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The efficiency of financial institutions: A review and preview of research past, present and future

April 1993
Journal of Banking & Finance 17(2-3):221-249

Authors:

University of Iowa

This introductory article reviews past research on the topic of financial institution efficiency, surveys the contributions in this special issue, and suggests how future research on this important topic might proceed.

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Journal of Banking and Finance 17 (1993) 221-249. North-Holland

The efficiency of financial institutions:

A review and preview of research past, present, and

future*

Allen N. Berger

Board of Governors of the Federal Reserve System, Washington, DC 20551, USA

William C. Hunter

Federal Reserve Bank of Atlanta, Atlanta, GA 30303, USA

Stephen G. Timme

Georgia State University, Atlanta, GA 30303, USA

This introductory article reviews past research on the topic of financial institution efficiency,

surveys the contributions in this special issue, and suggests how future research on this

important topic might proceed.

1. Introduction

In a world in which the structures of financial service industries are

changing rapidly, it is important to determine the cost and revenue efficiency

of the evolving financial institutions. If these institutions are becoming more

efficient, then we might expect improved profitability, greater amounts of

funds intermediated, better prices and service quality for consumers, and

greater safety and soundness if some of the efliciency savings are applied

Correspondence to: Allen N. Berger, Mail Stop 180, Federal Reserve Board, 20th and C Sts.

N.W., Washington, DC 20551, USA. Telephone (202) 452-2903, fax (202) 452-5295, or email

mlanbOO(&fed.frb,gov.

*The opinions expressed do not necessarily reflect those of the Board of Governors, the

Reserve Banks, or their staffs. The authors thank the Federal Reserve Bank of Atlanta and

Georgia State University School of Business for sponsoring the conference on which this special

issue is based at the Atlanta Reserve Bank, September 2425, 1992. Financial support from the

Georgia State University Department of Finance, Research Committee. and Center for Risk

Management and Insurance Research is gratefully acknowledged. The authors also thank Paul

Bauer, Sallv Davies. Shawna Grosskopf. Diana Hancock. Dave Humnhrev, Pat McAllister. and

Doug McManus for helpful comments; and Mandeep Chahal, Janet doher, Peter Dadalt, Sheila

Griffin, and Lynn Woosley for administrative help.

222 A.N. Berger et al., The efficiency offinancial institutions

towards improving capital buffers that absorb risk. Of course the converse

applies if the evolution results in less ellicient intermediaries, with the

additional danger of taxpayer-financed industry bailouts if substantial losses

are sustained.

It is clear that rapid changes in financial industry structure are occurring

around the globe. In the US, the thrift industry has virtually collapsed, the

insurance industry is under almost unprecedented financial pressure, and the

banking industry is in the midst of a dramatic consolidation wave in which

many of the nation’s largest banking organizations are merging with one

another. In Western Europe, there has been considerable consolidation of

banks within countries in anticipation of EC integration and the accompany-

ing consolidation across borders. In the Eastern Bloc, capitalist-style institu-

tions that allocate tinancial resources on the basis of their prospects for

financial success must almost be built from scratch out of the ruins of the

Communist command economies. In Asia and South America, countries are

restructuring regulations regarding the separation of commercial banking,

underwriting, and insurance, in attempts to increase financial industry

efficiency.

Unfortunately, the study of the efficiency of financial institutions has not

kept pace with these changes. While scale and scope efficiencies have been

extensively studied, primarily in the context of US financial institutions,

relatively little attention has been paid to measuring what appears to be a

much more important source of efficiency differences - X-inefficiencies, or

deviations from the efficient frontier. That is, differences in managerial ability

to control costs or maximize revenues appear to be greater than the cost

effects of the choice of scale and scope of production. Research to date

suggest that X-inefficiencies account for on the order of 20% or more of costs

in banking, while scale and product mix inefficiencies, when they can be

accurately estimated, are usually found to account for less than 5% of costs.

Although a considerable amount of research has taken place on X-

efficiency in general since its introduction in the 1960s [see Leibenstein

(1966)], published technical research on the X-efficiency of financial institu-

tions has only appeared in the last few years. Moreover, prior to this special

issue, nearly all such papers had measured X-efficiency for US commercial

banks, with less than a handful of papers measuring the efficiency of

nonbank financial institutions or banks outside of the US. Thus, in terms of

both maturity and breadth, the efficiency research has not kept pace with the

changes in the financial services industry.

The purpose of this special issue and the conference upon which it was

based is to accelerate the growth rate of knowledge about the efficiency of

financial institutions. The goals of this introductory article are to assess this

progress and to suggest directions in which future research might be most

fruitful. We subdivide the areas of research into six categories: (1) scale and

A.N. Berger et al., The efficiency of financial institutions 223

scope efficiencies in banking, (2) X-efficiency in banking, (3) the efficiency

implications of bank mergers, (4) the efficiency of thrifts and governmental

financial institutions, (5) the efficiency of the insurance industry, and (6) the

determinants of financial institution efficiency. For each category, we briefly

discuss the prior state of knowledge, summarize the contributions of the

articles in this special issue, and suggest directions for future research.

2. Scale and scope efficiencies in banking

The prior literature on scale efficiency in banking suggests that the average

cost curve has a relatively flat U-shape, with medium-sized firms being

slightly more scale efficient than either very large or very small firms [see the

survey by Humphrey (1990)]. The primary uncertainty expressed in this

literature is the location of the bottom of the average cost U - the scale-

efficient point. Studies that used only banks with under $1 billion in assets,

studies that used banks of all sizes, and one study that included all banks of

over $100 million usually found average costs to be minimized between

about $75 million and $300 million in assets [Berger et al. (1987), Ferrier

and Love11 (1990) Berger and Humphrey (1991), Bauer et al. (1992)]. Studies

that used only banks with over $1 billion in assets usually found the

minimum average cost point to be between $2 billion and $10 billion in

assets [Hunter and Timme (1986, 1991) Noulas et al. (1990), Hunter et al.

(1990)]. These results suggest that the functional form employed in these

studies may not be capable of incorporating the technologies of both large

and small banks together in a single model, or that some important factor

that varies with bank size may be excluded from the model.

The current paper by McAllister and McManus (1993) suggests that both

of these shortcomings are present in the existing scale efficiency literature.

First, they show that the commonly used translog cost function specification

gives a poor approximation when applied to banks of all sizes. The translog

does not hold up as a reasonable gloal approximation because it forces large

and small banks to lie on a symmetric U-shaped ray average cost curve and

disallows other possibilities, such as an average cost curve that falls up to

some output point and remains constant thereafter. Thus, it may be the case

that the diseconomies found for larger banks are simply the imposed

reflection of the economies found for small banks. In addition, the translog

approximation may behave poorly away from the mean product mix, which

can create problems in measuring scale efficiencies because large banks tend

to have very different product mixes from the average. McAllister and

McManus’ solution to this problem is to replace the translog with one of

several nonparametric estimation procedures.

McAllister and McManus’ other innovation is to add a missing factor to

224 A.N. Berger et al., The efficiency oj’$nancial institutions

the calculus of scale efficiency - risk. They show that as bank loan portfolios

increase in size up to about $1 billion, the standard deviation of the rate of

return falls precipitously, presumably because of diversification benefits. This

reduction in risk lowers the amount of tinancial capital which must be held

by the bank to keep the risk exposure of the bank’s creditors (including the

deposit insurer) at a given level. Because capital is the most expensive

marginal source of funding, this creates a financial scale economy by which

banks can lower their average costs of funds as scale increases by holding a

smaller proportion of capital (to the extent that this is allowed by regula-

tors). This represents an improvement over two previous attempts to

incorporate risk into the cost function for financial institutions, one of which

specified risk but did not include its cost [Hughes and Mester (1992)], and

one of which measured risk by provisions for loan loss reserves, which reflect

expected losses rather than the risk or variance of losses [Rossi (1992)].

McAllister and McManus find substantial scale inefficiencies for small

banks, full scale efficiency reached by about $500 million in assets, and

approximately constant average costs thereafter up to $10 billion in assets,

the upper limit of their sample. Their scale inefficiencies of over 10% for

banks below $100 million in assets suggests that scale inefficiencies may rival

X-inefficiencies for small banks, and that many of these banks may have

difficulty competing as deregulation continues. They also find that their

results differ quite dramatically from the results of a standard translog

model. Most of the difference in their results from the standard approach is

due to the use of nonparametric estimation procedures, although the

inclusion of financial scale economies does have a small, noticeable effect.

