Determinants of Cost Efficiencies in Bank Mergers

	Robert  DeYoung

	Office of the Comptroller of the Currency

	Economic & Policy Analysis

	Working Paper 93-01

	August 1993

Abstract:  A thick cost frontier methodology is used to estimate pre- and postmerger cost inefficiency in 348 mergers approved by the OCC in 1987 and 1988.  The results are not consistent

with the traditional "market for corporate control" story in which well-managed acquirers improve

the performance of poorly managed targets.  Cost efficiency improved in the majority of mergers,

but gains were small and were not related to the acquiring bank's efficiency advantage over its target. 

Efficiency improved most often when both merger partners were relatively cost inefficient,

suggesting that cost savings depend more on the opportunities facing management than the quality

of that management.  Banks that made acquisitions frequently were relatively successful at capturing

postmerger efficiencies, suggesting a role for experience effects. 

The opinions expressed are those of the authors and do not necessarily represent those of the

Office of the Comptroller of the Currency.  Any whole or partial reproduction of material in this paper should include the following citation:  DeYoung, Robert, “Determinants of Cost Efficiencies in Bank Mergers," Office of the Comptroller of the Currency, E&PA Working Paper 93-1, August 1993.

________________________________________________________________________________

The author thanks Dennis Glennon, Joseph Haubrich, and David Hayes for their expert advice, Zipora Alon for her excellent research assistance, the OCC working paper reviewers for their thoughtful comments, Holly Winebrenner for

her assistance with data, and Claire Emory for her editorial assistance.  Any remaining errors are the author's

responsibility.

Please address questions of substance to Robert DeYoung, Financial Economist, Bank Research Division, Office of the

Comptroller of the Currency, 250 E Street SW, Washington, D.C. 20219, (202) 874-5250.   

Questions regarding subscriptions to the OCC Economic & Policy Analysis Working Paper Series should be addressed

to Communications Division, Comptroller of the Currency, Washington, D.C.  20219.

I.  Introduction

	Banks acquire other banks for a variety of reasons -- for example, market power,

diversification, external growth, and managers' preferences -- but the most frequently cited reason

for recent bank mergers is cost savings.  There is considerable confusion, however, about whether

and how bank mergers generate cost savings.  In theory, pruning fixed expenses (e.g., by eliminating

overlapping branch offices or duplicate back office systems) can create scale economies by

allocating the overhead of a single bank across the activities of two previously independent banks. 

But the empirical literature on banking costs finds that scale economies are limited to the smallest

banks, and as such are not available in many bank mergers. 

	Recently, banking cost studies have identified a potential for merger-induced cost savings

that is independent of scale.  These studies find substantial cost inefficiencies -- sometimes referred

to as X-inefficiencies -- in banks of all sizes.1  The primary cause of these inefficiencies appears to be technical inefficiency, i.e., employing excess inputs.  Bank mergers may be able to exploit this

potential for cost savings by transferring efficient management practices to poorly managed target

banks.  This paper tests whether the pattern of pre- and postmerger cost inefficiency observed in

banks is consistent with an efficient "market for corporate control" and attempts to identify the

merger characteristics that are most conducive to converting premerger cost inefficiency into

postmerger cost savings.  

	This study examines the costs of 348 single-target bank mergers approved by the Office of

the Comptroller of the Currency in 1987 and 1988.  Estimates of pre and postmerger cost

inefficiencies are generated by comparing the costs incurred by merging banks to a "thick" cost

frontier, estimated using a multiproduct, translog cost specification.  Previous studies using frontier-

efficient techniques have focussed on mergers of banks with more than $1 billion in assets; this study

expands the literature by applying these techniques to bank mergers of all sizes.

	In general, the results are not consistent with the traditional market for corporate control story

in which a well-managed acquirer improves the performance of a poorly managed target.  Although

58 percent of the mergers studied did make efficiency gains, these gains tended to be statistically

insignificant, and were not related to the size of the efficiency advantage held by an acquiring bank

over its merger target.  Cost efficiency improved most when both merger partners were relatively inefficient, an indication that postmerger efficiencies may depend more on the opportunities facing

management than the quality of that management.  Mergers in which the acquiring bank made

repeated acquisitions over time generated efficiency gains 50 percent more often than acquisitions

by banks entering the market less frequently, suggesting that experience effects may exist.  There

was also weak evidence of efficiency gains in mergers between banks of disparate sizes, and mergers

between affiliates of the same bank holding company (BHC).  

	Section II discusses the sources of efficiencies from bank mergers and summarizes the results

of other bank merger efficiency studies.  Section III presents the statistical model and data sets used

to generate estimates of cost inefficiency.  Section IV outlines the hypotheses to be tested and

methods by which they are tested.  Section V reports the results of these tests.  Section VI contains

concluding remarks.

II.   Bank Mergers and Cost Savings

	Many banking experts advocate bank mergers on the grounds that they will produce

substantial cost savings.  Projected savings equal to 15 percent of noninterest expense at target banks

in intermarket mergers, and 40 percent in intramarket mergers, are not uncommon.2  But pronouncements like these can be misleading.  Berger and Humphrey (1992) point out that

expressing savings as a percentage of the operating costs at the target, rather than as a percentage of

the total costs of the combined banks, can vastly overstate savings.  And aside from such

misrepresentations, realized savings can simply fall short of projections.

	Although a merger can help a bank grow larger or become more diversified, the majority of

banking cost studies suggest that neither transformation will significantly reduce costs at most

banks.3  Most studies of scale economies find only modest savings that are fully exploited by the time a bank has $100 to $200 million in assets, followed eventually by slight scale diseconomies as

the bank grows larger.4  Studies of scope economies find little evidence that offering a wide range of products gives a bank a cost advantage over its less diversified rivals.

	From the scale and scope evidence alone, then, most bank mergers would seem to have no cost savings potential.  However, a third stream of research suggests that bank mergers might deliver

cost savings by replacing inefficient managers with efficient ones.  These studies find that banks of

similar size and product mix often incur widely divergent costs that vary by amounts far larger than

the savings available from scale and scope economies.5  If management quality is the source of these cost disparities, a merger in which a cost-efficient bank purchases a cost inefficient bank might generate significant cost savings.

