6.benchmarking the

  • 69 views
Uploaded on

 

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
69
On Slideshare
0
From Embeds
0
Number of Embeds
1

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide

Transcript

  • 1. The current issue and full text archive of this journal is available at www.emeraldinsight.com/1463-5771.htm 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, MalaysiaAbstractPurpose – The purpose of this paper is to critically examine the sources of inefficiency in the Koreanbanking sector. The present study focuses on three different approaches: intermediation approach,value-added approach, and operating approach, to differentiate how efficiency scores vary with changesin inputs and outputs.Design/methodology/approach – The paper utilizes the non-parametric data envelopment analysismethodology to measure the efficiency of banks operating in the Korean banking sector. The methodallows for the decomposition of technical efficiency (TE) into its mutually exhaustive components ofpure 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 bankingsector performance will examine the efficiency changes over time by employing the parametricstochastic frontier analysis method. Investigations into productivity changes over time, as a result of atechnical change or technological progress or regression by employing the Malmquist productivityindex could yet be another extension to the paper.Practical implications – The findings from this study are essential not only for the managers of thebanks, 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 regulatoryauthorities and bank managers who formulate going forward policies for improved efficiency of theKorean 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 performancePaper type Research paper1. IntroductionThe banking sector is the backbone of the Korean economy and plays an importantfinancial intermediary role. Therefore, the health of the sector is very critical to thehealth of the economy at large. Given the relation between the well being of the bankingsector and the growth of the economy (Rajan and Zingales, 1998; Levine, 1998; Levineand Zervos, 1998; Cetorelli and Gambera, 2001; Beck and Levine, 2004), knowledge Benchmarking: An International Journal Vol. 18 No. 1, 2011The author would like to thank Angappa Gunasekaran (the Editor) and two anonymous referees pp. 107-127 q Emerald Group Publishing Limitedfor the constructive comments and suggestions, which have significantly improved the contents 1463-5771of the paper. The usual caveats apply. DOI 10.1108/14635771111109841
  • 2. BIJ of the underlying factors that influence the banking sector’s efficiency is therefore18,1 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 Benchmarkingliterature. In Section 3, we outline the approaches to the measurement of efficiency the Koreanchange. Section 4 discusses the results, and finally we conclude in Section 5. banking sector2. Review of the literaturesSince its introduction by Charnes et al. (1978) and Banker et al. (1984), researchers havewelcomed DEA as a methodology for performance evaluation (Gregoriou and Zhu, 1092005). 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, andreferences 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 isamong the earliest to employ frontier estimation technique to examine the performanceof Asian banks. The results suggest that banks of different organizational statusperform 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 mainlyconcentrated on the comparison between the foreign and domestic banks’ performance.Generally, the empirical evidence showed that foreign banks have succeeded incapitalizing on their advantages and exhibit a higher level of efficiency than theirdomestic bank peers. Leightner and Lovell (1998) find that the average domesticThai banks experienced falling total factor productivity (TFP) growth, while theaverage foreign bank experienced increasing TFP. Unite and Sullivan (2003) suggestthat the entry of foreign banks in the Philippines has resulted in the reduction of interestrate spreads and bank profits of the domestic banks that are affiliated with familybusiness 