Another potential difficulty in the scale economy literature is that most

studies do not use a frontier estimation method. Scale economies theoretic-

ally apply only to the efficient frontier, and the use of data from banks off

the frontier could confound scale efficiencies with differences in X-efficiency.

Fortunately, this potential problem does not appear to be of practical

significance. Two previous studies that have compared scale efficiencies on

and off the efficient frontier have found only small differences [Berger and

Humphrey (1991) Bauer et al. (1992)], and two current papers that make

this comparison find the same result [McAllister and McManus (1993)

Mester (1993)].

The prior literature on scope efficiency for financial institutions is even

more problematic than the scale literature. Three major problems have been

recognized. First, there is a problem in applying the translog specification to

evaluate or test for scope economies. Computation of scope economies

compares the predicted costs of producing a given bundle of outputs by two

or more specializing firms versus joint production by a single firm. In the

two-output case, this amounts to comparing C(y,, 0) + C(0, y2) with C(y,, yZ)

where C(.) is the cost function and y is the output vector. Because the

A.N. Berger et al., The efficiency offinancial institutions 225

translog is multiplicative in outputs, it has the unfortunate property of

having predicted costs of zero for each of the specialized firms, i.e.,

C(y,, 0) = C(0, y2) = 0. Therefore, the translog specification imposes extreme

scale diseconomies on any data set.

One common solution to this problem is to define a minimum level of

each output, si, below which yi is not evaluated. The comparison in the

two-output case is then of C(y,-~,,&,)+c(~~,y~-&~) with C(y,,y,). The

minimum value Ei must be subtracted from yi so that the sum of the outputs

of the two specializing firms equals the output of the joint production firm.’

Unfortunately, this strategy does not really solve the problem of using the

translog, since any finding of scope economies can be summarily eliminated

by setting the ES sufficiently close to zero. This is because setting any output

sufficiently close to zero will yield predicted costs arbitrarily close to zero.

A better solution is to specify an alternate functional form that is not so

poorly behaved at or near zero output. Some studies have applied the Box-

Cox transformation to the outputs, replacing y with (y”- 1)/L Unfortunately,

2 is usually estimated to be close to zero, which again yields properties

similar to the translog [see Pulley and Humphrey (1993)]. Some papers have

avoided this problem by specifying ;1= 1, which amounts to replacing the log

of output with the level [Berger et al. (1987) Buono and Eakin (1990)]. The

composite function, which specifies the fixed-costs component of scope

economies separately from the cost-complementarities component, appears to

be a better choice, however [Pulley and Humphrey (1993)].

The second recognized problem in estimating scope economies is that

there are often little or no data on firms that specialize. In banking, virtually

all firms produce the entire array of products specified in the cost function.

In fact, the dense part of the data set is usually well away from zero outputs,

creating potentially significant problems of extrapolation. The effects of

extrapolation, often combined with the problems of the translog specification,

can be quite dramatic - measured scope economies and diseconomies are

often erratic and far exceed credible levels, at times over 1,000 percent in

absolute value [Berger and Humphrey (1991), Pulley and Humphrey (1993),

Mester (1993)].

A way around this problem is to examine alternative measures that use

product mixes that remain within the dense part of the data set, where more

confident conclusions can be drawn. Expansion path subadditivity (EPSUB)

combines the scale and product mix effects of moving from each size class

mean to the mean of the next largest size class, and provides what appears to

be a more reasonable representation of the opportunity of existing banking

‘In some cases, authors have not properly subtracted the ES, which gives a bias toward finding

scope economies. See Berger et al. (1987, p. 517) for a discussion of this and other estimation

problems in some of the scope studies.

226 A.N. Berger et al., The ejjkiency offinancial institutions

firms to change their outputs than scope economies [Berger et al. (1987)

Hunter et al. (1990), Berger and Humphrey (1991), Hunter and Timme

(1991)]. Hunter et al. (1990) also employ a similar restricted grid-search

procedure. These methods essentially sidestep the question of scope econo-

mies per se in favor of the potentially more interesting question of whether

efficiency can be improved by changing scale and product mix

simultaneously.

The third recognized problem in evaluating scope economies is that of

using data that are not on the efficient frontier. As in the case of scale

economies, scope economies are defined only on the efficient frontier, so that

evaluation using data off of the frontier could confound scope economies

with X-efficiencies. The empirical evidence suggests that, unlike the case of

scale economies, this is a problem for estimating scope economies in banking.

Berger and Humphrey (1991) found scope diseconomies of about l&20% on

the frontier and economies in the 1,000s of percent when the entire data set

was used. Mester (1993) also finds a large difference between scope econo-

mies on and off the frontier, although both sets of estimates are in the 1,000s

of percent.

The current paper by Berger et al. (1993) provides a new, more general

scope economies concept. Their ‘optimal scope economies’ are based on the

profit function instead of the cost function, and provide insights not available

from the conventional scope economies concept. They redefine scope econo-

mies so that they are output-efficient as well as input-efficient. That is, they

include all the revenue effects of output choices as well as the cost effects of

input choices. Implementation of their new concept also provides at least

partial solutions to all three of the recognized scope economy estimation

problems.

Their optimal scope economies concept determines whether a firm facing a

given set of prices and other exogenous factors should optimally produce the

entire array of products or specialize in some of them. The optimizing choice

is the output vector that maximizes profits over all possible output com-

binations for a firm that is fully X-efficient and scale efficient. They test

whether the optimal quantity of every output is greater than zero for all the

observed price vectors. If so, then optimal scope economies hold over the

observed range of data. If not, then it may be profitable for some firms to

specialize. They find that optimal scope economies prevail for most, but not

all firms.

Their concept contrasts with the conventional notion of scope economies,

which simply addresses the question of how costs of producing a given

output bundle may be minimized without determining whether that output

bundle is optimal. Generally, the point of evaluation will incorporate output

inefficiencies, i.e., will be at a suboptimal scale and product mix, and

therefore neither joint nor specialized production at that point is optimal.

A.N. Berger et al., The efficiency of financial institutions 227

Implementation of optimal scope economies addresses the three recognized

problems in scope economy estimation. First, the problem of evaluating a

translog or any other function at zero output is avoided - the profit function

has as its arguments the output prices, which are always positive. Second,

there should be little problem of extrapolation, since the prices faced by banks

are fairly close to each other. And third, the only points of evaluation are

fully X-efficient from both the input and output standpoints, eliminating the

potential problem of evaluating scope economies off of the efficient frontier.

The previous and current research on scale and scope efficiencies has

several important implications for the course of future research. First, it

strongly discourages the use of a translog cost function for research on

banking or any other industry where scale and product mix vary substan-

tially. The translog is insufficiently flexible to describe an industry with

increasing returns to scale up to some point and constant returns thereafter,

and seems to have difticulties when firms tend to change product mix

significantly as they change scale. The translog and the Box-Cox approxima-

tion also typically perform poorly in estimating scope economies because

they have trouble with evaluations at or near zero.

Second, future research should further investigate the concept of financial

scale economies. McAllister and McManus have shown that the inclusion of

the cost of risk has a modest effect on the scale efficiencies of some banks.

What is not yet known is the robustness of this result, and how important it

might be to other types of financial institutions, such as insurance companies,

which may have very different risk-scale relationships.

Third, it is important in future applications to estimate scale and scope

efficiencies only on the X-efficient frontier, where they are properly defined.

Scope efficiencies measured off of the frontier appear to have been con-

founded with X-efficiency differences, although this problem does not appear

to have occurred for scale efficiencies.

Fourth, additional research is needed on the optimal scope economies

concept. This concept examines whether it is optimal from a profitability

standpoint, including both costs and revenues, to produce all the products as

opposed to specializing in one or more of them. Implementation of the new

concept also provides at least partial solutions to the known problems of

scope economy estimation.

