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Benchmarking
Benchmarking the efficiency the Korean
of the Korean banking sector: banking sector
a DEA approach
107
Fadzlan Sufian
Khazanah Nasional Berhad, Kuala Lumpur, Malaysia and
Universiti Putra Malaysia, Serdang, Malaysia
Abstract
Purpose – The purpose of this paper is to critically examine the sources of inefficiency in the Korean
banking sector. The present study focuses on three different approaches: intermediation approach,
value-added approach, and operating approach, to differentiate how efficiency scores vary with changes
in inputs and outputs.
Design/methodology/approach – The paper utilizes the non-parametric data envelopment analysis
methodology to measure the efficiency of banks operating in the Korean banking sector. The method
allows for the decomposition of technical efficiency (TE) into its mutually exhaustive components of
pure technical and scale efficiencies.
Findings – The empirical findings suggest that estimates of TE are consistently higher under
`
an operating approach vis-a-vis the intermediation and value-added approaches. On the other hand,
banks are characterized by a relatively low level of TE under the intermediation approach.
Research limitations/implications – Further analysis on the performance of the Korean banking
sector performance will examine the efficiency changes over time by employing the parametric
stochastic frontier analysis method. Investigations into productivity changes over time, as a result of a
technical change or technological progress or regression by employing the Malmquist productivity
index could yet be another extension to the paper.
Practical implications – The findings from this study are essential not only for the managers of the
banks, but for numerous stakeholders such as the central banks, bankers associations, governments,
and other financial authorities. Knowledge of these factors would also be helpful to the regulatory
authorities and bank managers who formulate going forward policies for improved efficiency of the
Korean banking sector.
Originality/value – Unlike the previous studies on the efficiency of the Korean banking sector,
the paper focuses on three different approaches: intermediation approach, value-added approach,
and operating approach to differentiate how efficiency scores vary with changes in inputs and outputs.
Keywords Korea, Banking, Organizational performance
Paper type Research paper
1. Introduction
The banking sector is the backbone of the Korean economy and plays an important
financial intermediary role. Therefore, the health of the sector is very critical to the
health of the economy at large. Given the relation between the well being of the banking
sector and the growth of the economy (Rajan and Zingales, 1998; Levine, 1998; Levine
and Zervos, 1998; Cetorelli and Gambera, 2001; Beck and Levine, 2004), knowledge Benchmarking: An International
Journal
Vol. 18 No. 1, 2011
The author would like to thank Angappa Gunasekaran (the Editor) and two anonymous referees pp. 107-127
q Emerald Group Publishing Limited
for the constructive comments and suggestions, which have significantly improved the contents 1463-5771
of the paper. The usual caveats apply. DOI 10.1108/14635771111109841

2. BIJ of the underlying factors that influence the banking sector’s efficiency is therefore
18,1 essential, not only for the managers of the banks, but for numerous stakeholders, such as
the central banks, bankers associations, governments, and other financial authorities.
Knowledge of these factors would also be helpful to help the regulatory authorities
and bank managers formulate going forward policies for improved efficiency of the
Korean banking sector.
108 The purpose of the present study is to extend the earlier works on the performance
of the Korean banking sector and examine the efficiency of the Korean banking
sector during the period of 1992-2003. The efficiency estimate of each bank is computed
by using the data envelopment analysis (DEA) method. The method allows us to
distinguish between three different types of efficiency measures, namely technical,
pure technical, and scale. The DEA method has many advantages over traditional
parametric techniques such as regression techniques. While regression analysis focuses
on central tendency and approximates the efficiency of banks under investigation
relative to the average performance, DEA in contrast, focuses on the yearly
observations of individual banks and optimizes the performance measure of each bank.
We differentiate this paper from previous ones that focus on the Korean banking sector
and contribute to the present literature in several respects discussed below.
First, the previous studies that examined the efficiency of the Korean banking sector
adopt the intermediation approach. On the other hand, the present study compare the
results obtained from the intermediation approach with value-added or revenue
approach that was recently proposed by Drake et al. (2006) and operating approach.
This allows us to observe if different input and output definitions affect the obtained
efficiency levels.
Second, unlike the previous studies that examined the efficiency of the Korean
banking sector, the present study constructs and analyzes the results derived from
dynamic panels. Constructing a separate frontier for each of the years under study is a
critical issue in a dynamic business environment because a bank may be the most
efficient in one year, but may not be in the following year. Within the context of the
Korean banking sector, it becomes more crucial, as there is an ongoing liberalization in
the banking sector over the estimation period. A dynamic panel analysis may also
highlight any significant changes taking place in the Korean banking sector during the
period under study induced by the Bank of Korea (BOK) supervisory policies.
Finally, the present study attempts to critically analyze the returns to scale in the
Korean banking sector, which has not been critically examined in the previous research.
Given the initiatives by the BOK to strengthen the banking sector, which among others
involved mergers and acquisitions of the domestic banking institutions, the precise
nature of scale efficiency (SE) is critically important to both comprehend the economic
rationale and to prescribe their going forward policies. Furthermore, it will be of
practical interest to estimate and compare the returns to scale of the nationwide
commercial banks[1] compared to their regional commercial bank[2] peers given that
the size of the former are much larger than that of the latter. Furthermore, the regional
commercial banks only maintain branch banking within their 0 localities and mainly
serve small- and medium-sized enterprises, households, and individual borrowers in
their respective regions. On the other hand, the nationwide commercial banks are
allowed to maintain branch banking systems throughout the country and engage in both
short- and long-term financing.