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 technicalefficiency (TE). They also suggest that the productivity of the domestic banks is moresusceptible 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 Indiahave 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 bythe 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 revenueefficiency of the Pakistan banking sector during 1981-1998. They show that financialliberalization has positive impact on banks’ performance. Subsequently, di Patti andHardy (2005) examine the cost and profit efficiency of Pakistan commercial banksduring the period 1981-2002. They find that financial liberalization leads to higher bankprofitability, but only during the first round of financial reform of 1991-1992. Despite substantial studies performed in regard to the efficiency of financial institutionsin the USA, Europe, and other Asia-Pacific banking industries, empirical evidence on theKorean banking sector is relatively scarce. By employing the Malmquist productivityindex (MPI) approach, Gilbert and Wilson (1998) focus on the impact of deregulation inthe 1980s on the productive efficiency of Korean private banks during the post-1980s topre-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 exhibits110 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 Benchmarkingoperating at an optimal scale. However, banks in practice may face either economies or the Koreandiseconomies of scale. Thus, if one makes the CRS assumption when not all DMUs areoperating at the optimal scale, the computed measures of TE will be contaminated banking sectorwith SE. Banker et al. (1984) extended the CCR model by relaxing the CRS assumption. Theresulting Banker, Charnes, and Cooper (BCC) model is used to assess the efficiency of 111DMUs characterized by variable returns to scale (VRS). The VRS assumption providesthe measurement of pure technical efficiency (PTE), which is the measurement ofTE devoid of the SE effects. If there appears to be a difference between the TE andPTE 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 ata point where the production frontier exhibits CRS. The input-oriented BCC model with VRS assumption can be represented by thefollowing 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 rThe BCC model differs from the CCR model in that it includes the so-called convexity Pconstraint, N¼1 lf ¼ 1, which prevents any interpolation point constructed from the fobserved DMUs from being scaled up or down to form a referent point. In this model, theset of l values minimize lo to l* and identify a point within the VRS assumption, of which othe input levels reflect the lowest proportion of l* . At l* , the input levels of DMU fo can be o ouniformly contracted without detriment its output levels. Therefore, DMU fo hasefficiency 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), whichgenerates TE under the CRS assumption. This implies that PTE of a DMU is alwaysgreater 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 firminvolved 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 too18,1 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 Benchmarkingfrom the total cost incurred for running the business (Leightner and Lovell, 1998). the KoreanAccordingly, 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 practicalconsiderations and to examine the robustness of the estimated efficiency scores under 113various alternatives, the present study focuses on three major approaches: intermediationapproach, operating approach, and value-added approach. Under the intermediationapproach, we assume deposits (X1), labour (X4), and capital (X2) as inputs for producingloans (Y1) and investments (Y2). Under the operating approach, two types of inputs areconsidered namely, interest expenses (X3) and labour (X4). The relevant outputs areinterest 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 period1992-2003. The variables are obtained from published balance sheet information inannual reports of each individual bank. The final sample consists of 31 banks, whichaccount for more than 80 percent of the Korean banking sector’s total assets. Table Ipresents summary statistics of the output and input