strat-egies are more apparent using singular growth stratstrat-egies. Secondly, as McF-adden (1973) discusses, sample size is reduced and the similarities across choices create higher cross elasticities among alternatives than among dissim-ilar 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: PjjPgrowth, or the probability of
growth strategyj, contingent upon the choice to grow. To incorporate the de-cision 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, andf3, are thus de®ned as:
Strategyjfj 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.
coecient 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 coecient suggests that the bank is more likely to branch relative to acquiring other banks. In the remainder of the paper, coecients 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 vari-ables on the probability a bank will choose a particular growth strategy is shown below. The expectations are based on previous research where possible.
STATEBRis a binary variable that equals one if the bank is in a statewide
branching state and zero otherwise, and measures that bank's regulatory en-vironment. Beatty et al. (1987) ®nd a negative, but insigni®cant, relation be-tween 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.
MBHCis 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 coecient 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 coecient 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 coecient 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 coecient will be positive in the Bank Acquire and product expansion equations relative to Branch.
DENOVOis 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.
INCGROWis a variable that represents the average income growth over the
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 acqui-sitions. 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 environ-ments. Berger's results are similar when accounting for scale economies and
X-eciency, 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 eciency 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 ineciency and the Her®ndahl index and suggest ineciencies 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 ineciency. 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 coecient 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 eects of bank megamergers. If securities to assets are a source of liquidity for a bank anticipating growth activity, the coecient 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 coecient in the Bank Acquire relative to Product Expand equation would be negative as banks de-sire to grow out of markets with low loan demand.
LNASSETS is the log of total asset size of the bank. We hypothesize that
model. The expected coecient 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 ex-pansion probability equation relative to branching since it is expected that larger banks are more likely to expand products. Cheng et al. (1989) ®nd ac-quirer total asset growth is negatively related to the target purchase price-to-book 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 coecient for LNASSETS in the Bank Acquire relative to Product Expand equation is negative.
NONPERFMis 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 chargeos. 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 ex-pectation is that the coecient 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 coecient for Bank Acquire relative to product expansion would be negative.
The three lending variables,REALEST,COMMLOAN, andINSTALL, 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 eciency 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 eects 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 coecient is positive for the growth/no-growth 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.
DEPOSITSis total deposits divided by total assets. Deposits are used in the
X-eciency literature as a bank output (see Berger and Humphrey, 1992). In
deposit-to-assets indicates less likelihood of in-market growth, thus the coecient 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 eciency. 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 coecient to be positive in the growth/ no-growth model and positive in the Bank Acquire and Product Expand equations relative to branching.
CAPITALis the bankÕs equity to asset ratio. If banks that have low capital are excluded from growth, the coecient in the growth/no-growth model will be positive. However, if banks do not grow because growth reduces the capital ratio, the coecient will be negative in the growth/no-growth model. If banks enter into M&A activity to improve capital, then the coecient will be positive for the Bank Acquire probability equation relative to Branch. If capital pre-cludes a bank from entering into M&A activity due to small size, the coecient for the Bank Acquire equation relative to Branch will be negative.
LABORis 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 eects of megamergers, and Berger et al. (1996) use the measure as input prices while reviewing economies of scope. As eciency falls, it is hypothesized that bank managers will choose to grow into other areas or product lines. Thus, the expected coecients in the growth/no-growth model and Bank Acquire and Product Expand equations relative to branching are positive.
PHYCAPis 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, respec-tively. If large assets in place preclude a bank from growing, the coecient 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.
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.
ROAis 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 coecient 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 coecient 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 coecient 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 coecient will be insigni®cant.
4. Empirical results
Table 1 shows means of selected variables and pairwiset-statistics by time period for dierences 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 dierences between banks that grow and those that choose a particular type of growth, they should be
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.