Finally, future research should also concentrate on estimating scale as well

as scope efficiencies from the profit function, so that both revenue and cost

effects on these efficiency measures can be taken into account. In the only

prior study to estimate scale efficiencies from the profit function, Hancock

(1992) found significant scale efficiencies using a conventional (nonfrontier)

profit function. Future research should continue along these lines, but

estimate scale efficiencies using the efficient frontier to avoid the possibility of

confounding scale efficiencies with X-efficiencies.

228 A.N. Berger et al., The efficiency of financial institutions

3. The X-efficiency of the banking industry

A number of papers in recent years have measured the X-efficiency of US

commercial banks. We use the term X-efficiency here to describe all technical

and allocative efficiencies of individual firms, as distinguished from scale and

scope efficiencies. The one result upon which there is virtual consensus is that

X-efficiency differences across banks are relatively large and dominate scale

and scope efficiencies. However, there is no consensus on the best method for

estimating X-efficiency, or on the average level of X-efficiency of the banking

industry.

The major econometric problem lies in distinguishing X-efficiency differ-

ences from random error that may temporarily give certain institutions

relatively high or low costs. Four different approaches have been employed

in evaluating bank data. Each of these approaches maintains a different set

of assumptions about the probability distributions of the X-efficiency differ-

ences and random error for the purpose of distinguishing between these two

explanations of cost dispersion. The econometric frontier approach (EFA)

generally assumes that inefficiencies follow an asymmetric half-normal distri-

bution, that random errors follow a symmetric normal distribution, and that

both are orthogonal to the cost function exogenous variables [Ferrier and

Love11 (1990), Timme and Yang (1991), Bauer et al. (1992)]. The thick

frontier approach (TFA) assumes that deviations from predicted costs within

the lowest average-cost quartile of banks in a size class represent random

error, while deviations in predicted costs between the highest and lowest

quartiles represent X-inefficiencies [Berger and Humphrey (199 1, 1992b),

Bauer et al. (1992), Berger (1993)]. The data envelopment analysis (DEA)

approach generally assumes that there are no random fluctuations, so that all

deviations from the estimated frontier represent inefficiency [Rangan et al.

(1988), Aly et al. (1990), Ferrier and Love11 (1990), Elyasiani and Mehdian

(1990), Ferrier et al. (1991), Fixler and Zieschang (1991)]. Finally, the

distribution-free approach (DFA) assumes the efficiency differences are stable

over time, while random error averages out over time [Berger (1991, 1993),

Bauer et al. (1992), Berger and Humphrey (1992a)J

There is no simple rule for determining which of these methods best

describes the true nature of the banking data. This would not be a problem if

all of the methods arrived at essentially the same conclusion. Unfortunately,

this is not the case - in fact, the choice of measurement method appears to

strongly affect the level of measured inefficiency. Authors applying the EFA,

TFA, and DFA methods to banking usually find average inefficiency to be

about 20-250/, of costs, while authors using DEA find results ranging

anywhere from less than lO”/0 to over 50%. Perhaps a more important

problem is that when these methods are compared with one another using

the same data set, the rankings of individual banks often do not correspond

A.N. Berger et al., The efjiciency of financial institutions 229

well across methods, even when the methods find similar average efficiency

levels [Ferrier and Love11 (1990) Batter et al. (1992) Berger (1993)].

Moreover, the efficiency levels found are not invariant to the specification of

which financial products are specified as inputs and outputs, which are fixed

versus variable, or which output metric is employed, even when the

measurement method is held constant [Berg et al. (1991), Hunter and Timme

(1993)]. Thus, the results of using different methodologies and models are not

mutually consistent, making it difficult to determine which institutions are

most and least efficient, and which prospective industry entries, exits, and

consolidations are most likely to improve overall banking performance.

In addition, there are very few studies of the efficiency of banks and other

financial institutions outside of the US, and to our knowledge there are no

prior frontier efficiency comparisons across international borders. For exam-

ple, Murray and White (1983), M. Kim (1985), and Kolari and Zardkoohi

(1990), respectively, studied scale and scope efficiencies in Canadian, Israeli,

and Finnish financial institutions without using frontier methods, and Berg et

al. (1991) applied the DEA frontier technique to Norwegian banks. Given the

prior findings of relatively large X-efIiciencies within one country and the

increasing level of competition across countries, there is a clear need for

measuring and comparing X-efficiencies across borders.

Several papers in the current special issue contribute to this literature.

Berger et al. (1993) apply the distribution-free approach using the profit

function in place of the cost function, which brings output inefficiencies into

the model as well as input inefficiencies. Banks can err by producing at the

wrong level or mix of outputs as well as by employing the wrong level or

mix of inputs, and only the profit function can incorporate all of these

inefficiencies. The profit function has never before been applied to estimating

the X-efticiency of financial institutions and its few previous applications

elsewhere did not separate out all of the input and output technical and

allocative inefficiencies (defined below). The innovation of adding the output

side appears to be quite important. Berger et al. find that output inefficien-

cies are on average larger than input inefficiencies. That is, most of the

inefficiencies are in the form of deficient revenues, rather than excessive costs,

suggesting that use of the standard methods may substantially understate

bank inefficiency. On average, banks appear to lose about one-half of their

potential profits to inefficiency.

Berger et al. also devise and implement a new method of decomposing

total X-inefficiency into allocative and technical components that may be

more useful than the standard definitions. They define allocative inefficiency

as the loss of profits from choosing a poor production plan, and model this

as the effect of basing decisions on shadow prices instead of actual prices.

They define technical inefficiencies as the loss of profits from failing to meet

this production plan. This decomposition of total inefficiency into allocative

230 A.N. Berger et al., The efficiency of financial institutions

and technical components differs substantially from the standard decompo-

sition created by Farrell (1957) Kopp and Diewert (1982), and Zieschang

(1983). The standard method restricts technical inefficiencies to be in the

form of radial, or equiproportionate overuse of all inputs, and force all

deviations from input mix from the optimum into allocative inefficiencies. It

is shown that the standard decomposition is a special case of Berger et al.‘s

decomposition. The new decomposition may also be more useful because it

focuses on the source of inefficiency, i.e., poor plan versus poor implemen-

tation. Berger et al. find that most of the inefficiency is technical in nature -

that banks err primarily in not meeting their production plans, as opposed to

choosing unprofitable plans.

A surprising finding in this study is that larger firms are substantially more

X-efficient on average, or closer to the frontier, than smaller firms. This

finding may offset some of the scale diseconomies found for the largest banks

in cost studies. Given that most of the measured inefficiencies are on the

output side, this suggests that larger firms may have an advantage in terms

of achieving high-value output bundles.

The current paper by English et al. (1993) also estimates output efficiency

using some innovative new techniques. They measure output efficiency using

Shephard’s distance function. They measure the output technical efficiency of

a bank by the radial distance from the origin to the firm’s output point

divided by the distance from the origin to the production-possibilities

frontier along the same output mix ray. That is, actual output is divided by

the potential output for the same set of inputs. This approach is similar to

the standard DEA model in that the distance function is estimated using

deterministic linear programming techniques that do not allow for random

error. English et al. show that the distance function has a number of

especially useful analytical properties. One of these is the ability to construct

shadow price ratios from the production-possibilities frontier to test for alloca-

tive inefficiencies. They test for whether outputs are produced in revenue-

maximizing proportions by comparing shadow prices with actual prices.

English et al.‘s results suggest that on average, banks are only about 75%

technically efficient. Thus, their output inefficiency equals or exceeds the

input inefficiency found in cost studies, once again suggesting that output

inefficiency is important and should be included in efficiency studies. They

also find evidence of allocative inefficiency - measured shadow prices are as

much as two to three times the actual prices. Thus, it appears that banks are

as poor or poorer at maximizing revenues as they are at minimizing costs in

other studies.

The current paper by Berg et al. (1993) addresses the issues of measuring

the X-efficiency of non-US banks and comparing efficiency across countries.

They examine and compare bank efficiencies in three Nordic countries,

Finland, Norway, and Sweden. They first use a DEA approach to measure

A.N. Berger et al., The efficiency of Jinancial institutions 231

efficiency within each of the countries. They then employ an innovative

technique for comparing efficiency across groups. A form of the Malmquist

productivity index is used, which compares the proportionate adjustment of

inputs required for firms in different countries to be on a common efficient

frontier. They also decompose the Malmquist ratio into efficiency and

technology components. International comparisons are also made using a

pooled Nordic data set with a common frontier. Berg et al. generally find

that Swedish banks are more efficient than banks in the other two countries.