	Studies typically measure cost inefficiency by computing the distance between a bank's

actual costs and a "frontier-efficient" or "best practices" cost function representing the lower bound

of costs attainable only by the most efficiently managed banks.  The resulting estimate of cost

inefficiency is controlled for cost differences due to scale and product mix and is often decomposed

into components that measure technical and allocative inefficiency.  A variety of techniques have

been used, and although each has its particular strengths and weaknesses, each tends to find cost

inefficiencies that dominate scale and scope economies in most banks.6  

	The "thick frontier" method uses a priori information to select an efficient quartile of banks, then uses standard econometric techniques to estimate a "thick" lower bound (as opposed to a

discrete edge) of costs.  Using the thick frontier method, Berger and Humphrey (1991) found that

the average bank had unit costs around 25 percent greater than the efficient lower bound.  McAllister and McManus (1993) found that banks in the lower quartile operated at over 80 percent of their

efficiency potential, compared to only about 30 percent for banks in an upper cost quartile.

	"Stochastic cost frontier" methods separate the error term in an econometric cost function

into two components:  a symmetric, random disturbance, and a one-sided, nonrandom component

that is assumed to be related to inefficiencies.  Using stochastic cost frontier methods, Ferrier and

Lovell (1990) found that costs at the average bank were about 26 percent greater than the frontier. 

	"Data envelopment analysis" uses a nonparametric (linear programming) method to generate

a lower envelope of the production function.  Ferrier and Lovell (1990) found that the average bank

uses 21 percent more inputs than necessary.  Aly, Grabowski, Pasurka, and Rangan (1990) found

that the banks in their sample could have employed 35 percent fewer inputs without reducing output. 

Elyasiani and Mehdian (1990), who measured technical but not allocative efficiencies, found that

banks overemployed inputs by about 12 percent.

	Most studies of bank mergers and cost inefficiency have studied "megamergers" in which

both banks have over $1 billion in assets.  Shaffer (1993) used thick frontier techniques to simulate

megamergers between actual banks under various assumptions about the postmerger transfer of

efficiency.  In scenarios that paired cost-efficient banks with cost-inefficient banks, significant

savings resulted when it was assumed that 100 percent of the cost efficiency difference was

eliminated after the merger.

	Although Shaffer's results suggest that some bank mergers have the potential to reduce cost inefficiency, studies of actual mergers find that such potential does not guarantee actual cost savings. 

In a study of 57 megamergers, Berger and Humphrey (1992) used regression residuals to rank the

cost efficiency of merging banks against all nonmerging megabanks.  Most of these mergers showed the potential for efficiency gains (i.e., acquiring banks were more X-efficient than their targets);

however, this potential was not systematically exploited after the merger.  Fixler and Zieschang

(1993) used index numbers to compare the productivity (output as a ratio of inputs) of banks that

merged to the productivity of hypothetical "reference" banks having similar characteristics and found

results similar to those of Berger and Humphrey.  

	The number of studies testing bank mergers for overall (scale plus scope plus cost efficiency)

efficiency gains has increased greatly in recent years.  Srinivasan (1992), Rhoades (1993), Linder

and Crane (1992), O'Keefe (1992), Spindt and Tarhan (1991), and others compare pre- and

postmerger financial ratios.  With a few exceptions, these studies conclude that bank mergers do not

systematically reduce postmerger costs.7  Benston, Hunter, and Wall (1992) found that the acquisition price premium in bank mergers was positively related to acquiring bank efficiency

(proxied by the ratio of market to book value) and negatively related to target bank efficiency.  This

suggests that the market for corporate control of banks expected efficient managers to be able to

improve the performance of targets previously run by inefficient managers.  Results from stock

market event studies of bank mergers have been mixed; depending on the sample of mergers tested,

the studies have found either positive, negative, or insignificant abnormal returns.8  

III.  Estimating Cost Inefficiency 

	This study uses a modified thick cost frontier method to estimate efficiency benchmarks.  The benchmarks are expressed in terms of unit costs (total costs divided by assets).  Unit costs from

a sample of 348 mergers approved by the OCC in either 1987 or 1988 are compared to these

benchmarks.  Premerger cost inefficiency is estimated for acquiring and target banks by comparing

unit costs to the cost frontier in the year before the merger (either 1986 or 1987).  Postmerger cost

inefficiency is estimated for merged banks by comparing unit costs to the cost frontier three years

after the merger (either 1990 or 1991).

	An identical series of tasks was performed to estimate the thick cost frontier for each of these

years (1986, 1987, 1990, and 1991).  A multiproduct translog cost function was estimated for the

population of all commercial banks, and the residuals were saved.  The banks were placed in order

by asset size, and this ordering was partitioned into asset deciles.  The banks in each of these asset

deciles were then placed in order by size of residual.  The banks in the lowest quartile of each asset

decile (i.e., the 25 percent of the banks with the most negative residual values) were assumed to be

the most cost efficient or "best practices" banks.9  The translog cost model was then estimated again, this time using data from the best practices banks only, producing an efficient cost frontier for each

of the four years.  

III.A  Multiproduct Translog Cost Model  

	The following translog cost function and associated factor share equations were estimated using seemingly unrelated regression techniques:10

where:

C		= interest expense plus noninterest expense.

Yi 		= output vector; i,j = 1,2,3,4,5 (commercial and industrial loans, real estate loans, consumer loans, securities, and transactions deposits)

Wm 		= input price vector; m,n = 1,2,3,4 (wage rate, price of physical capital, price of deposited funds, price of purchased funds)

LIMIT	= dummy equal to 1 in limited branching states.

UNIT	 	= dummy equal to 1 in unit banking states.

BHC		= dummy equal to 1 if bank is a member of a bank holding company.

Sm 		= the share of C generated by expenditures on input m.

ε,η		= error terms assumed to capture random cost fluctuations.

	All variables were constructed from year-end Reports of Condition and Income (call report)

data except for LIMIT and UNIT, which were constructed using information from Amel (1991).  The

wage rate equals salaries and benefits divided by the number of full-time equivalent workers (FTEs); the price of physical capital equals depreciation expense divided by the original price of physical

assets; the price of deposited funds equals interest expense on transactions, savings, and time

deposits (excluding CDs > $100,000) divided by these balances; and the price of purchased funds

equals interest expense on large CDs, Fed funds, foreign deposits, and other borrowed funds divided

by these balances.  Transactions deposits equal demand deposits, NOW accounts, and other

nonsavings and nontime deposits.11  Securities are included as an output because of their unprecedented increase on bank balance sheets in the early 1990s.  BHC controls for cost differences

due to organizational form.  LIMIT and UNIT are included to control for the impact of branching

restrictions on costs.  The number of branches operated by each bank is purposefully excluded so

that the costs of overbranching can appear as cost inefficiency.12  

	More than 10,000 observations of commercial banks in each of the four years were used. 

Because the natural log of zero is undefined, output variables with values of zero were set equal to

one.  A small number of banks were dropped in each year due to reported costs of zero.  

