166 J.A. Haslem et al. International Review of Economics and Finance 8 1999 165–182
In addition to these exogenous factors, management behaviors were also extremely important. For example, Park 1994 found that some managers contributed to in-
creased bank risk by adopting high variance strategies.
2
These actions, especially in banks with inadequate cost controls and poor investment choices, had adverse results
that contributed to bank inefficiency. Graham and Horner 1988 also detailed the importance of management to bank
performance. They found that internal factors greatly influenced the degree to which adverse external conditions harmed banks, with management and the board of direc-
tors the ultimate determinants of bank success or failure.
3
The Economist 1990
surveyed the world of international banking and noted that the 1980s “did not add up to the best of times for America’s commercial banks.” Further, “efficiency and
profitability, not size, became the holy grail; but for many banks it remained elusive.”
4
In particular, 1987 was terrible year for large U.S. banks. This was due to the increasingly severe problems with LDC loans, especially in Latin America, that banks
belatedly began to acknowledge in a significant way on their financial statements. The year was characterized by huge increases in loan-loss provisions, which, of course,
greatly impacted reported financial performance.
5
The elusiveness of bank efficiency during the 1980s and its implications for profitabil- ity performance provided the motivation for this study.
6
Specifically, the objective was to analyze the 1987 and 1992 efficiency of large U.S. banks for purposes of: 1
identifying inefficient banks; 2 determining and profiling the inputoutput variables associated with inefficiency; and 3 profiling and summarizing the financial perfor-
mance of efficient and inefficient banks.
As reviewed in Evanoff and Israilevich 1991, earlier production theory recognized two basic types of bank inefficiency: output inefficiency, which represents suboptimal
andor deficient output production; and input inefficiency, which represents excess inputs andor suboptimal input mixes for a given levels of output.
7
The story that emerged from the study was that efficiency was a major problem. First, some 20 of the sample of large U.S. banks were identified as inefficient in
1987 and in 1992. Second, some 50–60 of the total inputsoutputs of inefficient banks were excessivedeficient, with inputs proportionately more inefficient than outputs.
Third, management should focus on overall efficiency, but with particular attention to inputs, especially excess cash and real capital, and foreign loans among the outputs.
Fourth, in 1987, the herd instinct that had led to the LDC loan crisis caused the methodology to find foreign loans as positive contributors to efficiency and incorrectly
identified efficient and inefficient banks, when measured by financial performance. Fifth, by 1992, banking had normalized and foreign activities were again profitable, and
inefficient banks were deficient in foreign loans. Sixth, in 1992, efficient banks were more profitable than inefficient banks across all measures—overall, foreign and domestic.
2. Methodology and model
Data Envelopment Analysis DEA was used to identify efficient and inefficient sample banks. Traditionally, DEA was used to analyze the relative efficiency of public
J.A. Haslem et al. International Review of Economics and Finance 8 1999 165–182 167
sector organizations, such as schools, hospitals, prisons and military operations, but more recently it has been applied to banks.
8
The theory, development and applications of DEA, as well as its strengths and weaknesses, have been detailed elsewhere and are beyond this paper’s empirical
scope.
9
Nonetheless, a brief review may be helpful. DEA was originally developed for use in service organizations, where the form of the production function is unknown
or perhaps not even considered. DEA has the advantage of being a flexible, nonpara- metric technique that makes no assumptions about the form of the production function.
Instead, it estimates an empirical “best practice production frontier” from the observed inputsoutputs of individual decision-making units DMUs, which replicates their
individual behavior rather than the average sample estimate of conventional produc- tion functions. A DMU is found to be “efficient” when comparisons with other units
indicate no inefficiency in the utilization of inputsoutputs, as measured by its position relative to the efficient frontier. The DEA best practice frontier is generally piecewise
linear and approximates the true production function. DEA is so-named because the data from the best practice DMUs generate the production frontier and thereby
“envelop” the data from the other DMUs.
10
Since its origins, the term DEA has been broadened as additional models for enhancing its advantages in measuring input
output efficiency have been developed.
11
The Integrated Data Envelopment System IDEAS was used to analyze bank efficiency.
12
The model selected was specified as: 1 variable returns to scale VRS, rather than constant returns to scale CRS; 2 “base” “nonoriented,” equal treat-
ment of inputsoutputs in the determination of any output “slack” andor input excess inefficiency, rather than input or output orientation; and 3 “units invariant” measure-
ment of efficiencyinefficiency i.e., efficiency scores are independent of units of mea- surement, rather than “standard” measurement of DMU variables.
13
Consistent with the base assumption, the inputoutput variables were treated as under management’s
simultaneous and discretionary control. In sum, choice of model specifications was guided by the scale economies, managerialinstitutional characteristics, and environ-
ment of the sample banks e.g., the inputoutput correspondence in assetliability management.
The distance measure delta was the primary criterion used to measure bank total efficiency. Delta was optimized to minimize each bank’s total inputoutput waste, as
measured by the weighted aggregation of the differences between each bank’s observed inputoutput points and those projected on the envelopment surface. If the delta score
was zero, the bank was identified as efficient, and its observed and the projected points were the same. If the score was greater than zero, the bank was identified as
inefficient, and its observed and the projected points differed.
Thus, using the base model specified in Ali and Seiford 1993, consider the case of n banks, each utilizing, in varying amounts, m distinct balance sheet inputs in order
to produce s different balance sheet outputs. This model is specified to minimize “total waste” and is represented mathematically as Eq. 1:
min
sr,ei
1
o
s r
5
1
m
rl
s
r
1
o
m i
5
1
n
il
e
i
2
1
168 J.A. Haslem et al. International Review of Economics and Finance 8 1999 165–182
The variable s
r
is the amount of slack foregone balance sheet output r, while the variable e
i
is the amount of excess balance sheet input i utilized. The values m
rl
and n
il
are shadow prices, or the marginal value of a unit of output or input. The analysis specifies bank specific bounds on the values of m
rl
and n
il
defined by Eq. 2: m
rl
5 1
y
rl
, r 5 1, . . . , s with n
il
5 1
x
il
, i 5 1, . . . , m 2
This procedure allows the projections and efficiency scores derived to be indepen- dent units invariant of the units of measurement for the data. This is a linear
programming problem with associated resource constraints and convexity conditions as discussed in Ali Seiford [1993].
The solution to the above problem identifies, for each bank, l, a projected point on the efficient frontier, xˆ
l
, yˆ
l
where the items x and y are the vectors of inputs and outputs. The particular form of the model allows for variable returns to scale. The
essence of the efficiency evaluation of a particular bank with an actual achieved combination of x
l
and y
l
is the identification of excesses in input utilization x
l
2 xˆ
l
and deficiencies in output yˆ
l
2 y
l
. A particular bank is deemed efficient if x
l
, y
l
5 xˆ
l
, yˆ
l
, the bank thus lying on the efficient frontier. Thus, one possible measure of inefficiency, delta or D
l
, can be defined by Eq. 3: D
l
5 m
l
yˆ
l
2 y
l
1 n
l
x
l
2 xˆ
l
3 Notice, that for efficient banks D
l
5 0.
As noted above, the base model was derived. This model embodies an approach consistent with the “systems-oriented” philosophy in banking where input and output
variables are simultaneously managed and determined. Two other oriented efficiency measures were used in the IDEAS model to provide
supplemental information on bank efficiency: iota and omicron. Any one of the three noted criteria could have been used to identify inefficient banks, but the interpretation
of inefficiency appropriately followed the primary criterion, delta. Iota and omicron measured bank efficiency in terms of proportional variable change.
14
3. InputOutput variables