3. This paper is set out as follows: in the next section, we provide reviews of the main Benchmarking
literature. In Section 3, we outline the approaches to the measurement of efficiency the Korean
change. Section 4 discusses the results, and finally we conclude in Section 5.
banking sector
2. Review of the literatures
Since its introduction by Charnes et al. (1978) and Banker et al. (1984), researchers have
welcomed DEA as a methodology for performance evaluation (Gregoriou and Zhu, 109
2005). However, a large body of literature exists on banking efficiency in the USA
(see surveys in Berger et al., 1993; Berger and Humphrey, 1997; Berger, 2007, and
references therein) and the banking systems in the Western and developed countries
(Sathye, 2001; Drake, 2001; Canhoto and Dermine, 2003; Webb, 2003; Fiordelisi, 2007;
Pasiouras, 2008; Sturm and Williams, 2008; Siriopoulos and Tziogkidis, 2010).
Fukuyama (1993) who considers the efficiency of 143 Japanese banks in 1990 is
among the earliest to employ frontier estimation technique to examine the performance
of Asian banks. The results suggest that banks of different organizational status
perform differently in respect to all efficiency measures and that SE is positively,
but weakly associated with bank size.
Single country studies focusing on the Asian banking sectors have mainly
concentrated on the comparison between the foreign and domestic banks’ performance.
Generally, the empirical evidence showed that foreign banks have succeeded in
capitalizing on their advantages and exhibit a higher level of efficiency than their
domestic bank peers. Leightner and Lovell (1998) find that the average domestic
Thai banks experienced falling total factor productivity (TFP) growth, while the
average foreign bank experienced increasing TFP. Unite and Sullivan (2003) suggest
that the entry of foreign banks in the Philippines has resulted in the reduction of interest
rate spreads and bank profits of the domestic banks that are affiliated with family
business groups. In a study on the Malaysian banking sector, Matthews and Ismail
(2006) suggest that foreign banks in Malaysia have exhibited a higher level of technical
efficiency (TE). They also suggest that the productivity of the domestic banks is more
susceptible to macroeconomic shocks than their foreign bank counterparts.
The South Asian banking sectors have also been studied extensively. Sathye (2003)
and Shanmugam and Das (2004) find that the public and foreign-owned banks in India
have exhibited a higher level of TE compared to their privately owned bank peers.
Iimi (2004) suggests that privatized banks in Pakistan are the most efficient, followed by
the foreign-owned banks, while the public banks are the least efficient. Hardy and di Patti
(2001) investigate the effects of financial reforms on profitability, cost, and revenue
efficiency of the Pakistan banking sector during 1981-1998. They show that financial
liberalization has positive impact on banks’ performance. Subsequently, di Patti and
Hardy (2005) examine the cost and profit efficiency of Pakistan commercial banks
during the period 1981-2002. They find that financial liberalization leads to higher bank
profitability, but only during the first round of financial reform of 1991-1992.
Despite substantial studies performed in regard to the efficiency of financial institutions
in the USA, Europe, and other Asia-Pacific banking industries, empirical evidence on the
Korean banking sector is relatively scarce. By employing the Malmquist productivity
index (MPI) approach, Gilbert and Wilson (1998) focus on the impact of deregulation in
the 1980s on the productive efficiency of Korean private banks during the post-1980s to
pre-crisis years, showing that deregulation did improve bank efficiency. Hao et al. (2001)

4. BIJ employ the stochastic cost frontier approach and focus on the pre-crisis period of 1985-1995.
18,1 The results indicate that high-growth institutions with vast geographical networks and
banks funded with retail deposits were the most efficient during that era. Most recently,
Park and Weber (2006) examine the efficiency and productivity of the Korean banking
sector during the 1992-2002 period. The empirical findings derived from the directional
technology distance function method suggest that the Korean banking sector exhibits
110 productivity growth attributed to technical progress, which outweighs declines in
efficiency. Other studies like An et al. (2007), Jeon et al. (2006), Choi and Hasan (2005) and
Choe and Lee (2003) examine the performance of Korean banking sector by employing
central tendency regression approach rather than frontier estimation techniques.
3. Methodology and data
3.1 Data envelopment analysis
The present study employs the DEA method, first introduced by Charnes et al. (1978)
(hereafter the CCR model) to estimate the input-oriented TE of the Korean banking
sector. The DEA method involves constructing a non-parametric production frontier
based on the actual input-output observations in the sample relative to which efficiency
of each bank in the sample is measured (Coelli, 1996). This approach measures the
efficiency of a decision-making unit (DMU) relative to other similar DMUs with the
simple restriction that all DMUs lay on, or below the efficiency frontier. If a DMU lies on
the frontier, it is referred to as an efficient unit; otherwise, it is labeled as inefficient.
The data are enveloped in such a way that radial distances to the frontier are minimized.
The CCR model can be formulated as follows:
" #
X 2 X þ
m s
min l 0 2 1 Si þ Sr ð1Þ
i¼1 r¼1
X
N
subject to : lf xif ¼ l o xif o 2 S 2
i where i ¼ 1. . .m
f ¼1
X
N
lf yrf ¼ S þ þ yrf o
r where r ¼ 1. . .s
f ¼1
lf $ 0; f ¼ 1. . .N ; S 2 ; S þ $ 0 ; i and r
i r
where xif and yrf are levels of the ith input and rth output, respectively, for DMU f.
N is the number of DMUs. 1 is a very small positive number (non-Archimedean) used
as a lower bound to inputs and outputs. lf denotes the contribution of DMU f in
deriving the efficiency of the rated DMU fo (a point at the envelopment surface). S 2 and
i
S þ are slack variables to proxy extra savings in input i and extra gains in output r. lo is
r
the radial efficiency factor that shows the possible reduction of inputs for DMU fo. If l* o
(optimal solution) is equal to one and the slack values are both equal to zero, then DMU
fo is said to be efficient. When S 2 or S þ take positive values at the optimal solution,
i r
one can conclude that the corresponding input or output of DMU fo can improve further
once input levels have been contracted to the proportion l* .o
The CCR model presupposes that there is no significant relationship between
the scale of operations and efficiency by assuming constant returns to scale (CRS) and

5. it delivers the overall TE. The CRS assumption is only justifiable when all DMUs are Benchmarking
operating at an optimal scale. However, banks in practice may face either economies or the Korean
diseconomies of scale. Thus, if one makes the CRS assumption when not all DMUs are
operating at the optimal scale, the computed measures of TE will be contaminated banking sector
with SE.
Banker et al. (1984) extended the CCR model by relaxing the CRS assumption. The
resulting Banker, Charnes, and Cooper (BCC) model is used to assess the efficiency of 111
DMUs characterized by variable returns to scale (VRS). The VRS assumption provides
the measurement of pure technical efficiency (PTE), which is the measurement of
TE devoid of the SE effects. If there appears to be a difference between the TE and
PTE scores of a particular DMU, then it indicates the existence of scale inefficiency,
i.e. TE ¼ PTE £ SE. The former relates to the capability of managers to utilize banks’
given resources, whereas the latter refers to exploiting scale economies by operating at
a point where the production frontier exhibits CRS.
The input-oriented BCC model with VRS assumption can be represented by the
following linear programming problem:
" #
X 2 X þ
m s
min l o 2 1 Si þ Sr ð2Þ
i¼1 r¼1
X
N
subject to : lf xif ¼ l o xif o 2 S 2
i where i ¼ 1. . .m
f ¼1
X
N
lf yrf ¼ S þ þ yrf o
r where r ¼ 1. . .s
f ¼1
X
N
lf ¼ 1
f ¼1
lf $ 0; f ¼ 1. . .N ; S 2 ; S þ $ 0 ; i and r
i r
The BCC model differs from the CCR model in that it includes the so-called convexity
P
constraint, N¼1 lf ¼ 1, which prevents any interpolation point constructed from the
f
observed DMUs from being scaled up or down to form a referent point. In this model, the
set of l values minimize lo to l* and identify a point within the VRS assumption, of which
o
the input levels reflect the lowest proportion of l* . At l* , the input levels of DMU fo can be
o o
uniformly contracted without detriment its output levels. Therefore, DMU fo has
efficiency equal to l* . The solution to model (2) is summarized in the following fashion:
o * *
DMU fo is pareto-efficient if l* ¼ 1 and S þ ¼ 0; r ¼ 1. . .s; S 2 ¼ 0; i ¼ 1. . .m.
o r i
If the convexity constraint in model (2) is dropped, one obtains model (1), which
generates TE under the CRS assumption. This implies that PTE of a DMU is always
greater or equal to its TE. Under the VRS assumption, the resulting SE can be measured,
since in most cases, the scale of operation of the firm may not be optimal. The firm
involved may be too small in its scale of operation, which might fall within the increasing