III.B  Merging Banks  

	The sample of 348 mergers from 1987 and 1988 was selected from the approved merger

applications in the OCC Quarterly Journal.  All mergers appearing in this source were included unless:  (a) data for all the variables in the model were not available, (b) the merger target was not

a commercial bank, or (c) multiple target banks were acquired in the same merger application.    

	The absolute and relative asset sizes of the 696 merging banks are shown in Tables 1a and

1b.  Acquiring banks were distributed somewhat uniformly by asset size, but their targets were

skewed toward smaller institutions.  The acquiring bank was larger than its target in 88 percent of

the mergers.  Both banks had less than $100 million in assets in about 35 percent of the mergers, and

less than 3 percent of the transactions were megamergers.  Relative to conventional estimates of

efficient scale in banking (see above), the median acquirer ($143 million) had exhausted

opportunities for scale economies, but the median target ($38 million) was operating at suboptimal

scale.

	About 31 percent of the mergers were purchase and assumption transactions (P&As)

involving insolvent target banks.  About 43 percent were mergers that consolidated two affiliates in

the same holding company organization.  About 39 percent were mergers in which the acquiring

bank purchased additional banks within a window starting two years before, and ending three years

after, the merger in question.  (Some mergers belong to more than one of these categories.)  There

are reasons to believe that comparisons of pre- and postmerger costs in these three categories could

be biased.  First, the FDIC often removes the assets that are most expensive to manage (e.g.,

repossessed real estate) from target banks during P&A transactions, which might bias results toward finding postmerger efficiency gains.  Second, lead banks in bank holding companies often provide

services (e.g., data processing) for affiliate banks.  Costs will be overstated at the lead bank and

understated at affiliates if only the variable costs of producing those services are passed along. 

Third, costs may be elevated at banks that make repeated acquisitions due to merger transition costs. 

Because of these potential biases, most other studies exclude these types of mergers.  In order to test

interesting hypotheses, however, they are included here.  Care is taken to report the results from

these mergers separately whenever possible.

III.C  Estimates of Cost Inefficiency  

	Cost inefficiency per dollar of assets was estimated by:

where Cit is actual total cost for bank i in year t, xit is the vector of exogenous variables from equation (1), C^(  ) is the cost that the bank would have incurred had it employed best practices

management techniques, and Ait is the total assets held by the bank.  Normalizing the difference between actual and predicted costs by assets yields two benefits.  First, measuring cost inefficiency

as a percentage of assets allows comparisons to standard performance measures such as return on

assets (ROA).  Second, normalizing by assets controls for asset growth or asset run-off during the

four intervening years.

	An ordinal measure of cost inefficiency was also constructed in order to control for changes

in interest rates, technology, etc. that could bias intertemporal comparisons of XIN.  The entire population of banks was divided into nine asset classes of at least 200 banks each.13  Within each class, banks were ranked by XIN from 1 (most efficient) to N (most inefficient).  The following

index was then constructed:

XIND ranges from zero for the least efficient bank, to one for the most efficient bank, in each asset

class α.14  

	Note that neither XIN or XIND is adjusted for the effects of scale economies or diseconomies

that may result from the merger.  Merger-induced savings that drive XIND closer to 1.0 are

independent from merger-induced increases in size that move banks closer to efficient scale -- in the

former, costs approach the efficient cost frontier, whereas in the latter, a lower portion of the

efficient cost frontier becomes available to the bank.15 

IV.  Tests of Merger-Induced Efficiencies

	An efficient market for corporate control reallocates assets from poorly managed, inefficient firms to well-managed, more efficient firms.  If an acquiring bank is managed more efficiently than

its target, we would expect the following expression to hold:

where the subscripts A and T refer to the acquiring and the target banks that merge at time t. 

Mergers for which this inequality holds might be said to have "potential" for inducing gains in cost

efficiency.  If the merger causes some of this potential to be realized, we would expect the following

expression to hold:

where wi is the percentage of total assets brought to the merger by bank i, and s years is a reasonably long enough time for target assets to be digested.  Mergers for which this inequality holds might be

said to have "captured" X-efficiencies.  

	If the inequality in (6) holds, the conventional explanation is that efficient practices were

transferred from the acquiring bank to the target.  There are at least three other (non-mutually

exclusive) possibilities.  First, efficient practices may have flowed in the other direction, from target

to acquirer.  Second, the process of combining two banks may have revealed previously

unrecognized inefficiencies at the acquiring bank, which were subsequently corrected.  Third, the

upheaval and reorganization associated with the merger may have provided acquiring bank managers

"cover" to make changes that were too personally distasteful otherwise.

	The potential for gains in cost efficiency may vary across different types of mergers.  For

example, mergers between banks in the same holding company may have less potential for cost efficiency gains.  If the holding company structure allows affiliates to share best management

practices prior to the merger, merging banks will have smaller premerger differences in efficiency,

and hence less potential for postmerger efficiency gains.16  On the other hand, acquisitions of insolvent banks may have larger than average potential for gains in cost efficiency.  Undercapitalized

banks often have low earnings and/or nonperforming assets, both of which might be related to cost

inefficiency:  excess costs depress earnings, and problem assets often require additional labor inputs

(for example, repossessed real estate must be managed by the bank).    

	The degree to which potential efficiency gains are converted into actual, postmerger cost

efficiencies may also vary across different types of mergers.  For instance, banks that make repeated

acquisitions may be more likely to capture cost efficiencies.  This could be true if "learning by

doing" occurs, if fixed expenditures incurred during earlier mergers can be spread over future

acquisitions, or if banks with core competencies in capturing efficiencies use acquisitions as part of

their competitive strategy.  Conversely, mergers between firms of similar size might generate fewer

efficiencies.17  In such mergers, the acquiring bank may not have the capacity to absorb the other's data processing requirements or back office operations, and target management may be able to use

its relatively equal political footing with the acquiring firm to retard change. 

V.   Results

	On average, acquiring banks were more cost-efficient than their targets.  The average (mean)

acquiring bank ranked in the 46th efficiency percentile (XIND), compared to the 41st efficiency

percentile for the average target.18  Cost inefficiency was 1.24 cents per dollar of assets (XIN) at the average acquiring bank and 1.62 cents at the average target.  Unit costs at the best practices banks

averaged 5.66 cents per dollar of assets in 1986-87, making costs at acquirers and targets,

respectively, about 22 percent and 29 percent higher than the efficient cost frontier.  These results

are consistent with the 20 to 30 percent range found in other studies.  (Parameter estimates from the

four cost frontiers appear in the appendix.)