Table 1
Means and pairwiset-statistics by group and period for selected variables
Panel A: Group means by time period 1983±1988
Variable
No-Assets (US$000s) 56930 118568 71253 75937 148170 Deposits-to-assets 0.8747 0.8836 0.8527 0.8859 0.8880 Capital ratio 0.1012 0.0880 0.1141 0.0888 0.0827 ROA 0.0054 0.0062 0.0015 0.0064 0.0077 ROE 0.0460 0.6493 0.0320 0.0581 0.0898
1989±1994
Assets (US$000s) 90848 200073 111449 95669 281946 Deposits-to-assets 0.8778 0.8838 0.8864 0.8894 0.8864 Capital ratio 0.0963 0.0885 0.0854 0.08870 0.0876 ROA 0.0073 0.0076 0.0060 0.0082 0.0076 ROE 0.0537 0.0800 0.0609 0.0757 0.0809
Panel B: Pairwiset-statistics for dierences in group means 1983±1988
No-These results are interesting in and of themselves because the decision to ex-pand 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 0no-growth and
1growth 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 1983±1988, No-growth (N2713), relative to growth (N521)
1989±1994, No-growth (N2694), relative to growth (N266)
Estimate p-value Estimate p-value
INTERCEPT )1.0711 0.2634 )2.9693 0.0244 STATEBR )0.0069 0.9277 )0.2124 0.0302
MBHC 0.0814 0.2188 0.3381 0.0002
CHARTER )1.4219 0.0001 )1.5612 0.0001 DENOVO )0.2878 0.0234 0.1523 0.2049 INCGROW )3.5551 0.0446 )16.6391 0.0080 MKTCONC )0.0851 0.0001 )0.0294 0.1462 LNASSETS 0.2323 0.0001 0.2973 0.0001 NONPERFM 114871 0.0036
)463486 0.1345 REALEST )0.0575 0.8639 0.3588 0.2985 COMMLOAN 0.0701 0.8714 )0.1557 0.8004
INSTALL 0.3338 0.4743 0.1154 0.8401
DEPOSITS 0.0949 0.8936 1.2349 0.2135
PURCHAS 0.1556 0.2418 0.3841 0.3277
CAPITAL )2.6780 0.0266 0.6252 0.7156 LABOR )4.5119 0.0001 )3.4659 0.0001
PHYCAP 2.3339 0.3337 )0.1921 0.9588
VROA )58.0520 0.5432 )327.5878 0.1862
ROA )6.4423 0.1900 )8.9720 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 av-erage deposit to asset ratio; PURCHASE is the avav-erage 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 statistic251.06.
*Signi®cant at 5% level. **
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 coecient for LNASSETS. The coecient 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 coecient is not as expected and could indicate that banks that have high labor costs or low eciency are unable to expand.
In the earlier time period (1983±1988) but not the latter, the coecients for DENOVO, MKTCONC, NONPERFM and CAPITAL are signi®cant. The DENOVO result indicates that de novo banks are less likely to grow, as ex-pected. The market concentration variable shows that banks in highly con-centrated markets are less likely to grow. The ®nding of MKTCONC is consistent with previous research on market power and eciency. The positive coecient 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 coecient 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 coecients 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 coecient for STATEBR indicates that banks in statewide branching states are less likely to grow as expected. The positive coecient 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 determi-nants on bank growth choice.
factors that in¯uence the bankÕs growth choice for the 1983±1988 period (the strategic determination period). None of the independent variables are signif-icant in the Bank Acquire relative to Branch equation suggesting little
dier-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 Bank Acquire only
Estimate t-statistic Estimate t-statistic Estimate t-statistic
CONSTANT 7.04590 0.561 6.47230 0.513 0.57351 0.081 STATEBR 0.97711 1.542 1.80870 2.677
)0.83155 )2.382 MBHC 0.53307 0.802 2.20710 3.113
)1.67410 )4.613 CHARTER 5.54350 0.636 10.54800 1.164 )5.00420 )1.411 DENOVO )1.82210 )1.033 )0.00303 )0.002 )1.81910 )1.858 INCGROW )35.96500 )1.609 )19.85200 )0.839 )16.11300 )1.449 MKTCONC )0.06641 )0.142 0.38335 0.783 )0.44977 )2.060 SECUR )0.23310 )0.095 )3.56940 )1.338 3.33630 2.119 LNASSETS )0.59616 )0.511 )0.97393 )0.793 0.37777 0.683 NONPERFM )66759 )0.117 )385260 )0.634 318500 1.159 REALEST 0.04377 0.016 )0.49656 )0.172 0.54033 0.331 COMMLOAN )1.59660 )0.505 )6.20660 )1.773 4.61000 2.130 INSTALL )2.44060 )0.645 )7.80690 )1.839 5.36630 2.143 DEPOSITS 5.67550 1.116 7.72690 1.584 )2.05140 )0.459 PURCHASE 0.10004 0.151 )0.57664 )0.822 0.67668 2.003 CAPITAL 5.95740 0.341 13.74400 0.731 )7.78670 )0.772 LABOR 2.95420 0.119 21.06500 0.808 )18.11100 )1.592 PHYCAP )19.04400 )0.894 )25.92000 )1.108 6.87650 0.513 VROA )45.23600 )0.067 )399.48000 )0.349 354.22000 0.364 ROA 12.83300 0.310 70.06800 1.521 )57.23500 )1.904 LAMBDA )2.08940 )0.269 )6.59220 )0.812 4.50290 1.349
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 av-erage deposit to asset ratio; PURCHASE is the avav-erage 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 Chi-square statistic224.54.