6. BIJ returns to scale (IRS) part of the production function. Similarly, a firm may be too
18,1 large and operate within the decreasing returns to scale (DRS) part of the production
function. In both cases, efficiency of the firms may be improved by changing their
scale of operation. If the underlying production technology follows CRS, then the firm
is automatically scale efficient. The resulting ratio illustrates SE, which is the impact
of scale size on the efficiency of a DMU. Formally, the SE of DMU fo is given as TE/PTE.
112 Where, TE and PTE are technical efficiency and pure technical efficiency of DMU fo,
respectively.
Since PTE is always greater or equal to TE, it means that SE (TE/PTE) is less or equal
to unity. If TE and PTE of a DMU are equal, then SE is equal to one. This means that
irrespective of scale, size has no impact on efficiency. If the TE scores derived from the
CRS assumption is less than the TE scores derived from the VRS assumption, then
SE will be below unity, meaning that the scale of operation does impact the efficiency
of the DMU.
3.2 Specification of bank inputs, outputs, and data
It is commonly acknowledged that the choice of variables in efficiency studies
significantly affects the results. The problem is compounded by the fact that variable
selection is often constrained by the paucity of data on relevant variables. The cost and
output measurements in banking are especially difficult because many of the financial
services are jointly produced and prices are typically assigned to a bundle of financial
services. The role of the commercial banks is generally defined as collecting the savings of
households and other agents to finance the investment needs of firms and consumption
needs of individuals. Four main approaches dominate the literature: the production
approach, the intermediation approach, the operating approach, and more recently,
the revenue or profit-oriented approach. The first two approaches apply the traditional
microeconomic theory of the firm to banking and differ only in the specification of banking
activities. The final two approaches go a step further and incorporate some specific
activities of banking into the classical theory and thereby modify it.
Under the production approach, pioneered by Benston (1965), a financial institution is
defined as a producer of services for account holders, that is, they perform transactions
on deposit accounts and process documents such as loans. Hence, according to this
approach, the number of accounts or its related transactions is the best measure for
output, while the number of employees and physical capital are considered as inputs.
However, the production approach might be more suitable for branch efficiency studies,
as at most times bank branches basically process customer documents and bank
funding, while investment decisions are mostly not under the control of branches
(Berger and Humphrey, 1997).
The intermediation approach on the other hand, assumes that financial firms act as an
intermediary between savers and borrowers and posits total loans and securities
as outputs, whereas deposits along with labour and physical capital are defined as
inputs (Sealey and Lindley, 1977). The operating approach (or income-based approach)
views banks as business units with the final objective of generating revenue from the
total cost incurred for running the business (Leightner and Lovell, 1998). Accordingly,
it defines banks’ output as total revenue (interest and non-interest income) and inputs as
the total expenses (interest and non-interest expenses). More recently, Drake et al. (2006)
proposed the revenue approach in DEA. The revenue approach (or income-based

7. approach) views banks as business units with the final objective of generating revenue Benchmarking
from the total cost incurred for running the business (Leightner and Lovell, 1998). the Korean
Accordingly, it defines banks’ output as total revenue (interest and non-interest income)
and inputs as the total expenses (interest and non-interest expenses). banking sector
The appropriateness of each approach varies according to the circumstances.
It is apparent that banks undertake simultaneous functions. However, based on practical
considerations and to examine the robustness of the estimated efficiency scores under 113
various alternatives, the present study focuses on three major approaches: intermediation
approach, operating approach, and value-added approach. Under the intermediation
approach, we assume deposits (X1), labour (X4), and capital (X2) as inputs for producing
loans (Y1) and investments (Y2). Under the operating approach, two types of inputs are
considered namely, interest expenses (X3) and labour (X4). The relevant outputs are
interest income (Y4) and non-interest income (Y5) emanating mostly from commission,
exchange, brokerage, etc. Under the value-added approach, labour (X4), capital (X2),
and interest expenses (X3) are used as inputs producing outputs like deposits (X1), loans
(Y1), and investments (Y2).
We use the annual bank level data of Korean commercial banks over the period
1992-2003. The variables are obtained from published balance sheet information in
annual reports of each individual bank. The final sample consists of 31 banks, which
account for more than 80 percent of the Korean banking sector’s total assets. Table I
presents summary statistics of the output and input variables used to construct the
DEA model.
4. Empirical findings
In this section, we will discuss the TE change of the Korean banking sector, measured by
the DEA method and its decomposition into PTE and SE components. The efficiency of
the Korean banking sector is first examined by applying the DEA method for each year
under investigation. To allow efficiency to vary over time, the efficiency frontiers are
constructed for each year by solving the linear programming (LP) problems rather than
constructing a single multi-year frontier.
Non- Non-
Interest interest Total Interest interest
Loans Investments income income deposits Capital expenses expense
(Y1) (Y2) (Y3) (Y4) (X1) (X2) (X3) (X4)
Min. 304.65 82.36 16.15 11.74 82.00 0.27 11.09 10.50
Mean 36,336.16 4,793.75 2,931.93 622.26 37,596.46 552.26 1,485.15 873.72
Max. 126,005.20 29,846.61 9,616.51 2,424.91 131,068.00 1,528.13 6,148.47 2,938.84
SD 28,568.93 5,320.97 2,211.16 521.67 29,328.75 402.07 1,148.98 634.82
Notes: Y1 – loans (includes loans to customers and other banks); Y2 – investments (includes dealing
and investment securities); Y3 – interest income; Y4 – non-interest income (defined as fee income and
other non-interest income, which among others consist of commission, service charges and fees,
guarantee fees, and foreign exchange profits); X1 – total deposits (includes deposits from customers and
other banks); X2 – capital (measured by the book value of property, plant, and equipment); X3 – interest Table I.
expenses; X4 – non-interest expense Descriptive statistics
Source: Banks annual reports and authors own calculations for inputs and outputs