	These results imply that target bank ROA would increase by about 0.25 cents on the dollar

if cost efficiency at the target improved to the level of the average acquiring bank, and that acquring

bank ROA would increase by about 0.82 cents on the dollar if acquiring banks could operate as

efficiently as their best practices peers.19  Considering that ROA at commercial banks has been approximately one cent on the dollar over the past ten years, either of these increases would be an

impressive gain; however, note that the potential for internally generated cost efficiencies is several

times greater than the apparent potential for efficiency transfers from acquirer to target. 

	Figure 1 shows the relationship between XIN, a bank's absolute amount of cost inefficiency,

and XIND, a zero-to-one ranking of a bank's cost inefficiency relative to its peers.  The following equation was fitted to the data in Figure 1 using ordinary least squares:

Standard errors appear below the coefficient estimates, all of which are statistically significant.  R2 equals .8278.  Equation 7 can be used to show that small changes in efficiency rank can have

profound effects on pre-tax ROA (by definition, a change in XIN is identical to a change in pre-tax ROA in the opposite direction).20  For example, an increase in efficiency rank from the midpoint of the first efficiency quartile (XIND = .125) to the midpoint of the second efficiency quartile (XIND

= .375) would yield an increase of .0145 in pre-tax ROA.  Similar movements from the second to

the third, and from the third to the fourth, efficiency quartiles would yield changes in pre-tax ROA

of .0027 and .0086, respectively.  

	Table 2 reports premerger XIND for various subsets of mergers.  Also included is the

difference in efficiency ranks between the average acquiring and target banks, as well as the implied

difference in pre-tax ROA (estimated using equation (7)).  P&A mergers exhibited the largest

potential for merger-induced efficiency gains.  Acquiring banks were more efficient than their targets

in about 69 percent of these mergers (significantly different from 50 percent at the 1 percent level). 

The average P&A target was only in the 22nd efficiency percentile -- not surprising given that failing

banks suffer from a variety of financial problems that may be related to efficiency.  Unexpected,

however, was the low 39th percentile efficiency rank of the average P&A acquirer.  Inspection of

the data reveals the likely reason:  three-quarters of these mergers were in Texas, Oklahoma, and Louisiana, where even relatively cost-efficient banks held repossessed real estate that was costly to

manage.  Still, the 17-point difference in XIND translates into a potential improvement in the target

bank's pre-tax ROA of over a half cent ($.0065) per dollar of assets.  

	Aside from P&A mergers, little systematic potential for efficiency transfers from acquirer

to target was revealed.  Acquiring banks were more cost-efficient than their targets in only about 51

percent of the non-P&A mergers.  Although these banks exhibited plentiful potential for internally

generated cost efficiencies, there was virtually no difference in cost efficiency between the partners

(XINA = 1.12 and XINT = 1.19).  On average, both acquirers and targets were in the 49th efficiency percentile.  

	Acquisitions of solvent targets are categorized in Table 2 by two defining characteristics. 

First, did the acquiring bank make other purchases during a window beginning two years before, and

ending three years after, the merger in question?  And second, did the merger consolidate two

affiliates of the same bank holding company, or were the merger partners unrelated?  "Active"

participants in the merger market were more cost efficient than their targets only about 48 percent

of the time, compared to about 53 percent for the banks that made only one acquisition during the

five-year window.  Neither of these results is statistically different from 50 percent.  Furthermore,

because cost estimates for active mergers may reflect the costs of other mergers in various stages of

completion, these results should be interpreted with caution.

	Acquirers in mergers between holding company affiliates were more efficient than their

targets only 42 percent of time.  "Targets" ranked higher than their "acquirers" (the 53rd vs. the 45th

percentile) in these mergers.  Although both of these results are statistically significant, they may

simply reflect the cost accounting associated with lead banks providing costly services for their affiliate-targets prior to the merger.   

	Acquirers in mergers of unrelated banks were more cost efficient than their targets about 64

percent of the time and outranked their acquisitions by 10 percentiles (54th vs. 44th).  Both of these

results are statistically significant.  Of all the subgroups in Table 2,  this category describes most

closely the traditional "market for corporate control" story, in which a relatively efficient bank

acquires a solvent, unrelated, and relatively inefficient bank.  The 10-point difference in XIND

translates into a potential pre-tax ROA improvement of almost one tenth of a cent per dollar of

assets.

	Because it either failed or became a merger target itself, the acquiring bank in 75 of the 1987-

88 mergers no longer existed in 1990-91.  Table 3 compares premerger (XINDt-1) and postmerger (XINDt+3) efficiency ranks for the surviving 273 mergers.  The results should be interpreted with caution due to two potential methodological biases.  First, self-selection in the data may overstate

efficiency gains.  Roughly a third of the 75 nonsurviving mergers failed, and because insolvent banks

are relatively inefficient (see Table 2), the 273 merged banks that survived could be biased toward

banks that are good at capturing efficiencies.  (Regardless of this potential bias, the results in Table

3 still reveal which types of mergers are relatively good at capturing efficiencies.)  Second, the magnitude (but not the sign) of the changes in XIND and XIN tends to understate overall efficiency

gains and is sensitive to the relative sizes of the merging banks.  For example, in a merger that

improves cost efficiency at the target bank by 10 percentiles, XIND will increase by 5.0 percentiles,

3.3 percentiles, or 2.5 percentiles, respectively, if the target is the same size, half the size, or a third

the size of its acquirer.  

	As shown in Table 3, the majority of bank mergers generated cost efficiencies, but the corresponding improvements in cost efficiency were small.  XIND increased in about 58 percent of

surviving mergers (significantly different from 50 percent at the 1 percent level).  However, the

efficiency rank increased by only a statistically nonsignificant 4 percentiles for the average merger,

for an approximate $.0004 reduction in costs per dollar of assets.  Despite the large premerger

potential for efficiency gains in P&A mergers, these mergers did not generate significant gains.  In

contrast, about 61 percent of the acquisitions of solvent targets generated efficiency gains

(significant at the 1 percent level).  The remainder of the analysis focuses on these 180 mergers.   

	Cost efficiency increased in about 64 percent of the non-P&A mergers between holding

company affiliates, though the average increase was not statistically significant.  This result is not

consistent with the expectation that intra-holding company mergers would deliver efficiency gains

less often than mergers of unrelated banks.  Perhaps BHC affiliates, which share many of the same

office systems and management styles, encounter fewer postmerger surprises.  In comparison, only

about 54 percent of the non-P&A mergers between unrelated banks generated efficiency gains. 

Although these mergers exhibited a large premerger potential for efficiencies (see Table 2), they

posted only a nonsignificant 2 percentile increase in efficiency rank.