*Signi®cant at 5% level. **
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 coecient 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 li-quidity 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 coecient 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 coecients 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 variable is positive and signi®cant in the Product Expand relative to Branch equation indicating that as deposits to assets increase, banks are more likely to grow through product expansion than branching. In the Bank Acquire relative to Product Expand equation, STATEBR and MBHC are negative and signi®cant as is the case in Table 3.
Table 6 presents the results for the multinomial logit model in the 1989±1994 period using the Primary Activity Method to assign strategies. Unlike the in-signi®cant results for this time period in Table 5, several of the variables are
Table 4
Multinomial logistic model from 1983 to 1988 for banks that branch primarily, acquire banks primarily, and Product Expand primarily using the Primary Activity Method to assign strategiesa
Variable Bank Acquire primarily
Estimate t-statistic Estimate t-statistic Estimate t-statistic
CONSTANT )1.29870 )0.106 )4.27710 )0.347 2.98080 0.521 STATEBR 0.90028 1.654 1.65330 2.910
)0.75297 )2.635 MBHC 0.55215 0.903 1.86040 2.954
)1.30830 )4.543 CHARTER 2.24780 0.276 5.07740 0.618 )2.82720 )1.111 DENOVO )2.38230 )1.455 )0.96862 )0.580 )1.41320 )1.901 INCGROW )29.38800 )1.413 )18.01500 )0.843 )11.36700 )1.303 MKTCONC )0.33765 )0.766 )0.03855 )0.086 )0.29896 )1.842 SECUR )0.75424 )0.334 )2.94990 )1.225 2.19540 1.618 LNASSETS 0.11756 0.102 )0.14499 )0.124 0.26221 0.639 NONPERFM 355370 0.584 127570 0.206 227640 1.078 REALEST )2.84290 )1.175 )2.78800 )1.088 )0.05496 )0.039 COMMLOAN )3.28510 )1.103 )5.96930 )1.880 2.68390 1.487 INSTALL )2.72170 )0.830 )5.16760 )1.467 2.44510 1.264 DEPOSITS 7.22140 1.578 10.45400 2.185
)3.23150 )0.836 PURCHASE 0.13332 0.203 )0.32266 )0.476 0.45586 1.550 CAPITAL )2.00160 )0.124 2.36580 0.140 )4.36240 )0.535 LABOR )4.59780 )0.195 9.03180 0.377 )13.62300 )1.609 PHYCAP )8.02320 )0.412 )17.87600 )0.860 9.84840 0.885 VROA )320.790 )0.484 )765.6600 )0.748 444.89000 0.525 ROA )9.79290 )0.246 26.40300 0.623 )36.18600 )1.490 LAMBDA 1.07940 0.146 )1.48450 )0.199 2.56170 1.067
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; INC-GROW 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 se-curities 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 Chi-square statistic216.91.