8. BIJ 4.1 Efficiency of the Korean banking sector
18,1 The summary results of technical, pure technical, and SE estimates under the three
approaches are presented in Tables II-IV, respectively. The average TE estimate (M)
represents the average of all optimal values obtained from the CCR model for each
bank (Table II). The empirical results suggest a large asymmetry between banks
regarding their TE scores. In particular, the different approaches of classifying inputs
114 and outputs of banks produced divergent sets of efficiency estimates. The estimates of
`
TE were observed to be consistently higher under operating approach vis-a-vis the
No. of Average Percentage Percentage
No. of efficient efficiency SD Interval of banks in of banks I SD
Year banks banks (M) (s ) (I ¼ M 2 s) (I ¼ M þ s) I below mean
Intermediation approach
1992 11 2 0.736 0.218 0.518 0.954 72.73 9.09
1993 14 2 0.787 0.154 0.633 0.941 71.43 7.14
1994 15 3 0.719 0.183 0.536 0.902 60.00 20.00
1995 16 3 0.867 0.100 0.767 0.967 68.75 12.50
1996 18 4 0.845 0.131 0.714 0.976 55.56 11.11
1997 23 2 0.622 0.158 0.464 0.780 82.61 4.35
1998 25 3 0.606 0.193 0.413 0.799 68.00 16.00
1999 24 3 0.714 0.177 0.537 0.891 66.67 16.67
2000 29 4 0.719 0.169 0.550 0.888 65.52 13.79
2001 26 3 0.741 0.155 0.586 0.896 61.54 19.23
2002 23 3 0.653 0.175 0.478 0.828 69.57 13.04
2003 23 3 0.678 0.166 0.512 0.844 73.91 8.70
Value-added approach
1992 11 5 0.942 0.078 0.864 1.020 81.82 18.18
1993 14 5 0.897 0.111 0.786 1.008 78.57 21.43
1994 16 6 0.893 0.107 0.786 1.000 75.00 25.00
1995 17 5 0.866 0.119 0.747 0.985 52.94 17.65
1996 19 7 0.902 0.111 0.791 1.013 84.21 15.79
1997 24 5 0.882 0.097 0.785 0.979 62.50 16.67
1998 25 5 0.682 0.205 0.477 0.887 68.00 8.00
1999 24 10 0.908 0.125 0.783 1.033 91.67 8.33
2000 29 2 0.546 0.159 0.387 0.705 82.76 6.90
2001 26 2 0.750 0.152 0.598 0.902 76.92 11.54
2002 23 1 0.727 0.163 0.564 0.890 73.91 17.39
2003 23 7 0.890 0.130 0.760 1.020 82.61 17.39
Operating approach
1992 11 6 0.972 0.035 0.937 1.007 63.64 36.36
1993 14 7 0.962 0.052 0.910 1.014 78.57 21.43
1994 16 8 0.935 0.070 0.865 1.005 68.75 31.25
1995 17 6 0.965 0.033 0.932 0.998 47.06 17.65
1996 19 5 0.932 0.090 0.842 1.022 89.47 10.53
1997 24 6 0.926 0.068 0.858 0.994 62.50 12.50
1998 25 9 0.871 0.124 0.747 0.995 44.00 20.00
1999 24 6 0.913 0.091 0.822 1.004 75.00 25.00
2000 29 5 0.878 0.119 0.759 0.997 68.97 13.79
Table II. 2001 26 6 0.846 0.199 0.647 1.045 80.77 19.23
Average TE 2002 24 5 0.858 0.118 0.740 0.976 62.50 16.67
of Korean banks 2003 23 5 0.833 0.133 0.700 0.966 60.87 17.39

9. Benchmarking
No. of Average Interval Percentage Percentage
No. of efficient efficiency SD of banks in of banks I SD the Korean
Year banks banks (M) (s ) (I ¼ M 2 s) (I ¼ M þ s) I below mean banking sector
Intermediation approach
1992 11 5 0.868 0.208 0.660 1.076 90.91 9.09
1993 14 4 0.852 0.150 0.702 1.002 92.86 7.14 115
1994 15 6 0.811 0.187 0.624 0.998 46.67 13.33
1995 16 8 0.921 0.088 0.833 1.009 75.00 25.00
1996 18 9 0.888 0.127 0.761 1.015 83.33 16.67
1997 23 7 0.727 0.204 0.523 0.931 56.52 13.04
1998 25 7 0.678 0.243 0.435 0.921 52.00 16.00
1999 25 7 0.785 0.188 0.597 0.973 44.00 24.00
2000 29 8 0.817 0.160 0.657 0.977 55.17 17.24
2001 26 6 0.797 0.159 0.638 0.956 57.69 15.38
2002 23 6 0.727 0.186 0.541 0.913 56.52 17.39
2003 23 6 0.727 0.182 0.545 0.909 65.22 8.70
Value-added approach
1992 11 7 0.955 0.078 0.877 1.033 81.82 18.18
1993 14 8 0.922 0.106 0.816 1.028 85.71 14.29
1994 16 8 0.921 0.101 0.820 1.022 81.25 18.75
1995 17 11 0.917 0.123 0.794 1.040 76.47 23.53
1996 19 12 0.947 0.085 0.862 1.032 78.95 21.05
1997 24 12 0.932 0.097 0.835 1.029 75.00 25.00
1998 25 7 0.712 0.211 0.501 0.923 60.00 8.00
1999 24 14 0.943 0.114 0.829 1.057 87.50 12.50
2000 29 9 0.798 0.197 0.601 0.995 58.62 10.34
2001 26 4 0.796 0.159 0.637 0.955 69.23 11.54
2002 23 4 0.773 0.163 0.610 0.936 65.22 17.39
2003 23 10 0.909 0.125 0.784 1.034 78.26 21.74
Operating approach
1992 11 8 0.989 0.022 0.967 1.011 81.82 18.18
1993 14 12 0.998 0.005 0.993 1.003 92.86 7.14
1994 16 11 0.981 0.033 0.948 1.014 81.25 18.75
1995 17 13 0.991 0.020 0.971 1.011 88.24 11.76
1996 19 13 0.989 0.024 0.965 1.013 89.47 10.53
1997 24 16 0.974 0.046 0.928 1.020 87.50 12.50
1998 25 12 0.925 0.104 0.821 1.029 80.00 20.00
1999 24 16 0.962 0.073 0.889 1.035 87.50 12.50
2000 29 11 0.937 0.102 0.835 1.039 89.66 10.34
2001 26 11 0.887 0.170 0.717 1.057 80.77 19.23 Table III.
2002 24 11 0.928 0.097 0.831 1.025 83.33 16.67 Average PTE
2003 23 10 0.917 0.106 0.811 1.023 78.26 21.74 of Korean banks
intermediation and value-added approaches. On the other hand, under the intermediation
approach, banks are characterized by relatively low level of TE. Illustratively, in the year
1999 only three (12.5 percent) banks were found to be efficient and the average TE for all
banks stood at 71.4 percent under the intermediation approach. The number of efficient
banks during the sample period ranged from two banks in 1992 and 1993 to four banks in
1996 under the intermediation approach and six banks in 1992 to nine banks in 1999
under the operating approach. On the other hand, the number of efficiency banks ranged
from a high of ten banks in 1999 to a low of one banks in 2002 under the value-added