	Cost efficiency improved in about three-quarters of the non-P&A purchases made by "active"

acquirers.  XIND increased by a statistically significant 12 percentiles in these mergers, even though: 

(1) these banks showed little premerger efficiency potential, and (2) acquirers in these mergers were

simultaneously incurring transition costs from other mergers.  In contrast, only about half of the

"one-time" mergers posted efficiency gains, and this measure fell to only 43 percent in one-time

mergers of unrelated banks (where the cost data is arguably of better quality).  These results are

consistent with the expectation that banks making repeated acquisitions may benefit from learning effects, spreading fixed merger-related costs over multiple acquisitions, or some other intrinsic

comparative advantage.  

	Mergers between banks of relatively equal size generated cost efficiencies less often.  The

180 acquisitions of solvent targets were split into three groups according to the relative size of the

merger partners.21  Only half of the mergers of similar-sized banks generated efficiencies, compared to about 73 percent for the mergers of more disparate-sized banks (significant at the 1 percent level). 

This result  is consistent with the hypothesis that political intransigence and capacity limitations

might hinder the performance of mergers of relative equals.  

	The 86 mergers in which the target was more efficient than its acquirer generated larger gains

in XIND (about 9 percentiles vs. about 2 percentiles), and generated them more often (about 66

percent vs. about 55 percent of the time), than did the 94 mergers in which the acquirer was the more

efficient of the two banks.  This result, along with earlier results, runs counter to the expectation that

postmerger efficiencies flow from buyers to targets.  With this traditional "market for corporate

control" story seemingly rejected, we are left to explain how else mergers might result in cost

efficiencies.  The data suggest that the amount of cost inefficiency present in both merging banks, combined with the process of merging two organizations, may be more important than the difference

in the quality of target and acquiring bank managers.  

	Melding two banks -- choosing between back office systems, eliminating duplicative branch

offices, etc. -- requires management to evaluate not only the procedures and performances at the

target bank, but also at the acquiring bank.  The more inefficiency that exists at both banks, the more potential for cost savings the merger holds.   The merger might also allow acquiring bank managers

to take actions at their own offices that are personally distasteful under normal circumstances, such

as firing workers.  Absent the investigations and opportunities made possible by the process of merger, inefficiencies might continue to exist unnoticed.  When both banks were relatively

inefficient banks (both with XIND < .50), over 80 percent of the mergers resulted in cost

efficiencies.  On average, XIND increased by over 15 percentiles (significant at the 1 percent level)

in these mergers, and the estimated improvement in pre-tax ROA was $.0054 per dollar of assets. 

In comparison, only about 47 percent of the mergers between two relatively efficient banks (both

with XIND > .50) generated cost efficiencies.  

	These results do not appear to be the result of a statistical reversion to the median (XIND =

.50) over time, i.e., if costs were temporarily increased or decreased by a one-time event at time t-1,

they would tend to revert back to their "normal" level after t-1.22   If reversion to the median is present, there should be a symmetry to the results, i.e., efficiency would apparently increase in

mergers between two relatively cost inefficient banks, and apparently decrease in mergers between

two relatively cost efficient banks, by roughly similar magnitudes.  Note that the results in Table 3

are not symmetric -- the reduction in XIND is statistically insignificant for mergers between two

relatively efficient banks.  

	If the merger process is a catalyst for identifying and correcting cost inefficiency, merging

banks should exhibit an upward drift in the efficiency rankings relative to nonmerging banks.  Table 4 provides evidence to this effect.  In the general population of commercial banks, about 28 percent

of the banks ranked below the median in 1986-87 moved above the median in 1990-91, and about

29 percent of the banks ranked above the median in 1986-87 were below the median three years

later.  The results for the 180 banks that acquired solvent targets were less symmetric. Acquiring

banks below the median prior to merging moved above the median after the merger more often

(about 39 percent), and acquiring banks above the median prior to merging moved below the median

after the merger less often (24 percent), than banks in the general population.  Mergers between two

efficient banks appear to be better able to maintain or increase their cost advantage than their

nonmerging peers, while mergers between two inefficient banks appear to be better able to cut costs

relative to their nonmerging peers.   

VI.  Conclusions

	The objective of this study was to test whether the pattern of pre- and postmerger cost

inefficiency in banks is consistent with the traditional market for corporate control explanation of

mergers, and whether some types of mergers are more successful than others at capturing  cost

savings. A sample of 348 single-target bank mergers approved by the Office of the Comptroller of

the Currency in 1987 and 1988 was examined.  Pre- and postmerger cost inefficiency in these

mergers was estimated using a thick frontier methodology and a multiproduct translog cost function. 

	Although both target banks and their acquirers exhibited large amounts of cost inefficiency,

the potential for internally generated cost efficiencies appeared to be much greater than the potential

for efficiency transfers from acquirer to target.   Consistent with earlier studies, the merging banks exhibited costs that were 22 percent to 29 percent higher than their "best practices" peers.  Aside

from purchase and assumption transactions, however, cost inefficiencies were virtually the same in

target and acquiring banks.  Only about half of the non-P&A mergers reallocated assets from less

efficient to more efficient banks.  

	Cost efficiency improved in about 61 percent of non-P&A mergers that survived for three

years, but the magnitude of the average gain was not statistically significant.  Contrary to

expectation, a merger's ability to generate cost efficiencies does not appear to be related to the

efficiency advantage that the acquirer holds over its target.  Rather, efficiency potential appears to

be related to the amount of inefficiency present in either or both partners prior to the merger.  Over

80 percent of the mergers in which both banks were relatively cost inefficient generated efficiency

gains.  This suggests that the process of combining two organizations--and revealing pre-existing efficiencies that were unknown prior to the merger--may be as important as the quality of the

managers overseeing the merger.  The possibility that this result is due to a statistical reversion to

the median was investigated and dismissed.

	In general, the results found here imply that motivations other than cost savings contribute

to most banks' decisions to merge.  Likely motivations include increasing market power, diversifying

loan portfolios, or achieving growth when local possibilities have been exhausted.  Certain types of

mergers, however, appear to have a higher probability than others of generating cost efficiencies. 

Efficiency gains were present in about three-quarters of the acquisitions by banks that repeatedly

entered the merger market, compared to only about 50 percent for the acquisitions by banks that

made only a single purchase during the sample period.   These results suggest that some banks may

have an experiential or comparative advantage at mergers, though this conclusion is somewhat tentative given the uncertain nature of the cost data for repeat acquirers.  There was weak evidence

that mergers between affiliates of the same holding company were more likely to make efficiency

gains, and that mergers between banks of relatively equal size were less likely to make efficiency

gains.

	References

Aly, H., R. Grabowski, C. Pasurka, and N. Rangan.  "Technical, Scale, and Allocative 	

	Efficiencies in U.S. Banking:  An Empirical Investigation,"  The Review of Economics 	 and Statistics, 72: 211-219 (1990).