Several other papers in the special issue also estimate X-efficiency for

commercial banks. Pi and Timme (1993), using the econometric frontier

approach, find average input efficiency to be about 88% for a sample of large

US bank holding companies, and Grabowski et al. (1993), using DEA, find

average input inefficiency to be about 68% for a sample of US multibank

holding companies and branching banks. These papers will be discussed in

detail below in the section on the determinants of financial institution

efficiency. The following section on bank mergers also describes three papers

that estimate the change in bank efficiency associated with mergers.

The past and present papers on bank X-efficiency suggest several impor-

tant topics for future research, First, the lack of correspondence among the

efficiency levels and rankings for the different measurement approaches

suggests that more research comparing these techniques is needed. One step

in this direction would be to determine more information about the standard

errors of the efficiency estimates to facilitate comparisons across approaches.

It may also be the case that the maintained assumptions of each of the four

main approaches about the probability distributions of the efficiencies and

random error may best lit different data sets. If so, and there is no

unambiguously best method for all data sets, then research leading to a

pretesting procedure for the most appropriate method for a given data set

may be useful.

Second, the evidence provided by Berger et al. and English et al.

suggesting that output inefficiencies are as large or larger than input

inefficiencies strongly implies that more research is needed using the profit

function, output distance function, and other methods that include output

inefficiencies. To our knowledge, no case has ever been made for excluding

output inefficiencies - banks and other firms seem just as likely to make

mistakes in maximizing revenues as in minimizing costs.

Third, more research is needed comparing these output-inclusive

approaches with other approaches. For example, a profit function model and

a cost function model estimated using the same data set could be compared.

If the profit function model were to find much larger inefficiencies, this would

support the case that cost function models seriously understate efficiencies. If

not, it might suggest something about the levels and relationships between

input and output inefficiencies.

232 A.N. Berger et al., The efficiency of financial institutions

Fourth, additional research on Berger et al.3 alternative technical and

allocative efficiency decomposition, using the cost, revenue, or profit func-

tions, is in order. Given that the standard decomposition is a special case of

their definition, it would be of interest to determine how far apart the

standard and new methods are empirically by estimating both on the same

data set using the same efficiency criterion (i.e., cost minimization, or revenue

or profit maximization).

Finally, much more research is needed measuring and comparing the

efficiency of banks and other financial institutions across international

borders, as in Berg et al. The integration of EC markets, as well as the

general globalization of financial markets, means that the most efficient

institutions may eventually dominate world markets. The cross-country

comparisons may also shed some light on the efficiency effects of various

regulatory policies. Substantial differences in efficiency across nations would

tend to suggest that regulatory policies be coordinated and made roughly

equal (e.g., the Basle-risk-based capital accord) to allow for fairer

competition.

4. The efficiency implications of hank mergers

The recent wave of large bank mergers in the US, combined with the

prospects for sweeping international mergers subsequent to EC integration,

has put the spotlight on the efficiency implications of bank mergers,

particularly mergers between large institutions. If these mergers are successful

in improving banking industry efficiency, substantial benefits may accrue to

the customers and claimholders of these banks, and the level of competition

within the banking industry may be considerably increased. Moreover, the

efficiency effects of mergers constitutes an important policy question on its

own, since merger applicants often cite prospective efficiency benefits as a

justification for merger approval.

Most prior studies of bank merger efficiencies compared simple pre-merger

and post-merger financial ratios, such as operating costs divided by total

assets, or the return on equity or assets [Rhoades (1986, 1990), Linder and

Crane (1992), Cornett and Tehranian (1992), Spindt and Tarhan (1992),

Srinivasan (1992), Srinivasan and Wall (1992)]. In most cases, the authors of

these studies corrected for the change in these financial ratios for other firms

in the industry to avoid confounding secular change within the industry with

the efficiency effects of mergers. One prior study also simulated the pre- and

post-merger costs of hypothetical merger partners using a cost function to

determine the potential effects of mergers on costs [Savage (1991)].

Most of these studies found no benefits on average from mergers. The

exceptions are Cornett and Tehranian (1992) and Spindt and Tarhan (1992).

Interestingly, these two exceptions found most of the merger benefits on the

A.N. Berger et al., The efficiency of financial institutions 233

revenue or output side, while most of the studies that did not find merger

benefits examined only the cost or input side. We will return to this issue

below.

There are several problems with these prior studies that examine simple

financial ratios. First and foremost, financial ratios may be misleading

indicators of efficiency because they do not control for product mix or input

prices. Implicitly, studies using a cost-to-asset ratio assume that all assets are

equally costly to produce and all locations have equal costs of doing

business. In addition, the use of a simple ratio cannot distinguish between X-

efficiency gains and scale and scope efliciency gains. This inability greatly

reduces the predictive power of the ratios in determining which types of

mergers are likely to be successful in improving efficiency, since scale and

scope efficiencies automatically change when a merger is consummated, but

X-efficiencies may or may not change.’

Only one prior study of which we are aware used a frontier method to

determine the efficiency effects of bank mergers [Berger and Humphrey

(1992a)]. Using the distribution-free approach, that study found very small,

statistically insignificant average X-efficiency benefits from mergers among

banks with over $1 billion in assets each. These benefits were more than

offset by the scale diseconomies created by merging banks that were

generally larger than efficient scale, resulting in a small total efticiency loss

that was sometimes statistically significant. They also found that there were

no efficiency gains associated with mergers in which the acquirer was more

efficient than the acquired bank or in which both banks were represented in

the same local market, two conditions often thought to be conducive to

merger efficiency gains. The finding of no gain associated with the degree of

market overlap was also found in Srinivasan and Wall’s (1992) ratio analysis.

The current paper by Rhoades (1993) takes off on these themes. He

analyzes 898 horizontal mergers - mergers in which there was some local

market overlap prior to the merger. In addition, the acquiring banks were

usually more efficient prior to the merger than the acquired banks. In these

two respects, these mergers satisfied the conditions often cited by banking

industry analysts and consultants as most likely to result in merger cost

efficiency benefits. In addition, his post-merger data are for the fourth to

sixth years after a merger, since consultants often warn of short-term

transition costs that prevent the full efficiency benefits of a merger from

accruing until three years after the merger.

Despite these supposedly very favorable conditions for merger efficiency

gains, Rhoades finds no such benefits. First, using his merger sample plus a

set of other banks that did not merge, he regresses the change in a simple

‘There are a number of other methodological diffkulties with these studies detailed in Berger

and Humphrey (1992a). Chief among them is that most of these studies ignore interest expenses,

which comprise about 70% of costs.

234 A.N. Berger et al., The efficiency of financial institutions

expense ratio on a dummy variable for whether a merger occurred, as well as

a number of control variables. The dummy coefficient was generally not

statistically significant, suggesting no change in expense ratios associated

with mergers. The degree of market overlap was also generally not significant

in the equations. Rhoades then repeated the analysis, using efficiency

quartiles from the thick frontier approach to determine whether efficiency

gains had been made. Again, mergers and the degree of market overlap

generally had no statistically significant effect on the probability of changing

efficiency quartiles after the merger. These results are consistent with most of

the simple ratio analyses and the earlier frontier efficiency analysis - no cost

efficiency benefits are found, even for horizontal (in-market) mergers in which

the acquiring firm is more efficient than the acquired firm.

The current paper by Shaffer (1993) takes a different tack. Instead of

examining the efficiency effects of past mergers, he tries to examine the range

of potential future efficiency changes and the sources of these changes. The

advantage of this approach is that the competitive environment for banking

organizations has changed in recent years, as have the motivations for bank

mergers. If past mergers were driven more by desires for size and market

expansion, while current mergers are driven more by desires to reduce costs

and increase profitability, then efficiency studies of past mergers could be

misleading. More robust insights could be provided by examining the

potential for efficiency gains and losses than to extrapolate from a period in

which the merger motives and outcomes were likely different.