variables used to construct theDEA model.4. Empirical findingsIn this section, we will discuss the TE change of the Korean banking sector, measured bythe DEA method and its decomposition into PTE and SE components. The efficiency ofthe Korean banking sector is first examined by applying the DEA method for each yearunder investigation. To allow efficiency to vary over time, the efficiency frontiers areconstructed for each year by solving the linear programming (LP) problems rather thanconstructing 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 10.50Mean 36,336.16 4,793.75 2,931.93 622.26 37,596.46 552.26 1,485.15 873.72Max. 126,005.20 29,846.61 9,616.51 2,424.91 131,068.00 1,528.13 6,148.47 2,938.84SD 28,568.93 5,320.97 2,211.16 521.67 29,328.75 402.07 1,148.98 634.82Notes: Y1 – loans (includes loans to customers and other banks); Y2 – investments (includes dealingand investment securities); Y3 – interest income; Y4 – non-interest income (defined as fee income andother 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 andother banks); X2 – capital (measured by the book value of property, plant, and equipment); X3 – interest Table I.expenses; X4 – non-interest expense Descriptive statisticsSource: Banks annual reports and authors own calculations for inputs and outputs
  • 8. BIJ 4.1 Efficiency of the Korean banking sector18,1 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 inputs114 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 13.79Table II. 2001 26 6 0.846 0.199 0.647 1.045 80.77 19.23Average TE 2002 24 5 0.858 0.118 0.740 0.976 62.50 16.67of 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 KoreanYear banks banks (M) (s ) (I ¼ M 2 s) (I ¼ M þ s) I below mean banking sectorIntermediation approach1992 11 5 0.868 0.208 0.660 1.076 90.91 9.091993 14 4 0.852 0.150 0.702 1.002 92.86 7.14 1151994 15 6 0.811 0.187 0.624 0.998 46.67 13.331995 16 8 0.921 0.088 0.833 1.009 75.00 25.001996 18 9 0.888 0.127 0.761 1.015 83.33 16.671997 23 7 0.727 0.204 0.523 0.931 56.52 13.041998 25 7 0.678 0.243 0.435 0.921 52.00 16.001999 25 7 0.785 0.188 0.597 0.973 44.00 24.002000 29 8 0.817 0.160 0.657 0.977 55.17 17.242001 26 6 0.797 0.159 0.638 0.956 57.69 15.382002 23 6 0.727 0.186 0.541 0.913 56.52 17.392003 23 6 0.727 0.182 0.545 0.909 65.22 8.70Value-added approach1992 11 7 0.955 0.078 0.877 1.033 81.82 18.181993 14 8 0.922 0.106 0.816 1.028 85.71 14.291994 16 8 0.921 0.101 0.820 1.022 81.25 18.751995 17 11 0.917 0.123 0.794 1.040 76.47 23.531996 19 12 0.947 0.085 0.862 1.032 78.95 21.051997 24 12 0.932 0.097 0.835 1.029 75.00 25.001998 25 7 0.712 0.211 0.501 0.923 60.00 8.001999 24 14 0.943 0.114 0.829 1.057 87.50 12.502000 29 9 0.798 0.197 0.601 0.995 58.62 10.342001 26 4 0.796 0.159 0.637 0.955 69.23 11.542002 23 4 0.773 0.163 0.610 0.936 65.22 17.392003 23 10 0.909 0.125 0.784 1.034 78.26 21.74Operating approach1992 11 8 0.989 0.022 0.967 1.011 81.82 18.181993 14 12 0.998 0.005 0.993 1.003 92.86 7.141994 16 11 0.981 0.033 0.948 1.014 81.25 18.751995 17 13 0.991 0.020 0.971 1.011 88.24 11.761996 19 13 0.989 0.024 0.965 1.013 89.47 10.531997 24 16 0.974 0.046 0.928 1.020 87.50 12.501998 25 12 0.925 0.104 0.821 1.029 80.00 20.001999 24 16 0.962 0.073 0.889 1.035 87.50 12.502000 29 11 0.937 0.102 0.835 1.039 89.66 10.342001 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 PTE2003 23 10 0.917 0.106 0.811 1.023 78.26 21.74 of Korean banksintermediation and value-added approaches. On the other hand, under the intermediationapproach, banks are characterized by relatively low level of TE. Illustratively, in the year1999 only three (12.5 percent) banks were found to be efficient and the average TE for allbanks stood at 71.4 percent under the intermediation approach. The number of efficientbanks during the sample period ranged from two banks in 1992 and 1993 to four banks in1996 under the intermediation approach and six banks in 1992 to nine banks in 1999under the operating approach. On the other hand, the number of efficiency banks rangedfrom 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 Percentage18,1 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 18.18116 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 