Amel, D.  "State Laws Affecting Commercial Bank Branching, Multibank Holding Company 

	Expansion, and Interstate Banking," unpublished paper (1991).

Baradwaj, B., D. Dubofsky, and D. Fraser.  "Bidder Returns in Interstate and Intrastate Bank 

	Acquisitions," Journal of Financial Services Research, 5: 261-273 (1991).

Benston, G., W. Hunter, and L. Wall.  "Motivations for Bank Mergers and Acquisitions:  

	Enhancing the Deposit Insurance Put Option versus Increasing Operating Net Cash 

	Flow," Federal Reserve Bank of Atlanta, Working Paper 92-4  (1992).

Berger, A., and D. Humphrey.  "Megamergers in Banking and the Use of Cost Efficiency as 

	an Antitrust Defense,"  The Antitrust Bulletin, 37: 541-600 (1992).

Berger, A., and D. Humphrey.  "The Dominance of Inefficiencies over Scale and Product Mix 

	Economies in Banking," Journal of Monetary Economics, August: 117-148 (1991).

Clark, J.  "Economies of Scale and Scope at Depository Financial Institutions: A Review of 

	the Literature," Federal Reserve Bank of Kansas City, Economic Review, September:  	16-33 (1988).

Elyasiani, E., and S. Mehdian.  "A Nonparametric Approach to Measurement of Efficiency 

	and Technological Change: The Case of Large U.S. Commercial Banks," Journal of  	Financial Services Research, 4: 157-168 (1990).

Evanoff, D., and P. Israilevich, "Productive Efficiency in Banking," Federal Reserve Bank of 

	Chicago, Economic Perspectives, July: 11-32 (1991).

Ferrier, G., and C. Lovell.  "Measuring Cost Efficiency in Banking:  Econometric and Linear 

	Programming Evidence," Journal of Econometrics, 46: 229-245 (1990).

Fixler, D., and K. Zieschang.  "An Index Number Approach to Measuring Bank Efficiency: 

	An Application to Mergers," Journal of Banking and Finance, 17: 437-450 (1993).

Humphrey, D. "Why Do Estimates of Bank Scale Economies Differ?" Federal Reserve Bank 

	of Richmond, Economic Review, September: 38-50 (1990).

Hunter, W., and S. Timme. "Technical Change, Organizational Form, and the Structure of 

	Bank Production," Journal of Money, Credit, and Banking, May: 152-167 (1986).

Johnston, J.  Econometric Methods, 3rd ed., New York: McGraw Hill (1984).

Lawrence, C., and R. Shay.  "Technology and Financial Intermediation in Multiproduct     

	Banking Firms," in  Technology, Innovation, Regulation, and the Monetary Economy, 53- 92, Cambridge:  Ballinger (1986).

Linder, J., and D. Crane, "Bank Mergers: Integration and Profitability," working paper, 	 Harvard Business School (1992).

Leibenstein, H. "Allocative Efficiency vs. X-Efficiency," American Economic Review, 56:  		392-415 (1966).

McAllister, P., and D. McManus. "Resolving the Scale Economy Puzzle in Banking," Journal  	of Banking and Finance, 17: 389-406 (1993).

Mester, L. "Efficient Production of Financial Services," Federal Reserve Bank of Philadelphia, 

	Business Review, January: 15-25 (1987).

O'Keefe, J. "Bank Failure Resolutions:  Implications for Banking Industry Structure, Conduct, 

	and Performance," FDIC Banking Review, 5: 17-35 (1992).

Rhoades, S. "Efficiency effects of (in-market) horizontal bank mergers," Journal of Banking  	and Finance, 17: 411-422 (1993).

Shaffer, S. "Can Megamergers Improve Bank Efficiency?" Journal of Banking and Finance,  	17: 423-436 (1993). 

Spindt, P., and V. Tarhan.  "An Empirical Examination of the Market for Corporate Control 

	in the Banking Sector," in Bank Structure and Competition, Federal Reserve Bank of  	Chicago, 568-601 (1991).

 Srinivasan, A. "Are There Cost Savings from Bank Mergers?," Federal Reserve Bank of 

	Atlanta, Economic Review, March: 17-28 (1992).  

Stalk, G, P. Evans, and L. Shulman. "Competing on Capabilities:  The New Rules of 	

	Corporate Strategy," Harvard Business Review, March-April: 57-69 (1992).

	Appendix

	Best practices parameter estimates for equation (1).

	1986		1987		1990		1991

α0	 0.3992	 0.0009	 1.0588**	 0.8565**

	(0.2178)	(0.2443)	(0.2426)	(0.2284)

α1	-0.0830**	 0.1631**	 0.0616*	-0.0149

	(0.0266)	(0.0243)	(0.0256)	(0.0303)

α2	 0.2260**	 0.2555**	 0.4404**	 0.0731**

	(0.0271)	(0.0258)	(0.0263)	(0.0236)

α3	 0.2698**	 0.2118**	 0.0817**	 0.6326**

	(0.0253)	(0.0293)	(0.0262)	(0.0415)

α4	 0.1311**	 0.1534**	 0.0568*	-0.0064

	(0.0151)	(0.0206)	(0.0275)	(0.0271)

α5	 0.3348**	 0.1817**	 0.2597**	 0.0889

	(0.0402)	(0.0447)	(0.0420)	(0.0481)

β11	 0.0333**	 0.0338**	 0.0286**	 0.0259**

	(0.0009)	(0.0008)	(0.0007)	(0.0007)

β22	 0.0309**	 0.0407**	 0.0337**	 0.0323**

	(0.0010)	(0.0013)	(0.0009)	(0.0009)

β33	 0.0482**	 0.0543**	 0.0676**	 0.0603**

	(0.0012)	(0.0013)	(0.0011)	(0.0009)

β44	 0.0378**	 0.0376**	 0.0471**	 0.0591**

	(0.0007)	(0.0007)	(0.0007)	(0.0009)

β55	 0.1520**	 0.1095**	 0.0637**	 0.0771**

	(0.0057)	(0.0039)	(0.0021)	(0.0019)

β12	-0.0083**	-0.0018	-0.0004	 0.0012

	(0.0014)	(0.0011)	(0.0007)	(0.0008)

	Best practices parameter estimates for equation (1), cont.