signi®cant. In the Bank Acquire and Product Expand equations relative to branching, MBHC and LAMBDA are signi®cantly positive and MKTCONC and ROA are negative and signi®cant in each equation separately. This
indi-Table 5
Multinomial logistic model from 1989 to 1994 for banks that branch only, acquire banks only, and Product Expand only using the Any Activity Method to assign strategiesa
Variable Bank Acquire only
Estimate t-statistic Estimate t-statistic Estimate t-statistic
CONSTANT )38.6180 )0.780 )16.5350 )0.359 )22.0830 )0.967 STATEBR )0.7459 )0.294 )0.5276 )0.220 )0.2183 )0.185 MBHC 3.6140 0.892 5.2427 1.348 )1.6287 )0.843 CHARTER 9.5524 0.015 9.4436 0.015 0.1088 0.014 DENOVO )3.6663 )1.696 )2.8288 )1.385 )0.8374 )0.720 INCGROW )52.6040 )0.277 )96.3130 )0.537 43.7080 0.519 MKTCONC )0.2199 )0.593 )0.1553 )0.445 )0.0646 )0.339 SECUR 5.9585 1.476 4.4504 1.224 1.5081 0.506 LNASSETS 1.1620 0.345 1.0862 0.341 0.0758 0.052 NONPERFM 199110 0.286 )572590 )0.814 771690 1.727 REALEST 4.6963 0.910 4.2306 0.888 0.4658 0.152 COMMLOAN 1.3671 0.270 )3.9875 )0.824 5.3546 1.265 INSTALL )4.3764 )0.817 )2.4029 )0.485 )1.9735 )0.542 DEPOSITS 18.8980 0.973 )0.4049 )0.024 19.3030 1.609 PURCHASE 4.9320 0.531 1.6531 0.182 3.2788 0.879 CAPITAL 0.7940 0.041 )13.3400 )0.765 14.1340 1.102 LABOR )3.5008 )0.086 )3.1586 )0.082 )0.3424 )0.020 PHYCAP )10.2390 )0.389 2.9315 0.114 )13.1710 )0.643 VROA )1720.20 )0.397 )574.77 )0.144 )1145.400 )0.438 ROA )81.0090 )0.740 )92.1160 )0.897 11.1060 0.198 LAMBDA 8.6220 0.527 8.5032 0.543 0.1188 0.018 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; INC-GROW 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 se-curities 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 Chi-square statistic157.22. *Signi®cant at 5% level.
cates that multibank holding companies are more likely to grow through bank acquisition or separately through product expansion than branching, all else constant. The positive and signi®cant LAMBDA coecient suggests that
Table 6
Multinomial logistic model from 1989 to 1994 for banks that branch primarily, acquire banks primarily, and Product Expand primarily using the Primary Activity Method to assign strategiesa
Variable Bank Acquire primarily
Estimate t-statistic Estimate t-statistic Estimate t-statistic
CONSTANT )85.2330 )2.496 )64.2230 )2.019 )21.0100 )1.155 STATEBR )3.5710 )2.046 )2.9988 )1.815 )0.5722 )0.603 MBHC 6.8293 2.448 8.5933 3.160
)1.7640 )1.171 CHARTER )8.7342 )0.012 )8.4712 )0.011 )0.2630 )0.043 DENOVO )0.9083 )0.683 )0.2205 )0.178 )0.6878 )0.826 INCGROW )225.0000 )1.670 )259.930 )2.034 34.9310 0.531 MKTCONC )0.5217 )2.060 )0.5504 )2.266 0.0287 0.194 SECUR 4.3043 1.309 2.4259 0.782 1.8784 0.744 LNASSETS 4.2934 1.901 4.3324 2.036
)0.0390 )0.036 NONPERFM )751190 )1.438 )784280 )1.559 330900 0.134 REALEST 7.4078 1.809 7.0763 1.804 0.3315 0.126 COMMLOAN )0.6325 )0.153 )5.5750 )1.394 4.9424 1.304 INSTALL )1.2377 )0.289 0.2748 0.066 )1.5125 )0.483 DEPOSITS 39.4850 2.561 19.0350 1.410 20.4500 1.890 PURCHASE 8.3657 1.389 6.9484 1.184 1.4173 0.610 CAPITAL 13.9680 0.903 )1.3707 )0.100 15.3390 1.287 LABOR )48.3120 )1.714 )47.3610 )1.768 )0.9518 )0.071 PHYCAP )25.3910 )1.130 )10.1040 )0.466 )15.2870 )0.799 VROA )5882.600 )1.765 )5767.700 )1.915 )114.9000 )0.054 ROA )182.1600 )2.113 )202.950 )2.462 20.7960 0.427 LAMBDA 23.3470 2.086 23.0160 2.146 0.3320 0.066 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; INC-GROW 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 se-curities 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 chi-square statistic185.94. *Signi®cant at 5% level.
accounting for the prior growth/no-growth choice reduces the bias that would have occurred without the previous growth choice. The positive sign shows that the model would have been biased towards selecting Bank Acquire or Product Expand in the two equations rather than branching. The signi®cant coecient for this time period indicates the importance of accounting for the prior growth/no-growth decision.