Shaffer employs the thick frontier approach to simulate the potential

effects of scale and product mix, branch closings, and X-efficiency changes on

cost. After estimating a cost function, he constructed over 20,000 random

pairings among 210 banks with over $1 billion in assets. He finds that scale

and product mix effects, as well as some assumed closings of the branches of

the acquired bank, would likely have less than a 2% average absolute effect

on total costs. However, the X-efficiency gains or losses have the potential to

be quite dramatic. If the banks in the most efficient quartile acquire other

banks, the predicted cost savings are as much as 21% if the managerial

efficiencies can be transferred to acquired component of the bank. By the

same token, there may be efficiency losses of as much as 21% if banks in the

least efficient quartile spread their inefficiency to other banks through

mergers. This very large range of potential outcomes strongly suggests that

scrutiny be given to the managerial talents of the acquiring bank prior to a

merger.

The current paper by Fixler and Zieschang (1993) takes quite a different

approach to analyzing the efficiency implications of bank mergers. Instead of

employing one of the standard efficiency measurement methods, they con-

struct Tornqvist productivity indices for every bank in their sample and use

these as measures of relative efficiency. For each bank, productivity is

A.N. Berger et al., The efficiency of financial institutions 235

measured by a value-weighted output index divided by a value-weighted

input index. This method is equivalent to assuming that each bank has its

own technology and measuring efficiency by comparing the quality of these

technologies. They find that acquiring banks are about 40-50x more efficient

than other banks prior to merging, and that they maintain about this same

advantage over other banks in the years after the merger. Given that

evidence in other papers indicates that acquiring banks are more cost-

efficient than acquired banks, this suggests that acquisition activity may

boost the productivity and efficiency of the industry.

One reason why Fixler and Zieschang’s results may differ somewhat from

the cost studies of bank mergers is that they include revenue effects through

their output index as well as cost effects through their input index. As noted

above, the merger studies that find net benefits mostly find them in revenue

enhancements, rather than cost improvements. Also, the current profit

function paper by Berger et al. (1993) finds the measured output efficiency

differences to be larger on average than measured input efficiency differences.

Thus, Fixler and Zieschang may find merger benefits because of revenue

effects in their output index.

There are several implications of these tindings for future research into the

merger efficiency question. First, the literature should move away from the

use of simple financial ratios and into the use of frontier efficiency

techniques. As noted above, frontier techniques take account of product mix

and input prices and need not make extreme assumptions about all assets in

all locations having the same minimum efficient costs. In addition, frontier

techniques allow for the separate estimation of X-efficiency from scale and

scope efficiencies, which may have very different merger implications.

Second, further research is needed to determine the factors which predict

merger efficiency gains or losses. Although most of the literature found no

efficiency benefits on average, all of the studies found that some of the

mergers were successful, while others were not. For policy purposes, it is

important to know the factors that predict efficiency benefits, given that

merger participants often claim such benefits when applying for regulatory

approval. Unfortunately, the research to date has only found conditions that

do not seem to work in predicting merger success - market overlap and

greater relative efficiency of the acquiring firm.

Third, the profit function should be applied to merger efficiency analysis.

The past and present research that found average efficiency benefits from

mergers all included revenue effects, while the research that did not find such

benefits generally used only cost data. This ‘coincidence’ suggests that

mergers might improve efficiency, but only on the output side. This could

occur if the merger helps the consolidated bank better achieve a higher-

revenue output bundle through improved marketing, product innovation,

repricing, risk management, or other revenue-enhancing effects. The results of

236 A.N. Berger et al., The efficiency of financial institutions

current and prior research consistently point to the possibility of output

efficiency gains from mergers that could be captured and analyzed using a

profit function frontier analysis.

Finally, future analysis will be needed on the question of whether merger

efficiency effects are time-dependent. The supposition raised in Shaffer (1993)

and earlier in Berger and Humphrey (1992a) is that the mergers of the 1990s

may be more likely to bring about efficiency gains than the mergers of the

1980s because merger participants are now more motivated to try and bring

about efficiency gains. Berger and Humphrey (1992a) found no material

difference in average merger efficiency effects in the first and second half of

the 198Os, but further research will be needed as data on the mergers of the

1990s become available.

5. The efficiency of thrifts and governmental financial institutions

The efficiency of thrift institutions, such as savings and loans (S&Ls) and

credit unions (CUs), has not been studied extensively in the literature to date.

Even less is known about governmental financial institutions, such as the

Federal Reserve, that compete with the private sector in providing measur-

able financial services as well as performing traditional governmental func-

tions. Given the substantial inefficiency found in banking, the study of

efficiency questions for these competitors to banks takes on added

importance.

The previous literature on thrift efficiency has concentrated on measure-

ment of scale and scope efficiencies [Murray and White (1983, H.Y. Kim

(1986), Goldstein et al. (1987) Mester (1987, 1989, 1991), Dowling and

Philippatos (1990) and LeCompte and Smith (1990)]. Similar to banking, the

consensus of this literature is that the average cost curve is U-shaped with

scale economies for institutions below $100 million in assets and constant

costs or diseconomies for larger institutions. Tests for scope economies are

mixed, which is understandable in light of the methodological weaknesses of

conventional scope economies tests discussed in section 2 above.

Interest in thrift efficiency has received much of its impetus from the

record failure rate of US S&Ls in the 1980s and the taxpayer bailout of the

1990s. The most often cited factors contributing to this downfall have been

interest rate risk, deregulation, and the economic decline of specific geo-

graphic markets. More recently, the possibility of X-inefficiency in the use of

inputs and outputs has been offered as a contributing factor. Given that X-

inefficiency has been empirically linked to commercial bank failures [Berger

and Humphrey (1992b)], this is a distinct possibility for thrifts.

Recently, some efforts have been made to study thrift X-efficiency.

Cebenoyan et al. (1993) employed an econometric frontier approach and

A.N. Berger et al., The eficiency of financial institutions 237

Hermalin and Wallace (1992) applied a DEA approach. Controlling for other

factors, Cebenoyan et al. found no significant difference in efficiency between

mutual and stock S&Ls, while Hermalin and Wallace found efficiency to be

directly correlated with an S&L’s product mix.

The current study by Mester (1993) uses the econometric frontier approach

to provide further evidence on efficiency in mutual and stock S&Ls. Mester

makes an important extension of the standard EFA model to permit both

the cost frontier and error structures to differ between the two organization

forms. A likelihood ratio test indicates that the data supports this unres-

tricted model. This finding implies that efficient mutuals and stocks use

different production technologies. Mester also finds mutuals to be more

efficient on average than stocks, contrary to conventional wisdom, her own

previous results, and the recent results of Cebenoyan et al. (1992). This

finding is discussed further in the section below on the determinants of

efficiency.

Mester (1993) also investigates the determinants of thrift efficiency. She

finds higher capital-to-asset ratios to be correlated with greater efficiency in

both mutual and stock firms, although incorporating capital into the cost

model did not influence the results significantly. Other significant correlates

with inefftciency measures differ for mutual and stocks. The explanatory

power of the determinants is generally weak.

The current paper by Fried et al. (1993) conducts an efficiency analysis of

credit unions (CUs) using a national sample of institutions operating in 1990.

They use DEA techniques to construct the free disposal hull of the observed

data which represents the production possibilities set of the sample CUs.

Using this technique allows the authors to assign CUs to one of two sets:

undominated or efficient CUs versus dominated or inefficient CUs. The

performance of these institutions is then evaluated using dominance relation-

ships and productive efficiency measures.

The authors find a substantial amount of dominance in the data and the

existence of a large number of efficient or ‘role model’ institutions for the

inefficient dominated firms. On average, the inefficient CUs are about 20%

less efficient than the best practice CUs. The inefficiency estimates are found

to be non-neutral, varying by the quality and quantity of services offered.

They also find that CUs are more likely to be classified as efficient if they

have a large number of members, have a high ratio of investments to loans

and low ratio of real estate loans to total loans, and have no branches,

among other characteristics. As is the case for Mester (1993), the explanatory

power of these determinants is relatively low.

A major methodological contribution of Fried et al. is the incorporation of

price and service variety components into the measured output of the firm.

This is important for mutual and cooperative types of organizations in which

the customers are the owners. The customer/owner may prefer an increase in

238 A.N. Berger et al., The efficiency of financial institutions

costs which would lower conventionally measured efficiency if the higher

costs were in the form of higher interest paid or additional services provided.

Fried et al.‘s method allows the fulfilling of such a preference to be measured

as an efficiency improvement.