10. BIJ
No. of Average Interval Percentage Percentage
18,1 No. of efficient efficiency SD of banks in of banks I SD
Year banks banks (M) (s) (I ¼ M 2 s) (I ¼ M þ s) I below mean
Intermediation approach
1992 11 2 0.857 0.161 0.696 1.018 81.82 18.18
116 1993 14 2 0.928 0.099 0.829 1.027 85.71 14.29
1994 15 3 0.897 0.148 0.749 1.045 86.67 13.33
1995 16 3 0.944 0.092 0.852 1.036 93.75 6.25
1996 18 4 0.952 0.066 0.886 1.018 83.33 16.67
1997 23 3 0.875 0.141 0.734 1.016 78.26 21.74
1998 25 3 0.919 0.121 0.798 1.040 84.00 16.00
1999 25 4 0.915 0.110 0.805 1.025 88.00 8.00
2000 29 4 0.879 0.087 0.792 0.966 75.86 3.45
2001 26 4 0.935 0.106 0.829 1.041 92.31 7.69
2002 23 3 0.909 0.123 0.786 1.032 86.96 13.04
2003 23 3 0.944 0.120 0.824 1.064 91.30 8.70
Value-added approach
1992 11 5 0.986 0.019 0.967 1.005 72.73 27.27
1993 14 6 0.973 0.040 0.933 1.013 78.57 21.43
1994 16 11 0.970 0.059 0.911 1.029 81.25 18.75
1995 17 5 0.946 0.066 0.880 1.012 88.24 11.76
1996 19 7 0.954 0.090 0.864 1.044 89.47 10.53
1997 24 5 0.948 0.052 0.896 1.000 83.33 16.67
1998 25 5 0.959 0.056 0.903 1.015 88.00 12.00
1999 24 11 0.963 0.055 0.908 1.018 87.50 12.50
2000 29 2 0.703 0.173 0.530 0.876 65.52 10.34
2001 26 3 0.944 0.077 0.867 1.021 92.31 7.69
2002 23 4 0.939 0.076 0.863 1.015 82.61 17.39
2003 23 8 0.978 0.041 0.937 1.019 91.30 8.70
Operating approach
1992 11 6 0.983 0.027 0.956 1.010 81.82 18.18
1993 14 7 0.963 0.049 0.914 1.012 78.57 21.43
1994 16 8 0.953 0.062 0.891 1.015 81.25 18.75
1995 17 6 0.973 0.027 0.946 1.000 82.35 17.65
1996 19 5 0.943 0.086 0.857 1.029 94.74 5.26
1997 24 6 0.951 0.048 0.903 0.999 54.17 20.83
1998 25 9 0.941 0.063 0.878 1.004 88.00 12.00
1999 24 10 0.951 0.082 0.869 1.033 87.50 12.50
2000 29 5 0.939 0.093 0.846 1.032 89.66 10.34
Table IV. 2001 26 8 0.950 0.105 0.845 1.055 88.46 11.54
Average SE 2002 24 5 0.925 0.080 0.845 1.005 79.17 20.83
of Korean banks 2003 23 6 0.912 0.117 0.795 1.029 82.61 17.39
approach. In sum, the empirical findings seem to suggest that there was no perceptible
change in number of efficient banks under the intermediation and operating approaches,
although a noticeable increase was observed under the value-added approach during
the year 1999.
Under the operating approach, the dispersion of TE scores as measured by its
standard deviation depicts an increasing trend during the years 1996-1999. On the other
hand, the percentage of banks wherein TE lies within the interval of one standard
deviation around the mean hovered around 89.5 percent in 1996 to 75 percent in 1999

11. under the operating approach and 84.2 percent in 1996 to 91.7 percent in 1999 under Benchmarking
the value-added approach. These numbers were slightly lower under the intermediation the Korean
approach. As the TE estimates itself is time varying, these proportions do not necessarily
corroborate the degree of efficiency of the banking sector. For example, under the banking sector
intermediation approach, around 82.6 percent in 1997 and around 61.5 percent in 2001 of
banks recorded TE within the interval of one standard deviation around the mean. Yet,
banks were much more efficient in 2001 than in 1997. 117
As against the changing benchmark of comparison, these proportions quantify the
number of banks that are close to the average over time and thus merely capture the
kurtosis of the efficiency distribution depending on the approach. For instance, under
the intermediation approach the efficiency scores displays a leptokurtic distribution,
i.e. the efficiency scores has a high peak with a small variance, suggesting that a lot
of scores fall in the center of the distribution. On the other hand, under the operating
approach the efficiency of the Korean banking sector seem to follow a mesokurtic
distribution, i.e. the efficiency scores display a moderate peak with gradual curves
suggesting a normal number of scores in the middle range of the distribution.
Overall, the empirical findings presented in Table II clearly bring forth low degree of
efficiency in the Korean banking sector, particularly a year after the Asian financial
crisis stemming from the under utilization of resources (inputs). Finally, considering
the evolution of efficiency over time, a clear temporal pattern does not emerge from
these different approaches. It is also worth noting from Table II that although in general
TE level seems to deteriorate a year after the Asian financial crisis under all approaches,
the deterioration seems to be more pronounced under the intermediation approach
model.
Table III presents the PTE estimates, while SE estimates under the three approaches
are presented in Table IV. It is observed that over the sample period, both pure technical
and SE measures, especially under intermediation and value-added approaches display
significant variation and the Korean banking sector did not achieve sustained efficiency
gains. Estimates of PTE under the intermediation approach vary from a high of
92.1 percent in 1995 to a low of 67.8 percent in 1998. In most of the years, only about
20-30 percent of banks were found to be pure technically efficient under the
intermediation approach. Interestingly, the percentage of banks whose PTE falls within
the interval of one standard deviation around the mean displayed a large asymmetry,
particularly during the period 1997-1999 under the intermediation and value-added
approaches. It is observed from Table III that under the intermediation approach
the percentage stood at around 56.5 percent in 1997 to 44 percent in 1999, while under
value-added approach the figures stood at around 75 percent in 1997 to 87.5 percent
in 1999.
It is interesting to note that the number of efficient banks under CRS (TE) assumption
and VRS (PTE) assumption differs markedly, irrespective of the choice of various inputs
and outputs. The findings clearly demonstrate low degree of SE among Korean
commercial banks. Under the intermediation approach for example, Table III reveals
that eight banks were found to be efficient under VRS in 2000, whereas only four banks
were found to be efficient under CRS. This indicates that the remaining four banks failed
to reach the CRS frontier owing solely to scale.
It is observed from Tables III and IV that under the operating approach, SE seems to
outweigh PTE in determining the total TE of the Korean banking sector. On the other