The negative coecient on MKTCONC indicates that banks in highly concentrated markets are more likely to grow through branching than either bank acquisition or product expansion, ceteris paribus. Most importantly, the ®nding of a negative coecient for ROA in the Bank Acquire and Product Expand equations relative to branching indicates that performance is a de-terminant of the type of growth choice. The negative sign indicates that rela-tively higher performing banks are more likely to branch, counter to expectations. More likely the ®nding indicates that higher performers are in less concentrated markets and have greater market power, thus these banks choose to expand locally through branching rather than geographically through bank acquisition or to expand products.
5. Conclusions and implications
This study reviews the determinants of the choice of banks to grow or not, and the determinants of the choice to branch, Bank Acquire, or Product Ex-pand in the 1983±1988 and 1989±1994 time periods. The methodology uses a two-step method where the ®rst step is a Heckman correction probit model of the growth/no-growth decision and the second step uses the conditional in-formation of growth and models the probability of growing a particular way. Bank growth strategies are assigned for banks that apply for any growth ac-tivity, termed the `Any Activity Method', and those that have proportions of growth applications greater than 50% for one activity, termed the `Primary Activity Method'.
In the 1983±1988 period, de novo banks are less likely to grow, and banks in highly concentrated markets are less likely to grow, consistent with previous research. Banks with relatively poor performing loan portfolios are more likely to grow, banks with relatively lower capital are more likely to grow indicat-ing that capital does not preclude a bank from growindicat-ing. In the 1989±1994 time period, banks in statewide branching states are less likely to grow, and multibank holding companies are more likely to grow, ceteris paribus, as expected.
The multinomial model is used to indicate which variables in¯uence theparticular type of growth chosen by banks, while incorporating the growth/ no-growth decision from the ®rst stage Heckman correction model. For the 1983±1988 model using the Any Activity Method to assign strategies, 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. For bank acquirers relative to product expanding banks, bank acquirers are more likely to be in a non-statewide branching state and have highly competitive envi-ronments as indicated by the weighted Her®ndahl Index. Multibank holding companies are more likely to Product Expand than to acquire banks which could be an artifact 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, and bank acquirers with higher proportions of commercial and installment loans are more likely to Product Expand. Banks with higher reliance on purchased funds are more likely to acquire other banks than Product Expand indicating a commitment to traditional banking activities.
For the same 1983±1988 period, but growth strategies are assigned using the Primary Activity Method 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. As deposits to assets increase, banks are more likely to grow through product expansion than branching. Bank Acquire as compared to banks that expand products are less likely to acquire banks than expand products if in a statewide branching state or if formed as a multibank holding company.
Product Expand, thereby suggesting that these banks choose to expand locally through branching rather than geographically through bank acquisition or to expand products.
These results suggest that bank structure in¯uences bank growth choice since multi-bank holding companies are more likely to grow, and for those banks that grow, are more likely to choose product expansion. Regulatory environments are a determinant in that banks in statewide branching states are less likely to grow, but for those that do, they are more likely to choose product expansion as the method of growth. Banks in high income growth states are less likely to grow, indicating that growing customer bases somewhat mitigates the need to expand beyond existing oces. Larger banks are more likely to grow, but not more likely to choose one method of growth over the others. Banks with high labor prices are less likely to grow in both time periods, as are those with higher capital in the earlier time period. Banks with higher performance are more likely to branch in the 1989±1994 time period using the Primary Activity Method to assign strat-egies. This could be the case, for example, if banks experiencing reduced pro®ts are reluctant to engage in costly mergers and acquisitions of banks or nonbanking ®rms.
The importance of bank growth choice and the method of growth for those that choose to grow is likely to increase in importance as we enter a new period of de- and re-regulation. As banks are more easily able to acquire other banks due to the Riegle±Neal Act of 1994 and expand products due to the continued erosion of the Glass±Steagall Act, the topic becomes more important. As competition from both banks and nonbank ®nancial service ®rms increases, the growth activities of banks will become more critical to the survival of banking ®rms and the industry.
Acknowledgements
We wish to thank Ray Degennaro, Harold Black, Cary Collins, John Mayo, and an anonymous referee for helpful comments. Cyree would like to thank Bryant College for a summer grant while conducting this research.