	1986		1987		1990		1991

β13	 0.0064**	-0.0112**	-0.0093**	-0.0033**

	(0.0012)	(0.0009)	(0.0007)	(0.0007)

β14	 0.0027*	 0.0014	-0.0049**	-0.0073**

	(0.0012)	(0.0012)	(0.0011)	(0.0011)

β15	-0.0356**	-0.0200**	-0.0139**	-0.0145**

	(0.0028)	(0.0014)	(0.0018)	(0.0013)

β23	-0.0010	-0.0091**	-0.0131**	-0.0151**

	(0.0011)	(0.0009)	(0.0006)	(0.0007)

β24	-0.0126**	-0.0166**	-0.0181**	-0.0033**

	(0.0011)	(0.0010)	(0.0010)	(0.0007)

β25	-0.0126**	-0.0190**	-0.0096**	-0.0060**

	(0.0024)	(0.0025)	(0.0013)	(0.0013)

β34	 0.0054**	-0.0000	-0.0165**	-0.0109**

	(0.0009)	(0.0013)	(0.0008)	(0.0007)

β35	-0.0523**	-0.0187**	-0.0120**	-0.0093**

	(0.0026)	(0.0021)	(0.0014)	(0.0014)

β45	-0.0219**	-0.0230**	-0.0042**	-0.0453**

	(0.0019)	(0.0019)	(0.0014)	(0.0018)

γ1	 0.5286**	 0.4845**	 0.2260**	 0.4969**

	(0.0509)	(0.0599)	(0.0585)	(0.0504)

γ2	 0.9862**	 0.6851**	 0.7508**	 1.0806**

	(0.0330)	(0.0353)	(0.0347)	(0.0350)

γ3	-0.4265**	-0.1325**	 0.0151	-0.2469**

	(0.0369)	(0.0437)	(0.0500)	(0.0389)

γ4	-0.0884**	-0.0370	 0.0081	-0.3305**

	(0.0339)	(0.0384)	(0.0373)	(0.0365)

	Best practices parameter estimates for equation (1), cont.

	1986		1987		1990		1991

δ11	 0.0261**	 0.0441**	 0.0688**	-0.0326**

	(0.0074)	(0.0093)	(0.0091)	(0.0079)

δ22	 0.2508**	 0.1654**	 0.1736**	 0.1651**

	(0.0050)	(0.0040)	(0.0038)	(0.0041)

δ33	-0.0074	 0.0239**	 0.0245**	-0.0205**

	(0.0040)	(0.0045)	(0.0056)	(0.0048)

δ44	 0.0054	 0.0026	 0.0052*	 0.0084**

	(0.0032)	(0.0025)	(0.0022)	(0.0023)

δ12	-0.1144**	-0.0892**	-0.0840**	-0.0484**

	(0.0031)	(0.0032)	(0.0034)	(0.0035)

δ13	 0.0749**	 0.0361**	 0.0175*	 0.0325**

	(0.0054)	(0.0068)	(0.0076)	(0.0059)

δ14	 0.0133*	 0.0089	-0.0024	 0.0485**

	(0.0054)	(0.0056)	(0.0056)	(0.0058)

δ23	-0.0709**	-0.0356**	-0.0448**	-0.0234**

	(0.0036)	(0.0038)	(0.0037)	(0.0037)

δ24	-0.0220**	 0.0129**	-0.0056	-0.0683**

	(0.0042)	(0.0047)	(0.0051)	(0.0052)

δ34	 0.0034	-0.0245**	 0.0028	 0.0114*

	(0.0040)	(0.0042)	(0.0037)	(0.0047)

θ11	 0.0438**	-0.0012	 0.0160**	 0.0232**

	(0.0044)	(0.0033)	(0.0038)	(0.0045)

θ12	-0.0382**	-0.0090**	 0.0007	-0.0359**

	(0.0027)	(0.0027)	(0.0035)	(0.0030)

θ13	-0.0088**	 0.0147**	-0.0143**	 0.0201**

	(0.0032)	(0.0023)	(0.0023)	(0.0035)

	Best practices parameter estimates for equation (1), cont.

	1986		1987		1990		1991

θ14	 0.0033	-0.0045**	-0.0025	-0.0075**

	(0.0021)	(0.0015)	(0.0016)	(0.0019)

θ21	-0.0095*	-0.0149**	-0.0362**	-0.0057

	(0.0045)	(0.0036)	(0.0041)	(0.0036)

θ22	-0.0084**	 0.0059**	 0.0283**	 0.0063**

	(0.0020)	(0.0016)	(0.0026)	(0.0018)

θ23	 0.0259**	-0.0055	 0.0033	-0.0099**

	(0.0037)	(0.0030)	(0.0027)	(0.0036)

θ24	-0.0079**	 0.0145**	 0.0046*	 0.0094**

	(0.0020)	(0.0024)	(0.0018)	(0.0016)

θ31	-0.0245**	-0.0296**	-0.0040	-0.0969**

	(0.0039)	(0.0041)	(0.0042)	(0.0063)

θ32	-0.0037	-0.0053	 0.0141**	 0.0635**

	(0.0030)	(0.0027)	(0.0033)	(0.0037)

θ33	 0.0119**	 0.0244**	-0.0075**	 0.0341**

	(0.0026)	(0.0032)	(0.0025)	(0.0040)

θ34	 0.0163**	 0.0105**	-0.0026	-0.0007

	(0.0021)	(0.0024)	(0.0021)	(0.0024)

θ41	-0.0141**	 0.0021	 0.0152**	 0.0402**

	(0.0021)	(0.0029)	(0.0040)	(0.0040)

θ42	 0.0059**	 0.0165**	 0.0307**	-0.0679**

	(0.0020)	(0.0018)	(0.0028)	(0.0031)

θ43	 0.0035	-0.0135**	-0.0453**	 0.0232**

	(0.0020)	(0.0020)	(0.0038)	(0.0035)

θ44	 0.0048**	-0.0052**	-0.0006	 0.0045**

	(0.0014)	(0.0016)	(0.0015)	(0.0015)

	Best practices parameter estimates for equation (1), cont.

	1986		1987		1990		1991

θ51	-0.0525**	-0.0217**	-0.0400**	 0.0264**

	(0.0069)	(0.0066)	(0.0070)	(0.0065)

θ52	 0.0959**	 0.0534**	-0.0159**	 0.0335**

	(0.0048)	(0.0032)	(0.0043)	(0.0054)

θ53	-0.0281**	-0.0178**	 0.0534**	-0.0551**

	(0.0060)	(0.0051)	(0.0046)	(0.0062)

θ54	-0.0152**	-0.0139**	 0.0025	-0.0048

	(0.0036)	(0.0039)	(0.0038)	(0.0038)

λB	 0.0116**	 0.0088*	 0.0140**	 0.0052

	(0.0034)	(0.0034)	(0.0033)	(0.0034)

λU	-0.0100	-0.0456**	-0.0549**	-0.0435**

	(0.0053)	(0.0062)	(0.0077)	(0.0087)

λL	-0.0172**	-0.0159**	-0.0248**	-0.0115**

	(0.0046)	(0.0042)	(0.0030)	(0.0030)

R2	.9842		.9845		.9898		.9880

N	 3499		 3374		 3309		 2939

	Table 1a

	Distribution of acquiring and target banks by asset size.