12. BIJ hand, under the intermediation approach, the empirical findings seem to suggest
18,1 that PTE outweighs SE in determining the total TE of the Korean banking sector. Finally,
under the operating approach, although SE is generally lower during the pre-Asian
financial crisis, the trend is less clear during the post-Asian financial crisis period.
4.2 Composition of the efficiency frontiers
118 The results in the preceding analysis highlight the sources of TE of the Korean banking
sector. Since the dominant source of total TE in the Korean banking sector seems to be
scale related, it is worth to examine further the trend in the returns to scale of the Korean
banks. As pointed out in the previous section, a bank can operate at CRS or VRS where
CRS signifies that an increase in inputs results in a proportionate increase in outputs and
VRS means a rise in inputs results in a disproportionate rise in outputs. Further, a bank
operating at VRS can be at IRS or DRS. IRS means that an increase in inputs results in a
higher increase in outputs, while DRS indicate that an increase in inputs results in lesser
output increases.
To identify the nature of returns to scale, first the CRS scores (obtained with the CCR
model) is compared with VRS (derived from the BCC model) scores. For a given bank, if
the VRS score equals to its CRS score, the bank is said to be operating at CRS. On the
other hand, if the scores are not equal, a further step is needed to establish whether
the bank is operating at IRS or DRS. To do this, the DEA model is used under the
non-increasing returns to scale (NIRS) assumption[3]. If the score under VRS equals the
NIRS score, then the bank is said to be operating at DRS. Alternatively, if the score under
VRS is different from the NIRS score, then the bank is said to be operating at IRS
(Coelli et al., 1998).
Table V shows the composition of banks that lie on the efficiency frontier under the
intermediation approach. The composition of the efficiency frontier suggests that the
number of banks that span the frontier varies between two and four banks under
the intermediation approach. It is observed from Table V that Korean French Bank and
Housing and Commercial Bank appeared to be the global leaders, i.e. have appeared the
most times on the efficiency frontier under the intermediation approach. Under the
intermediation approach, the empirical findings seem to suggest that 15 (48.4 percent)
banks have managed to appear on the frontier, while 16 other banks have never made it
to the efficiency frontier throughout the period of study.
On the other hand, the number of banks which span the efficiency frontier under the
value-added approach displays large variations ranging from one bank in the year 2002
to ten banks in the year 1999. It is observed from Table VI that three banks namely,
Housing and Commercial Bank, Cheju Bank, and Woori Bank have appeared the most
times on the efficiency frontier. Unlike the intermediation approach, the empirical
findings seem to suggest that only three (9.68 percent) banks have failed to appear on the
efficiency frontier under the value-added approach.
Under the operating approach, the composition of the efficiency frontier is relatively
more stable with small variations. It is observed from Table VII that the number of
banks which span the efficiency frontier ranged from five to nine banks. The empirical
findings seem to suggest that 22 (70.97 percent) banks have managed to reach the
efficiency frontier. Interestingly, we find that six banks have managed to appear as the
global leader banks under the operating approach, compared to two banks under
the intermediation approach and three banks under the value-added approach.

13. Bank 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Count bank
Busan Mutual Savings Bank DRS IRS IRS IRS 0
Cheju Bank IRS IRS IRS DRS IRS IRS IRS DRS IRS IRS IRS 0
Chohung Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0
Citibank Korea IRS IRS IRS IRS DRS IRS IRS DRS DRS CRS CRS CRS 3
Daegu Bank IRS IRS CRS CRS CRS CRS IRS DRS DRS DRS IRS IRS 4
Debec Mutual Savings Bank IRS IRS IRS IRS IRS IRS 0
Eutteum Mutual Savings Bank CRS IRS IRS 1
Hana Bank IRS IRS IRS IRS DRS IRS DRS DRS DRS DRS DRS DRS 0
Hanmaum Mutual Savings Bank DRS IRS IRS IRS 0
Hansol Mutual Savings Bank DRS IRS IRS IRS 0
Housing and Commercial Bank CRS CRS CRS CRS DRS NA DRS DRS CRS 5
Industrial Bank of Korea CRS CRS DRS DRS DRS CRS CRS 4
Jeil Mutual Savings Bank IRS IRS IRS DRS IRS IRS IRS 0
Jeonbuk Bank IRS IRS IRS IRS DRS IRS IRS IRS DRS IRS IRS DRS 0
Jinheung Mutual Savings Bank CRS IRS IRS IRS IRS IRS IRS IRS 1
Kangwon Bank IRS IRS IRS IRS DRS IRS IRS 0
Kookmin Bank DRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 1
Korea Exchange Bank CRS CRS IRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 2
Korea First Bank DRS DRS CRS DRS DRS NA DRS DRS DRS DRS DRS DRS 1
Korea Mutual Savings Bank IRS IRS IRS DRS IRS IRS IRS 0
Korean French Banking Corp. CRS CRS CRS CRS CRS CRS 6
Kwangju Bank IRS IRS IRS DRS IRS IRS NA DRS DRS 0
Kyongnam Bank CRS DRS IRS IRS DRS DRS DRS 1
Peace Bank of Korea Bank IRS IRS CRS 1
Pureun Mutual Savings Bank DRS IRS IRS IRS 0
Pusan Bank IRS IRS IRS IRS DRS IRS IRS NA DRS NA IRS IRS 0
Seoul Mutual Savings Bank CRS IRS IRS IRS DRS IRS IRS IRS 1
Shinhan Bank IRS IRS IRS CRS DRS CRS CRS DRS IRS 3
Shinmin Mutual Savings Bank CRS IRS IRS 1
Solomon Mutual Savings Bank IRS IRS IRS IRS IRS IRS 0
Woori Bank IRS IRS IRS IRS DRS DRS DRS DRS DRS 0
Count year 2 2 3 3 4 2 3 3 4 3 3 3 35
Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constant
returns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on the
efficiency frontier during the year; “count bank” denotes the number of times a bank has appeared on the efficiency frontier during the period of study;
banks which correspond to the shaded regions have not been efficient in any year in the sample period compared to the other banks in the sample
production frontiers –
Benchmarking
intermediation approach
Composition of
Table V.
banking sector
the Korean
119

14. BIJ
18,1
120
Table VI.
Composition of
value-added approach
production frontiers –
Bank 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Count bank
Busan Mutual Savings Bank IRS DRS IRS CRS 1
Cheju Bank CRS CRS CRS CRS CRS IRS IRS DRS IRS IRS IRS 5
Chohung Bank DRS DRS DRS DRS CRS DRS DRS CRS DRS DRS NA DRS 2
Citibank Korea IRS IRS IRS CRS CRS DRS IRS DRS DRS DRS IRS NA 2
Daegu Bank IRS DRS NA DRS DRS DRS IRS CRS DRS DRS IRS CRS 2
Debec Mutual Savings Bank CRS IRS IRS IRS IRS IRS 1
Eutteum Mutual Savings Bank IRS IRS IRS 0
Hana Bank CRS DRS DRS DRS DRS DRS NA NA DRS IRS CRS CRS 3
Hanmaum Mutual Savings Bank CRS IRS IRS IRS 1
Hansol Mutual Savings Bank CRS DRS IRS DRS 1
Housing and Commercial Bank CRS CRS CRS CRS CRS CRS DRS CRS DRS 7
Industrial Bank of Korea DRS DRS CRS DRS IRS NA CRS 2
Jeil Mutual Savings Bank DRS CRS IRS IRS DRS IRS NA 1
Jeonbuk Bank IRS IRS NA IRS DRS IRS IRS IRS DRS IRS IRS IRS 0
Jinheung Mutual Savings Bank CRS DRS CRS CRS IRS IRS IRS IRS 3
Kangwon Bank CRS IRS CRS IRS IRS DRS IRS 2
Kookmin Bank DRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 1
Korea Exchange Bank CRS CRS CRS DRS DRS DRS DRS DRS DRS DRS NA DRS 3
Korea First Bank DRS DRS CRS DRS DRS DRS IRS DRS DRS IRS IRS CRS 2
Korea Mutual Savings Bank CRS CRS CRS IRS IRS IRS IRS 3
Korean French Banking Corp. IRS CRS CRS CRS DRS IRS DRS IRS IRS IRS 3
Kwangju Bank CRS NA IRS DRS DRS IRS IRS DRS DRS 1
Kyongnam Bank DRS DRS DRS DRS CRS DRS DRS 1
Peace Bank of Korea Bank DRS IRS CRS 1
Pureun Mutual Savings Bank IRS CRS IRS IRS 1
Pusan Bank IRS DRS NA DRS DRS DRS IRS CRS DRS NA IRS CRS 2
Seoul Mutual Savings Bank CRS CRS IRS IRS IRS IRS IRS IRS 2
Shinhan Bank NA NA DRS DRS DRS DRS CRS DRS 1
Shinmin Mutual Savings Bank IRS IRS CRS 1
Solomon Mutual Savings Bank IRS IRS IRS IRS IRS IRS IRS 0
Woori Bank CRS CRS CRS CRS DRS DRS CRS DRS DRS 5
Count year 5 5 6 5 7 5 5 10 2 2 1 7 60
Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constant
returns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on the
efficiency frontier during the year; “count bank” denotes the number of times a bank has appeared on the efficiency frontier during the period of study;
banks which correspond to the shaded regions have not been efficient in any year in the sample period compared to the other banks in the sample