10.34Table IV. 2001 26 8 0.950 0.105 0.845 1.055 88.46 11.54Average SE 2002 24 5 0.925 0.080 0.845 1.005 79.17 20.83of 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 Benchmarkingthe value-added approach. These numbers were slightly lower under the intermediation the Koreanapproach. As the TE estimates itself is time varying, these proportions do not necessarilycorroborate the degree of efficiency of the banking sector. For example, under the banking sectorintermediation approach, around 82.6 percent in 1997 and around 61.5 percent in 2001 ofbanks 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 thenumber of banks that are close to the average over time and thus merely capture thekurtosis of the efficiency distribution depending on the approach. For instance, underthe 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 lotof scores fall in the center of the distribution. On the other hand, under the operatingapproach the efficiency of the Korean banking sector seem to follow a mesokurticdistribution, i.e. the efficiency scores display a moderate peak with gradual curvessuggesting 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 ofefficiency in the Korean banking sector, particularly a year after the Asian financialcrisis stemming from the under utilization of resources (inputs). Finally, consideringthe evolution of efficiency over time, a clear temporal pattern does not emerge fromthese different approaches. It is also worth noting from Table II that although in generalTE level seems to deteriorate a year after the Asian financial crisis under all approaches,the deterioration seems to be more pronounced under the intermediation approachmodel. Table III presents the PTE estimates, while SE estimates under the three approachesare presented in Table IV. It is observed that over the sample period, both pure technicaland SE measures, especially under intermediation and value-added approaches displaysignificant variation and the Korean banking sector did not achieve sustained efficiencygains. Estimates of PTE under the intermediation approach vary from a high of92.1 percent in 1995 to a low of 67.8 percent in 1998. In most of the years, only about20-30 percent of banks were found to be pure technically efficient under theintermediation approach. Interestingly, the percentage of banks whose PTE falls withinthe interval of one standard deviation around the mean displayed a large asymmetry,particularly during the period 1997-1999 under the intermediation and value-addedapproaches. It is observed from Table III that under the intermediation approachthe percentage stood at around 56.5 percent in 1997 to 44 percent in 1999, while undervalue-added approach the figures stood at around 75 percent in 1997 to 87.5 percentin 1999. It is interesting to note that the number of efficient banks under CRS (TE) assumptionand VRS (PTE) assumption differs markedly, irrespective of the choice of various inputsand outputs. The findings clearly demonstrate low degree of SE among Koreancommercial banks. Under the intermediation approach for example, Table III revealsthat eight banks were found to be efficient under VRS in 2000, whereas only four bankswere found to be efficient under CRS. This indicates that the remaining four banks failedto reach the CRS frontier owing solely to scale. It is observed from Tables III and IV that under the operating approach, SE seems tooutweigh 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 suggest18,1 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 frontiers118 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 bankBusan Mutual Savings Bank DRS IRS IRS IRS 0Cheju Bank IRS IRS IRS DRS IRS IRS IRS DRS IRS IRS IRS 0Chohung Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0Citibank Korea IRS IRS IRS IRS DRS IRS IRS DRS DRS CRS CRS CRS 3Daegu Bank IRS IRS CRS CRS CRS CRS IRS DRS DRS DRS IRS IRS 4Debec Mutual Savings Bank IRS IRS IRS IRS IRS IRS 0Eutteum Mutual Savings Bank CRS IRS IRS 1Hana Bank IRS IRS IRS IRS DRS IRS DRS DRS DRS DRS DRS DRS 0Hanmaum Mutual Savings Bank DRS IRS IRS IRS 0Hansol Mutual Savings Bank DRS IRS IRS IRS 0Housing and Commercial Bank CRS CRS CRS CRS DRS