While numerous studies examine the efficiency of firms in regulated

industries, few studies examine the efficiency of the regulators themselves.

The public nature of these institutions in no way makes them immune to the

questions of efficient production. This is especially true when governmental

institutions have measurable outputs and are in direct competition with

private sector firms in the provision of their services.

The Federal Reserve System (FRS) represents a prime example of this,

since it provides measurable, marketed payments services (check clearing,

wire transfers, etc.) in direct competition with commercial banks and service

bureaus. Previous research into the efficiency of the FRS in providing

payments services has been confined to evaluating scale efficiency. Early

research by Humphrey suggested that about 78% of the checks processed by

the FRS were cleared through offices with significant diseconomies of scale

prior to the full pricing of payments services. The inefficient scale was

attributed to a lack of significant competition from private sector firms. In

studies using more recent data, Humphrey found constant returns to scale in

Federal Reserve check processing, suggesting that increased competition from

private sector providers and internal pressures to reduce costs from the

Monetary Control Act of 1980 (MCA) resulted in improved scale efficiency.

The current paper by Bauer and Hancock (1993) extends the work of

Humphrey in three fundamental ways. First, the authors examine the X-

efficiency and scale efficiency with which FRS offices employ inputs into the

production of check processing services. Second, the authors employ a

number of econometric and linear programming techniques in estimating

efficiency, including variants of all four of the main approaches outlined

above. Doing so allows the authors to test the sensitivity of the efftciency

measures and ranking to variations in the techniques used to estimate

efficiency. Finally, by examining a longitudinal data base (1979-1990) the

authors are able to investigate the impact of technological change on FRS

payments system efficiency.

Bauer and Hancock find average inefficiency of FRS offices of about 255

30%. However, unlike the banking studies, they find that the results of most

of the efficiency methods are highly correlated with one another and give

very similar rankings across FRS offtces. An exception occurs when they use

the free disposal hull DEA technique. They find that DEA is not appropriate

for their sample because the large variation in output levels across obser-

vations made it difticult to find comparable offtces.

Their scale efficiency results are consistent with Humphrey’s earlier finding

of improvement after passage of the MCA and the implementation of pricing

A.N. Berger et al., The efficiency of financial institutions 239

for payments services. Somewhat surprisingly, however, their measured

X-efficiencies remained virtually unchanged over time. They also find rela-

tively little evidence of technological progress.

The past and current research on the efficiencies of thrifts and the FRS

suggest that much research remains to be done on these topics. First, the

recommendations for future banking research given in the previous three

sections of this paper apply with equal or greater vigor to thrifts. Despite this

industry’s importance, its efficiency remains understudied.

Second, the finding of Mester that the cost frontier and error structures

differ between mutuals and thrifts implies that future studies that compare

the efficiency of two or more groups should consider checking whether the

groups differ in all the available dimensions. Misleading comparisons could

be made if, for example, cost or profit function coefficients were incorrectly

forced to be equal across groups.

Third, the model of Fried et al. suggests that care should be taken in

matching the output or service characteristics with the organizational form.

If the form is cooperative or mutual, then price and service variety should be

specified as beneficial outputs, while the standard outputs are likely sufficient

for the standard corporate form.

Fourth, further research is needed into the efficiency of governmental or

quasi-governmental financial institutions, such as Fannie Mae, Freddie Mac,

and the Small Business Administration, that provide measurable outputs in

at least somewhat competitive markets. For example, the efficiency of Fannie

Mae and Freddie Mac could be compared to each other and to private-

sector issuers of mortgage-backed securities. In addition, research on the

efficiency of various electronic payments mechanisms, such as automated

clearinghouse (ACH), Fedwire, and CHIPS, is important, as these methods

continue to substitute for paper checks.

6. The efficiency of the insurance industry

The insurance industry currently faces many challenges, including

increased competition, consolidation, solvency risks, and a changing regula-

tory environment. The question of the efficiency of the firms in this industry

is clearly important to determining how the industry will respond to these

challenges and which firms are likely to survive. Despite the importance of

such analysis, there has been very little prior research examining the

efficiency of insurers [see Weiss (1991)]. Moreover, the previous insurance

cost literature typically focused exclusively on scale and scope efficiency

[Cummins and VanDerhei (1979), Fields and Murphy (1989), Grace and

Timme ( 1992)].

The three current studies by Cummins and Weiss (1993), Yuengert (1993),

240 A.N. Berger et al., The efficiency of financial institutions

and Gardner and Grace (1993) go well beyond the existing literature to

examine the X-efficiency of insurers. All three use different frontier

approaches and model specifications and find different results. Cummins and

Weiss (1993) apply the econometric frontier approach with the standard half-

normal assumption for the X-efficiency term to data on property-liability

insurers. Separate models are estimated for small, medium, and large firms,

since firms in these groups may not operate on the same frontier. Cummins

and Weiss report average overall efficiency of 900/, for large insurers, 79% for

medium insurers, and 88% for small insurers. These average efficiency

measures cannot be directly compared to each other since separate cost

functions were estimated for each group. Thus, the lower average efficiency

for the medium-sized group might reflect greater dispersion of efficiency

within this group of insurers rather than lower average efficiency.

The current paper by Yuengert (1993) explicitly allows for heteroskedas-

ticity in both the X-efficiency and random error components in estimating

efficiency for US life insurers. This constitutes an important methodological

advance because it allows for a much richer specification in which scale

efficiency, X-efficiency, and random error may be disentangled. To illustrate

this, Yuengert’s fig. 1 maps out average costs against size for life insurers.

Visual observation reveals that smaller firms generally have higher average

costs than larger firms, although the envelope of the lowest average costs is

approximately constant across firm size. This pattern could indicate scale

economies and a heteroskedastic random error term that has higher variance

for smaller firms. However, this pattern could also indicate a situation in

which there are no scale economies, but heteroskedasticity in the X-efficiency

term as the explanation of the dispersion of costs away from the minimum

for smaller firms. By allowing both the X-efficiency term and the random

error to be heteroskedastic, he lets the data choose between these alterna-

tives. Yuengert shows that the standard approach, which does not allow for

heteroskedasticity in the X-efficiency term, can confound X-efficiencies with

scale efficiencies and result in biased estimates of both measures.

Yuengert’s second methodological innovation is a comparison of the effects

of specifying different probability distributions for the X-efficiencies. He

compares the results of the commonly used half-normal specification to a

more flexible gamma-normal model. He also compares both of these

econometric frontier approaches to the results of the thick frontier and

weighted least squares methods. He finds that measured efficiency does

depend on the method chosen, with the average efficiency ranging from 50%

to 65”/, for the different methods.

The current paper by Gardner and Grace (1993) examines the X-efficiency

of a sample of US life insurers using the distribution-free approach, They

find average efficiency of about 45’j/,, slightly less than that reported by

Yuengert. They also relate firm-specific X-efficiency to rent-seeking activities,

A.N. Berger et al., The efficiency of financial institutions 241

such as those that support entry barriers and price cooperation, as well

as a number of other factors. They find that cost or input efftciency is

positively related to rent-seeking activities, contrary to expectations. These

activities are usually assumed in economic models to waste real resources

as they improve revenues. Presumably, revenue or output efficiency would

also be positively related to rent-seeking activities, although they did not

include the output side. They also find that X-efficiency is virtually inde-

pendent of organizational form (stock vs. mutual) and regulatory factors.

Finally, they find that X-efficiency is positively related to size, which is

consistent with the results for commercial banks reported by Berger et al.

(1993). However, it is also possible that the reported efficiency-size relation-

ship is indicative of heteroskedasticity problems, as discussed by Yuengert

(1993).

The papers by Cummins and Weiss (1993), Yuengert (1993) and Gardner

and Grace (1993) highlight the need for future research in the insurance

industry. First, there is no consensus on the best measurement of outputs

and costs for insurers, or on the average level of efftciency in this industry.

Similar problems have been encountered in research on other financial

services industries. Further research on the robustness of results to the choice

of outputs and costs is needed.

Second, these papers indicate that the insurance industry displays substan-

tial dispersion both across groups and across firms in measured X-efficiency.

Further research is needed in identifying the determinants of efficiency in this

industry in order to predict which groups and individual firms are likely to

survive the increased competition of the future.