15. Bank 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Count bank
Busan Mutual Savings Bank CRS DRS CRS CRS 3
Cheju Bank CRS CRS CRS CRS CRS DRS DRS DRS IRS DRS DRS 5
Chohung Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0
Citibank Korea DRS CRS IRS CRS DRS DRS CRS DRS DRS DRS DRS DRS 3
Daegu Bank CRS DRS DRS DRS DRS DRS DRS CRS DRS CRS DRS DRS 3
Debec Mutual Savings Bank CRS CRS CRS CRS CRS IRS 5
Eutteum Mutual Savings Bank IRS DRS IRS 0
Hana Bank CRS DRS DRS DRS DRS DRS DRS DRS DRS NA DRS DRS 1
Hanmaum Mutual Savings Bank DRS DRS DRS IRS 0
Hansol Mutual Savings Bank DRS DRS DRS DRS 0
Housing and Commercial Bank CRS CRS CRS DRS DRS DRS DRS CRS DRS 4
Industrial Bank of Korea DRS DRS DRS DRS CRS CRS DRS 2
Jeil Mutual Savings Bank DRS CRS DRS DRS DRS IRS NA 1
Jeonbuk Bank IRS DRS CRS CRS CRS CRS CRS NA DRS IRS DRS DRS 5
Jinheung Mutual Savings Bank CRS DRS CRS DRS IRS IRS IRS CRS 3
Kangwon Bank CRS CRS CRS CRS DRS DRS DRS 4
Kookmin Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0
Korea Exchange Bank CRS DRS CRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 3
Korea First Bank CRS CRS CRS DRS DRS DRS DRS IRS DRS NA DRS DRS 3
Korea Mutual Savings Bank CRS CRS CRS CRS DRS CRS CRS 6
Korean French Banking Corp. CRS CRS CRS CRS CRS DRS DRS IRS DRS CRS 6
Kwangju Bank DRS DRS DRS DRS DRS DRS NA DRS DRS 0
Kyongnam Bank DRS DRS DRS DRS DRS DRS DRS 0
Peace Bank of Korea Bank DRS IRS DRS 0
Pureun Mutual Savings Bank CRS CRS CRS CRS 4
Pusan Bank DRS DRS DRS DRS DRS DRS DRS NA DRS IRS DRS DRS 0
Seoul Mutual Savings Bank CRS CRS CRS CRS IRS DRS DRS IRS 4
Shinhan Bank CRS CRS CRS DRS CRS CRS CRS DRS 6
Shinmin Mutual Savings Bank CRS CRS IRS IRS 2
Solomon Mutual Savings Bank DRS IRS NA DRS IRS IRS IRS 0
Woori Bank DRS CRS DRS DRS DRS DRS DRS DRS DRS 1
Count year 6 7 8 6 5 6 9 6 5 6 5 5 74
Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constant
returns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on the
efficiency frontier during the year; “count bank” denotes the number of times a bank has appeared on the efficiency frontier during the period of study;
banks which correspond to the shaded regions have not been efficient in any year in the sample period compared to the other banks in the sample
production frontiers –
Benchmarking
operating approach
Composition of
banking sector
the Korean
Table VII.
121

16. BIJ In general, the empirical findings presented in Tables V-VII clearly indicates that while
18,1 the small banks tend to operate at CRS or IRS, the large banks tend to operate at CRS or
DRS, findings which are similar to the earlier studies by among others Miller and Noulas
(1996), McAllister and McManus (1993) and Noulas et al. (1990). To recap, McAllister and
McManus (1993) suggest that while the small banks have generally exhibited IRS, the
large banks on the other hand tend to exhibit DRS, and at best CRS. As it appears, the
122 small Korean banks have experienced IRS in their operations during the period of study.
One implication is that for the small Korean banks, a proportionate increase in inputs
would result in more than a proportionate increase in outputs. Hence, the small Korean
banks, which have been operating at IRS, could achieve significant cost savings
and efficiency gains by increasing its scale of operations. In other words, substantial
gains can be obtained by altering the scale via internal growth or further consolidation in
the sector. In fact, in a perfectly competitive and contestable market, the efficient
banks should absorb the scale inefficient banks in order to exploit cost advantages.
Thus, the banks that experience IRS should either eliminate their scale inefficiency,
or be ready to become a prime target for acquiring banks, which can create value from
underperforming banks by streamlining their operations and eliminating their
redundancies and inefficiencies (Evanoff and Israelvich, 1991).
On the other hand, the results seem to suggest that further increase in size would only
result in a smaller increase of outputs for every proportionate increase in inputs for the
large banks, resulting from the fact that the large banks have been operating at DRS and
CRS. If anything could be delved from the results, decision makers ought to be more
cautious in promoting mergers, particularly among the large banks as means to enjoy
efficiency gains.
The composition of the efficiency frontier shows that under the intermediation
approach the majority of Korean banks have experienced economies of scale (operating
at IRS) ranging from 63.64 percent in 1992 to 60.87 percent in 2003. On the other hand,
the share of banks experiencing diseconomies of scale (operating at DRS) accelerates
from 18.18 percent in 1992 to 26.09 percent in 2003, suggesting the extra production costs
faced by the rapidly growing domestic banks. On the other hand, the share of scale
efficient banks (operating at CRS) decelerates from 18.18 percent in 1992 to 13.04 percent
in 2003, signaling worsening SE over time.
Under the operating approach, we find that the majority of Korean banks have
experienced diseconomies of scale (operating at DRS). During the period under study,
the empirical findings suggest that the share of banks experiencing diseconomies of
scale (operating at DRS) accelerates sharply from 36.36 percent in 1992 to 52.17 percent
in 2003. Likewise, the empirical findings seem to suggest that Korean banks which
experienced economies of scale (operating at IRS) accelerates gradually from 9.09 percent
in 1992 to 21.74 percent in 2003. On the other hand, the share of scale efficient
banks (operating at CRS) decelerates sharply from 54.55 percent in 1992 to 21.74 percent
in 2003.
Similar to the intermediation approach, the empirical findings seem to suggest that
the majority of Korean banks have experienced economies of scale (operating at IRS)
under the value-added approach, ranging from 36.36 percent in 1992 before increasing to
43.48 percent in 2003. On the other hand, the share of banks experiencing diseconomies
of scale (operating at DRS) decelerates from 45.45 percent in 1992 to 30.43 percent in