	Dollars are in millions.

_________________________________________________________________

	     Asset size       Acquirers    Targets   

$    0 to   $100 million     37.5%       76.5%

$  100 to $1,000 million     34.4%       20.8%

$1,000 million and above     28.1%        2.7%

           median assets   $  143       $  38

             mean assets   $2,027       $ 140

	Table 1b

	Distribution of mergers by asset size of acquiring and target banks.

	Dollars are in millions.

_________________________________________________________________

                                 Target           

                     $0-$100  $100-$1,000  $1,000+

    Acquirer        

                     ┌─────────┬────────┬────────┐

       $0-$100       │  35.3%  │   1.7% │   0%   │

                     ├─────────┼────────┼────────┤

   $100-$1,000       │  26.2%  │   8.1% │   0%   │

                     ├─────────┼────────┼────────┤ 

       $1,000+       │  14.7%  │   0.9% │  2.6%  │

                     └─────────┴────────┴────────┘

	Figure 1

	Mapping premerger XIND into premerger XIN.  Data from 696 merging banks.

	Figure 1 is not available via download.

	If you need tTo see Figure 1, please use the land mail option andto request Working Paper 93-1. via  

	Table 2

	Premerger efficiency comparisons.  XIN measured in basis points.

_________________________________________________________________                                              

                            %XINDA  mean  mean  XINDA-  XINT-

		 		   #    >XINDT  XINDA XINDT  XINDT   XINA 

                       _________________________________________  

	All Mergers

________________________________________________________________

All 				  348   56.9%** .463  .412  .051†   .0007

	Target Insolvent

________________________________________________________________

All P&As			  107   69.2%** .393  .226  .167††  .0065

	Target Solvent

________________________________________________________________

All				  241   51.4%  .494  .495 -.001  -.0000

  Active			   87   48.3%  .515  .540 -.025  -.0002 

  Not active		  154   53.3%  .482  .469 +.013   .0000

  Holding company	  138   42.0%** .454  .532 -.078† -.0007 

  Unrelated		  103   64.1%** .547  .445 +.102†  .0009

** and * denote a difference from 50.0% at the 1 percent and 5 percent significance levels, respectively.  †† and † denote a difference from zero at

the 1 percent and 5 percent significance levels, respectively.  Both tests are

two-tailed and use a uniform approximation to the standard normal

distribution. 

	Table 3

	Postmerger efficiency comparisons.  XIN measured in basis points.

_________________________________________________________________	

		     	 %XINDt+3  mean   mean   XINDt+3-  XINt-1-   

		 	 #    >XINDt-1  XINDt-1 XINDt+3  XINDt-1   XINt+3

	All Mergers

				______________________________________________________________

All			273	  57.9%**	.458	 .497   +.039	 .0004

	Target Insolvent

______________________________________________________________

All			 93	  52.7%	.358	 .375   +.017	 .0004

	Target Solvent

______________________________________________________________

All			180	  60.6%**	.509	 .561   +.052	 .0004

Holding co.: 	109	  64.2%**	.497	 .569   +.072	 .0005

  Active		 48	  75.0%**	.516	 .655   +.139†	 .0012

  Nonactive	 	 61	  55.7%	.483	 .502   +.019	 .0002

  XINDA>XINDT	 45	  53.3%	.573	 .599   +.026	 .0002

  XINDA<XINDT	 64	  71.9%**	.444	 .548   +.104†	 .0009

Unrelated:	 	 71	  54.9%	.527	 .547   +.020	 .0001

   Active		 27	  74.1%**	.502	 .609   +.107	 .0007

   Nonactive  	 44	  43.2%	.542	 .510   -.032	-.0002 

   XINDA>XINDT	 49	  57.1%	.554	 .566   +.012	 .0000

   XINDA<XINDT	 22	  50.0%	.466	 .505   +.039	 .0003

Active	 	 75	  74.7%**	.519	 .638   +.119†	 .0010

Nonactive  		105	  50.5%	.508	 .505   -.003	-.0000

XINDA > XINDT	 94	  55.3%	.563	 .582   +.019	 .0001

XINDA < XINDT	 86	  66.3%**	.449	 .537   +.088†	 .0007

XINDA,T > .500	 55	  47.3%	.751	 .711   -.040	 .0011

XINDA,T < .500	 53	  81.1%**	.247	 .403   +.156††     .0054

1⁄3 most equal 	 60	  50.0%	.576	 .574   -.002	-.0000

1⁄3 least equal	 60	  73.3%**	.497	 .596   +.099	 .0007

** and * denote a difference from 50.0% at the 1 percent and 5 percent significance levels.  †† and † denote a difference from zero at the 1 percent

and 5 percent significance levels.  Both tests are two-tailed and use the

uniform approximation to the standard normal distribution.

	Table 4

	Pre- and Postmerger Stability of XIND

_________________________________________________________________

	All Surviving Acquirers of Solvent Targets (180)

				      Postmerger

			     XINDA,T<.50  XINDA,T>.50   

                   ┌───────────┬───────────┐ Premerger

                   │           │           │ 

                   │   60.7%   │   39.3%   │ XINDA,T<.50 

                   │           │           │ 

                   ├───────────┼───────────┤ 

                   │           │           │ 

                   │   24.0%   │   76.0%   │ XINDA,T>.50 

                   │           │           │ 

                   └───────────┴───────────┘ 

	All Surviving Banks, 1986-1990 (10,955)

				      Postmerger

			     XINDA,T<.50  XINDA,T>.50 

                   ┌───────────┬───────────┐ Premerger

                   │           │           │ 

                   │   71.3%   │   28.7%   │ XINDA,T<.50 

                   │           │           │ 

                   ├───────────┼───────────┤ 

                   │           │           │ 

                   │   30.0%   │   70.0%   │ XINDA,T>.50 

                   │           │           │ 

                   └───────────┴───────────┘ 

	All Surviving Banks, 1987-1991 (10,679)

				      Postmerger

			     XINDA,T<.50  XINDA,T>.50 

                   ┌───────────┬───────────┐ Premerger

                   │           │           │ 

                   │   72.6%   │   27.4%   │ XINDA,T<.50 

                   │           │           │ 

                   ├───────────┼───────────┤ 

                   │           │           │ 

                   │   28.5%   │   72.5%   │ XINDA,T>.50 

                   │           │           │ 

                   └───────────┴───────────┘