Third, more research along the methodological lines started by Yuengert

(1993) should be conducted using both insurance and noninsurance data. His

research strongly suggests that heteroskedasticity, as well as the specification

of the distributions of efficiency and random error, may substantially affect

the results reported by other investigators using standard techniques. If

future research finds that these results are robust, a wholesale change in the

methodology for measuring efficiency may be in order.

Fourth, all of the insurance efficiency studies to date use a cost frontier

approach to examine input efficiency. Additional research using other

methods such as DEA, a profit function, or output distance function, should

also be applied to examine the robustness of the results, and could provide a

more complete understanding of the health of this industry.

Finally, to our knowledge, no research to date has examined the X-

efficiency of insurers outside the US nor have transnational insurers been

specifically examined. Research into X-efficiency in the international arena

should provide insights into the likely results of future cross-border compe-

tition as well as the need for internationally coordinated restructuring of

insurance regulation.

242 A.N. Berger et al., The efficiency of financial institutions

7. The determinants of financial institution efficiency

Since X-efficiency was introduced in the 1960s great strides have been

made in developing techniques to measure it. However, relatively little

empirical research has been devoted to developing an understanding of those

factors which influence a firm’s efficiency. Some prior studies focused on the

impact of regulation and organizational form on costs and scale and scope

efficiencies, but these earlier studies did not relate these factors directly to X-

efficiency [Flannery (1984), Hunter and Timme (1986), Mester (1991)].

A number of the current papers reverse this trend and make inroads into

examining the determinants of financial institutions’ X-efficiency. Factors that

are likely to influence a firm’s X-efficiency may be grouped into (1) agency

problems between owners and managers, (2) regulation and organizational

and legal structures, and (3) scale and scope of operations.

The current study by Mester (1993) examines differences in efficiency for

stock and mutual savings and loans, while Gardner and Grace (1993)

examine the relative efficiency of stock and mutual life insurers. Previously,

Fama and Jensen (1983) and Mayers and Smith (1988) argued that

principal-agent problems should be greater in mutual than stock companies

because of the greater separation of ownership and managerial control.

Hence in the absence of effective monitoring devices, mutual firms should

exhibit lower efficiency than stock firms.

As discussed above, Mester (1993) reports that stock S&Ls are signifi-

cantly less efficient than mutual S&Ls for a period after deregulation,

contrary to theory and previous results. Mester (1993) suggests that this

surprising result may reflect a sample selection problem. The competitive

pressures associated with deregulation plus the interest rate risk and credit

risk problems of many S&Ls during this time period may have encouraged

the least efficient mutuals to convert to stock form to reduce agency costs.

Thus, Mester’s tinding may be the result of a sample selection bias in which

the least efficient mutuals converted in order to improve their efficiency, but

had not yet achieved substantial efficiency gains. By comparison, Gardner

and Grace (1993) find no statistical difference between the efficiency of stock

and mutual U.S. life insurers, consistent with previous insurance studies

[Grace and Timme (1992)].

The current paper by Pi and Timme (1993) also examines the effects of

agency costs on efficiency. To mitigate agency costs, there must be an

effective system for owners to separate decision management from decision

control. CEOs are generally endowed with the most power in the decision

management process. That is, they are typically highly involved in the

development of investment proposals and their on-going implementation. On

the other hand, the board of directors, led by the chairman, is generally

endowed with the most decision control. That is, the chairman and board

typically approve investments and monitor their implementation. Therefore,

A.N. Berger et al., The ef3ciency of financial institutions 243

when the CEO is also board chairman, principal-agent conflicts may be

exacerbated as a result of the concentration of decision management and

control in one individual. However, other mechanisms, such as CEO stock

ownership, outside institutional or large block ownership, and the board’s

proportion of outside versus inside directors, may mitigate these agency

problems.

Pi and Timme find that Chairman-CEO affiliation is associated with lower

efficiency, consistent with expectations. They also report that X-efficiency is

positively related to Nonchairman-CEOs’ percentage of stock ownership, but

it is negatively related to the percentage ownership of Chairman-CEOs.

Finally, they report that X-efficiency is unrelated to institutional and large

block holders ownership and the proportion of inside directors. These results

suggest that management-team structure affects X-efficiency, but that internal

and external monitoring mechanisms may not be as effective as envisioned in

the literature.

It seems likely that regulation has also had effects on efficiency by

influencing a financial institution’s organizational structure. For example,

both state and Federal agencies regulate depository institutions’ ability to

operate branches and engage in nonbank activities, such as investment

banking. Banks often at least partially circumvent regulations by forming

bank holding companies. Bank holding companies are evidence of binding

US regulations since these institutions exist primarily in the US, and not in

other industrialized countries which have much less stringent banking

regulations. Although holding companies may serve some of the same

purposes as the organizational structure they attempt to emulate, they likely

also incur higher costs in doing so. It would be expected that bank holding

companies would exhibit lower X-efficiency than branch banking organiza-

tions with the same assets because of duplications of efforts (e.g., multiple

boards of directors, multiple advertising, etc.). In addition, the holding

company form may inhibit the flow of resources among affiliates relative to

the allocation of resources within a consolidated branching bank.

The current paper by Grabowski et al. (1993) empirically examines the

observed relative efficiency of branch banking organizations (BBS) and multi-

bank holding companies (MBHCs). They report findings consistent with

those predicted by theory - BBS exhibit significantly greater overall efficiency

(72%) than MBHCs (65%). Decomposition of the overall efficiency measure

reveals that both BBS and MBHCs exhibit a relatively high degree of (the

standard measure of) allocative efficiency, suggesting that both use nearly the

right mix of inputs. The results indicate, however, that BBS have a much

higher level of (the standard measure of) pure technical efficiency than

MBHCs, suggesting that BBS are better able to control costs than are

MBHCs. The average MBHC’s after-tax ROA would increase by approxi-

mately 55 basis points if it achieved the same level of efficiency as the

244 A.N. Berger et al., The efficiency of financial institutions

average BB. Thus, the difference in efficiency between BBS and MBHCs

appears to be both statistically and economically significant. If the existence

of the MBHC is primarily an organization’s means of circumventing

regulations, then Grabowski et al.‘s results suggest that substantial gains in

efficiency, profitability, and perhaps safety and soundness might be achieved

by relaxing such regulations.

The current paper by Fare and Primont (1993) provides insights into the

X-efficiency benefits from freely transferring resources within a consolidated

banking organization. They derive the theoretical gain in efficiency resulting

from a single-unit organizational form’s ability to allocate resources, such as

a commercial bank that directly operates a network of branches, relative to

the allocation of resources by a multi-unit organization, such as a bank

holding company that operates the same number of branches. Their results

can be applied to any single-unit versus multi-unit comparison, including

mergers that combine independent banks into a single unit and the

consolidation of separate banks and nonbank holding company afftliates into

a single financial service firm.

Financial institutions’ X-efficiency may also be related to the scale and

scope of operations. The efftciency-scale relationship is important for making

proper managerial decisions and has recently been the focus of attention by

regulators and other government officials. Berger et al. (1993) find that X-

efficiency is strongly positively related to a bank’s scale of operations. Given

that most of their X-efficiency differences are on the output side, this implies

that larger firms may be better able to reach their optimal mix and scale of

outputs, increasing output efficiency. Fried et al. (1993), Gardner and Grace

(1993), Mester (1993) and Pi and Timme (1993) present mixed results for the

relationship between scale and financial institutions’ X-efficiency, although

these studies do not include output efficiency.

The above studies make important inroads into finding the determinants

of financial institution efficiency. They also highlight many of the challenges

and potentially fruitful avenues for future research. First, the determinants of

efficiency should be given more theoretical underpinning. For example, in

recent years there have been many significant advances in our understanding

of principal-agent relationships, but prior to the current papers, little or no

research had linked these agency problems to efficiency. More research is

warranted in the area of linking these theoretical advances to X-efficiency.

Second, more attention should be given to the potential for sample

selection problem when comparing groups of firms. When firms shift among

groups, such as the mutual-to-stock conversions cited by Mester (1993),

controls should be used to avoid sample selection biases. For example, firms

that shift could be deleted from the samples entirely or studied separately.