17. 2003, while the share of scale efficient banks (operating at CRS) also decelerates from Benchmarking
45.45 percent in 1992 to 30.43 percent in 2003. the Korean
Overall, the empirical findings from this study seem to suggest that in the
case of the Korean banking sector, technical inefficiency has much to do with the scale banking sector
of production rather than the inefficient utilization of resources. The dominant effect
of scale indicates that most of Korean banks have been operating at the “incorrect” or
non-optimal scale of operations. They either experience economies of scale (i.e. IRS) 123
due to being at less than the optimum size, or diseconomies of scale (i.e. DRS) due to being
at more than the optimum size. Thus, decreasing or increasing the scale of production
could result in cost savings, or efficiencies.
5. Concluding remarks
The present study examines the efficiency of the Korean banking sector during the
period 1992-2003. The DEA method is employed to three different approaches to
demonstrate how efficiency scores vary with changes in inputs and outputs. During
the period under study, Korean banks’ have exhibited a higher level of TE under the
operating approach compared to the intermediation and value-added approaches, while
banks are characterized by relatively low level of TE under the intermediation approach.
The empirical findings suggest that the inefficiency of the Korean banking sector was
largely due to scale rather than pure technical under the operating approach, while scale
inefficiency seems to outweighs pure technical inefficiency under the value-added
approach. We find that under the intermediation approach, the Korean banking sector’s
inefficiency stems largely from pure technical, rather than scale.
The empirical findings from this study present important ramifications. From the
policy-making perspectives, the empirical findings clearly demonstrate the sensitivity of
the efficiency scores derived from the DEA method to the choice of inputs and outputs.
If anything could be delved, the policy makers ought to be more cautious before making
decisions from the results derived from a single approach. The results clearly highlights
that a bank may be the most efficient under certain approach, but may not be under
another approach.
The empirical findings from this study clearly suggest that the decline in the efficiency
of the Korean banks were mainly due to scale. The results imply that banks operating in
the Korean banking sector are either too small to benefit from the economies of scale,
or too large to be scale efficient. Thus, from the policy-making perspective, the results
imply that the relatively smaller banks could raise their efficiency levels by expanding,
while the larger banks would need to scale down their operations to be scale efficient.
From the economies of scale perspectives, mergers among the small banking
institutions should be encouraged. This should entail the small banks to reap the
benefits of economies of scale. The larger institutions will also have better capacity to
invest in the state-of-the-art technologies, which could enhance the rate of TFP growth
of the Korean banking sector. Furthermore, consolidation among the small banking
institutions may also enable them to better withstand macroeconomic shocks like the
Asian financial crisis.
During the period under study, it is observed that in terms of SE, the larger banks
have lagged behind their smaller counterparts. The optimal size for a firm would be at a
point where it reaches a constant return to scale (CRS). To recap, a DMU operating
under IRS needs to expand its operations, while a DMU, which is operating at DRS would

18. BIJ on the contrary lead to downsizing. Perhaps, the reason why larger banks are
18,1 underperforming in comparison to their smaller peers could be that their size has become
more of a burden than an advantage arising from the mergers and acquisitions activities.
There are considerable costs associated with the management of a large organization
and making sure that these costs do not outweigh the size benefits is of great importance.
The findings above could be a reflection to the belief that scope economies, rather than
124 economies of scale, are often seen as the main benefit banks derive by merging.
The empirical results which suggest that the Korean banks have been operating at
a non-optimal scale of operations are in line with the findings by among others Sufian
(2007) on the Malaysian banking sector. To recap, Sufian (2007) found that during the
post-merger period the inefficiency of the Malaysian banking sector was largely due to
scale rather than pure technical. He also suggests that the mergers were particularly
successful for the small- and medium-sized banks, which have benefited the most from
expansion and via economies of scale. If anything could be delved from the results,
policy makers ought to be more cautious in promoting mergers as a mean to achieve
greater efficiency in the banking sector. However, as mentioned earlier, the findings
above could be a reflection to the belief that scope economies, rather than economies of
scale, are often viewed as the main benefit banks derive by merging, particularly within
the context of the Korean banking sector.
However, over the long term, improvements may arise arising from a more
progressive banks developing and introducing new technologies. Such innovative
banks may acquire improved status and benefit from scale operations. Although size
alone is not sufficient to guarantee efficiency, nonetheless being a large bank is an
important aspect to achieving sufficient scale to be able to invest in the identification
and development of cutting-edge technology and management systems. This certainly
applies where there has been significant progress in enhancing the network of delivery
channels, including optimizing the number of branches within the bank’s network.
Owing to its limitations, the paper could be extended in a variety of ways. First, the
scope of this study could be further extended to investigate changes in cost, allocative,
and technical efficiencies over time. Second, the non-parametric frontier analysis used in
this paper could be combined with the stochastic frontier analysis method of estimating
the frontier. This should testify to the robustness of the results against alternative
estimation methods. Finally, investigation of changes in productivity over time as a
result of technical change or technological progress or regress by employing the
MPI could yet be another extension to the current paper.
Despite these limitations, the findings of this study are expected to contribute
significantly to the existing knowledge on the operating performance of the Korean
banking sector. Nevertheless, the study have also provide further insight to bank-specific
management as well as the policymakers with regard to attaining optimal utilization of
capacities, improvement in managerial expertise, efficient allocation of scarce resources,
and most productive scale of operation of the banks in the industry. This may also
facilitate directions for sustainable competitiveness of future banking operations in Korea.
Notes
1. During the period under study, there were 17 nationwide banks namely Cho Hung Bank,
Commercial Bank of Korea (merged to form Hanvit Bank in 1999), Korea First Bank
(nationalized in 1999), Hanil Bank (merged to form Hanvit Bank in 1999), Bank of Seoul

19. (nationalized in 1998), Korea Exchange Bank, Shinhan Bank, Hanmi Bank (KorAm Bank), Benchmarking
Donghwa Bank (acquired by Housing and Commercial Bank in 1998), Daedong Bank
(acquired by Kookmin Bank in 1998), Hana Bank, Boram Bank (merged with Hana Bank in the Korean
1999), Peace Bank of Korea (merged into Woori Holding Co. in 2001), Kookmin Bank, banking sector
Housing and Commercial Bank (merged into Kookmin Bank in 2001), and Woori Holding Co.
(formerly known as Hanvit Bank prior to be renamed in 2002).
2. There were ten regional commercial banks during the period under study. The regional 125
commercial banks consist of Daegu Bank, Pusan Bank, Chung Chong Bank (acquired by
Hana Bank in 1998), Kwangju Bank, Bank of Cheju, Kyungki Bank (acquired by Hanmu Bank
in 1998), Jeonbuk Bank, Kangwon Bank (merged into Cho Hung Bank in 1999),
Kyungnam Bank, and Choongbuk Bank (merged into Cho Hung Bank in 1999).
3. Interested readers are referred to excellent books by Coelli et al. (1998), Thanassoulis (2001),
and Cooper et al. (2000) for detailed discussions on the NIRS within the DEA method.
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Corresponding author
Fadzlan Sufian can be contacted at: fsufian@gmail.com
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