References
Akhavein, J.D., Berger, A.N., Humphrey, D.B., 1997. The eects of megamergers on eci-ency and prices: Evidence from a bank pro®t function. Review Industrial Organization 12, 95± 139.
Baradwaj, B.G., Dubofsky, D.A., Fraser, D.R., 1992. Bidder returns in interstate and intrastate bank acquisitions. Journal of Financial Services Research 5, 261±274.
Beatty, R.A., Santomero, A.M., Smirlock, M.L., 1987. Bank merger premiums: Analysis and evidence. Monograph Series in Finance and Economics, 13±33.
Benston, G.J., Hanweck, G.A., Humphrey, D.B., 1982. Scale economies in banking: A restructuring and reassessment. Journal of Money, Credit and Banking 14, 256±435. Benston, G.J., Hunter, W.C., Wall, L.D., 1995. Motivations for bank mergers and acquisitions:
Enhancing the deposit put option versus earnings diversi®cation. Journal of Money, Credit, and Banking 27, 777±788.
Berger, A.N., 1995. The pro®t±structure relationship in banking-tests of market power and ecient-structure hypotheses. Journal of Money, Credit, and Banking 27, 404±431.
Berger, A.N., Hancock, D., Humphrey, D.B., 1993. Bank eciency derived from the pro®t function. Journal of Banking and Finance 17, 317±347.
Berger, A.N., Humphrey, D.B., 1992. Megamergers in banking and the use of cost eciency as an antitrust defense. Antitrust Bulletin 37, 541±600.
Berger, A.N., Humphrey, D.B., Pulley, L.B., 1996. Do consumers pay for one-stop banking: Evidence from and alternative revenue function. Journal of Banking and Finance 20, 1601± 1621.
Berger, A.N., Hunter, W.C., Timme, S.G., 1993. The eciency of ®nancial institutions: A review and preview of research past, Present and Future. Journal of Banking and Finance 17, 221±249.
Berger, A.N., Saunders, A.B., Scalise, J.M., Udell, G.F., 1998. The eects of bank mergers and acquisitions on small business lending. Journal of Financial Economics 50, 187±229. Bhargava, R., Fraser, D.R., 1998. On the wealth and risk eects of commercial bank expansion
into securities underwriting: An analysis of section 20-subsidiaries. Journal of Banking and Finance 22, 447±465.
Boyd, J.H., Graham, S.L., Hewitt, R.S., 1993. Bank holding company mergers with nonbank ®nancial ®rms: Eects on the risk of failure. Journal of Banking and Finance 17, 43±64. Cheng, D.C., Gup, B.E, Wall, L.D., 1989. Financial determinants of bank takeovers. Journal of
Money, Credit, and Banking 21, 524±536.
Cornett, M.M., De, S., 1991. Common stock returns in corporate takeover bids: Evidence from interstate bank mergers. Journal of Banking and Finance 15, 273±296.
Cyree, K.B., Wansley, J.W., Black, H.A., 1998. Bank Growth and Changes in Operating Performance, Working paper, University of Tennessee, Knoxville, TN.
Heckman, J.J., 1976. The common structure of statistical models of truncation, sample selection, and limited independent variables and a simple estimator for such models. Annals of Economic and Social Measurement 5, 475±492.
Heckman, J.J., 1979. Sample selection bias as a speci®cation error. Econometrica 47, 153±161. Liang, N., Savage, D.T., 1990. New data on the performance of nonbank subsidiaries of bank
holding companies. Federal Reserve Sta Studies, 1±12.
Madura, J., Wiant, K.J., 1994. Long-term valuation eects of bank acquisitions. Journal of Banking and Finance 18, 1135±1154.
McFadden, D.L., 1973. Conditional logit analysis of qualitative choice behavior. Frontiers in Econometrics, Academic Press, New York.
Nelson, R.W., 1985. Branching, scale economies, and banking costs. Journal of Banking and Finance 9, 177±192.
Peristiani, S., 1997. Do mergers improveX-eciency and scale eciency of US banks? Evidence from the 1980s. Journal of Money, Credit and Banking 29, 326±337.
Rose, P.S., 1987. The impact of mergers in banking: Evidence from a nationwide sample of federally chartered banks. Journal of Economics and Business 39, 289±312.