Third, Fare and Primont (1993) establish the maximum theoretical

efficiency gains owing to a single-unit organization’s better ability to allocate

A.N. Berger et al., The efjiciency of financial institutions 245

resources over a multi-unit organization. However, various factors such as

agency costs, managerial skills, and local market limitations may significantly

inhibit single-unit organizations from achieving these maximum gains.

Ideally, in future research a model like Pare and Primont’s could be used to

establish the maximum gain for the phenomenon under investigation (e.g.,

conversion of MBHC to BB, or mergers of independent banks), and this

value would be compared to the observed actual efficiency gain. Such studies

would be further enhanced by ascertaining the factors which impeded the

achievement of the maximum gain.

Fourth, further research is needed into the relationships among scale,

scope, and X-efficiencies. For example, if larger firms are more X-efficient

(i.e., closer to the frontier), this may offset scale diseconomies (i.e., a higher

cost frontier for larger banks). The research cited here strongly suggests that

such research should be performed using methods that incorporate output

inefhciencies, since the output side seems to be where the relationship

between X-efficiency and scale is strongest. Moreover, prior research has

indicated that the relationship between input measures of efficiency and

profitability is relatively weak [Berger (1991), Timme and Yang (1991)],

suggesting that differences in input efficiencies may be somewhat offset by

differences in output efficiencies or other factors.

Finally, more robust econometric procedures should be employed to

establish the causal relationship between X-efficiency and its hypothesized

determinants. A limitation in some of the studies is that measured X-

efficiency is regressed on a set of these hypothesized determinants, such as

size, ownership structure, capital-asset ratio, etc. This may create problems

of interpretation if efficiency is also a causal factor for some of the regressors.

For example, X-efficiency may be positively related to size because larger

firms become efficient (e.g., by virtue of their ability to achieve optimal

output), or because more efficient firms compete more effectively and become

large. To our knowledge, no study to date has utilized a methodology which

distinguishes the causality of efficiency from the effects of efficiency on the

other variables.

8. Conclusions

The purpose of this introductory article is to summarize the research to

date on the efficiency of financial institutions, analyze the contributions of

the papers presented at the Atlanta conference, and suggest avenues for

future research. The remainder of this special issue contains the more

detailed papers, discussants’ comments, and panel discussions. After each

session of two or three articles, we have included the comments of

discussants who are experts on the topic in order to create a more complete

view. We also include the comments of three panels comprised of academic,

J.B.F.r B

246 A.N. Berger et al., The efficiency of financial institutions

government, and industry experts. The first panel discusses the myths and

realities of the benefits from consolidation in the US banking system. These

panelists’ comments immediately follow the papers and discussants’

comments on bank mergers. The second panel concerns the efficiency of

financial service delivery around the globe, particularly Eastern and Western

Europe and Japan. The third panel gives views about the future of financial

service firms, and includes separate discussions of the future of the banking,

thrift, securities, and insurance industries. The comments of the latter two

panels appear at the end of this special issue.

In closing, we express our gratitude to all the authors, discussants, and

panelists who made this issue possible.

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بناء على الأهمية الاقتصادية لتحرير سعر الفائدة، وحجم قطاع البنوك الأردني فإنه من المفيد أن نختبر أثر هذا التعديل المالي الذي حصل في العام 1989/90 (تحرير سعر الفائدة) على هامش الفائدة في البنوك الأردنية. وتشير نتائج التحاليل الإحصائية إلى أن تحرير سعر الفائدة لم يؤثر في هامش الفائدة. كما تشير النتائج إلى أن المحددات الرئيسة لهامش الفائدة هي إجمالي القروض إلى الأصول، ومخصصات الديون المشكوك فيها إلى إجمالي القروض. وبعبارة أخرى، فإن البنوك الأردنية لا تحمل العملاء تكلفة الإقراض ومخصصاتها من خلال توسيع هوامش سعر الفائدة.

Measuring Cost Efficiency in the Property-Liability Insurance Industry

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Full-text available

Jan 1993

Banking efficiency in the Nordic countries1

Article

Full-text available

Apr 1993
J BANK FINANC

Evidence of the relative competitiveness of the banking industries in three Nordic countries is provided, by applying Data Envelopment Analysis of productivity on the national and the pooled data sets. The analysis produces a detailed account of how well banks from different countries and different sizes may be prepared to meet the more intense competition of a common European banking market.

An Examination of Cost Economies in the United States Life Insurance Industry

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Full-text available

Mar 1992

Using an industry sample of 423 U.S. life insurers, this study reports estimates of overall and product specific scale economies, as well as, pair-wise cost complementarities for a wide variety of products. Estimates of these cost characteristics are provided for numerous output vectors since theory suggests that the magnitude of scale economies and cost complementarities may vary with the scale and mix of outputs. In contrast, previous studies only provide a single point estimate of industry cost characteristics using the sample mean output vector. This study, therefore, provides a more complete representation of the industry's cost characteristic and, in turn, new insights into decisions related to the optimal scale and mix of outputs.

Separation of Ownership and Control

Article

Jan 1983

This paper analyzes the survival of organizations in which decision agents do not bear a major share of the wealth effects of their decisions. This is what the literature on large corporations calls separation of 'ownership' and 'control.' Such separation of decision and risk bearing functions is also common to organizations like large professional partnerships, financial mutuals and nonprofits. We contend that separation of decision and risk bearing functions survives in these organizations in part because of the benefits of specialization of management and risk bearing but also because of an effective common approach to controlling the implied agency problems. In particular, the contract structures of all these organizations separate the ratification and monitoring of decisions from the initiation and implementation of the decisions.

CHANGES IN THE COST OF INTERMEDIATION - THE CASE OF SAVINGS AND LOANS

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Sep 1990

Economies of scale in the US savings and loan industry: 1973–83

Article

Feb 1990

The question addressed in this research is whether or not there exist economies of scale in the US savings and loan (S&L) industry. The extant literature, based on limited regional samples, suggests that it depends upon the specification of the function used to estimate costs. This study employs the complete S&L industry data base and two distinctly different methodologies, both of which yield similar results for the time period studied. Specifically it was found (a) that cost reductions through asset expansion were possible for firms in the US S&L industry;(b) that from 1979 to 1983 the minimum efficient scale for associations declined;(c) that a substantial change has occurred in operating costs and the estimated augmented elasticity.

An index number approach to measuring bank efficiency: An application to mergers

Article

Apr 1993
J BANK FINANC

The current wave of bank mergers has resulted in an interest in the measurement of consequent efficiencies. Previous studies have generally focused on the measurement of merger-induced cost efficiencies. In this paper, we turn to the production side and examine the change in relative productivity arising from bank mergers. After deriving our superlative multilateral productivity index, we compute the indexes for our entire sample of banks, approximately 2,000 banks in each of the years 1984–1988, and for a cohort of 160 banks that merged in 1986. We find that acquiring banks achieve no gains in efficiency, and this finding supports those of other studies. On the other hand, we find that our acquiring banks are consistently more productive than the sample as a whole. By implication, if mergers can be generally characterized as the acquisition by a relatively more productive bank of a relatively less productive bank, an empirically valid supposition, then industry performance should improve as a result of these mergers.

Allocative Efficiency V. X-Efficiency

Article

Jan 1966

Harvey Leibenstein

Measuring Cost Efficiency in the Property-Liability Insurance Industry

Article

Apr 1993
J BANK FINANC

Escalating prices and periodic availability problems in property-liability insurance markets are often attributed to insurance company inefficiency and lax claims settlement practices. This paper investigates these issues by estimating stochastic cost frontiers for three size-stratified samples of property-liability insurers over the period 1980–1988. A translog cost function and input share equations are estimated using maximum likelihood techniques. The results show that large insurers operate in a narrow range around an average efficiency level of about 90 percent relative to their cost frontier. Efficiency levels for medium and small insurers are about 80 and 88 percent in relation to their respective frontiers. Wider variations in efficiency are present for these two groups in comparison with large insurers. Large insurers slightly over-produce loss settlement services, while small and medium-size insurers under-produce this output. The small and intermediate size groups are characterized by economies of scale, suggesting the potential for cost reductions from consolidations in the industry.

The Measurement of Productive Efficiency

Article

Jan 1957
J Roy Stat Soc

Michael J. Farrell

The efficiency of financial institutions: A review and preview of research past, present and future

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