NA DRS DRS CRS 5Industrial Bank of Korea CRS CRS DRS DRS DRS CRS CRS 4Jeil Mutual Savings Bank IRS IRS IRS DRS IRS IRS IRS 0Jeonbuk Bank IRS IRS IRS IRS DRS IRS IRS IRS DRS IRS IRS DRS 0Jinheung Mutual Savings Bank CRS IRS IRS IRS IRS IRS IRS IRS 1Kangwon Bank IRS IRS IRS IRS DRS IRS IRS 0Kookmin Bank DRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 1Korea Exchange Bank CRS CRS IRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 2Korea First Bank DRS DRS CRS DRS DRS NA DRS DRS DRS DRS DRS DRS 1Korea Mutual Savings Bank IRS IRS IRS DRS IRS IRS IRS 0Korean French Banking Corp. CRS CRS CRS CRS CRS CRS 6Kwangju Bank IRS IRS IRS DRS IRS IRS NA DRS DRS 0Kyongnam Bank CRS DRS IRS IRS DRS DRS DRS 1Peace Bank of Korea Bank IRS IRS CRS 1Pureun Mutual Savings Bank DRS IRS IRS IRS 0Pusan Bank IRS IRS IRS IRS DRS IRS IRS NA DRS NA IRS IRS 0Seoul Mutual Savings Bank CRS IRS IRS IRS DRS IRS IRS IRS 1Shinhan Bank IRS IRS IRS CRS DRS CRS CRS DRS IRS 3Shinmin Mutual Savings Bank CRS IRS IRS 1Solomon Mutual Savings Bank IRS IRS IRS IRS IRS IRS 0Woori Bank IRS IRS IRS IRS DRS DRS DRS DRS DRS 0Count year 2 2 3 3 4 2 3 3 4 3 3 3 35Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constantreturns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on theefficiency 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 bankBusan Mutual Savings Bank IRS DRS IRS CRS 1Cheju Bank CRS CRS CRS CRS CRS IRS IRS DRS IRS IRS IRS 5Chohung Bank DRS DRS DRS DRS CRS DRS DRS CRS DRS DRS NA DRS 2Citibank Korea IRS IRS IRS CRS CRS DRS IRS DRS DRS DRS IRS NA 2Daegu Bank IRS DRS NA DRS DRS DRS IRS CRS DRS DRS IRS CRS 2Debec Mutual Savings Bank CRS IRS IRS IRS IRS IRS 1Eutteum Mutual Savings Bank IRS IRS IRS 0Hana Bank CRS DRS DRS DRS DRS DRS NA NA DRS IRS CRS CRS 3Hanmaum Mutual Savings Bank CRS IRS IRS IRS 1Hansol Mutual Savings Bank CRS DRS IRS DRS 1Housing and Commercial Bank CRS CRS CRS CRS CRS CRS DRS CRS DRS 7Industrial Bank of Korea DRS DRS CRS DRS IRS NA CRS 2Jeil Mutual Savings Bank DRS CRS IRS IRS DRS IRS NA 1Jeonbuk Bank IRS IRS NA IRS DRS IRS IRS IRS DRS IRS IRS IRS 0Jinheung Mutual Savings Bank CRS DRS CRS CRS IRS IRS IRS IRS 3Kangwon Bank CRS IRS CRS IRS IRS DRS IRS 2Kookmin Bank DRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 1Korea Exchange Bank CRS CRS CRS DRS DRS DRS DRS DRS DRS DRS NA DRS 3Korea First Bank DRS DRS CRS DRS DRS DRS IRS DRS DRS IRS IRS CRS 2Korea Mutual Savings Bank CRS CRS CRS IRS IRS IRS IRS 3Korean French Banking Corp. IRS CRS CRS CRS DRS IRS DRS IRS IRS IRS 3Kwangju Bank CRS NA IRS DRS DRS IRS IRS DRS DRS 1Kyongnam Bank DRS DRS DRS DRS CRS DRS DRS 1Peace Bank of Korea Bank DRS IRS CRS 1Pureun Mutual Savings Bank IRS CRS IRS IRS 1Pusan Bank IRS DRS NA DRS DRS DRS IRS CRS DRS NA IRS CRS 2Seoul Mutual Savings Bank CRS CRS IRS IRS IRS IRS IRS IRS 2Shinhan Bank NA NA DRS DRS DRS DRS CRS DRS 1Shinmin Mutual Savings Bank IRS IRS CRS 1Solomon Mutual Savings Bank IRS IRS IRS IRS IRS IRS IRS 0Woori Bank CRS CRS CRS CRS DRS DRS CRS DRS DRS 5Count year 5 5 6 5 7 5 5 10 2 2 1 7 60Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constantreturns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on theefficiency 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 bankBusan Mutual Savings Bank CRS DRS CRS CRS 3Cheju Bank CRS CRS CRS CRS CRS DRS DRS DRS IRS DRS DRS 5Chohung Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0Citibank Korea DRS CRS IRS CRS DRS DRS CRS DRS DRS DRS DRS DRS 3Daegu Bank CRS DRS DRS DRS DRS DRS DRS CRS DRS CRS DRS DRS 3Debec Mutual Savings Bank CRS CRS CRS CRS CRS IRS 5Eutteum Mutual Savings Bank IRS DRS IRS 0Hana Bank CRS DRS DRS DRS DRS DRS DRS DRS DRS NA DRS DRS 1Hanmaum Mutual Savings Bank DRS DRS DRS IRS 0Hansol Mutual Savings Bank DRS DRS DRS DRS 0Housing and Commercial Bank CRS CRS CRS DRS DRS DRS DRS CRS DRS 4Industrial Bank of Korea DRS DRS DRS DRS CRS CRS DRS 2Jeil Mutual Savings Bank DRS CRS DRS DRS DRS IRS NA 1Jeonbuk Bank IRS DRS CRS CRS CRS CRS CRS NA DRS IRS DRS DRS 5Jinheung Mutual Savings Bank CRS DRS CRS DRS IRS IRS IRS CRS 3Kangwon Bank CRS CRS CRS CRS DRS DRS DRS 4Kookmin Bank DRS DRS DRS DRS DRS DRS DRS DRS DRS DRS 0Korea Exchange Bank CRS DRS CRS DRS DRS DRS DRS DRS DRS CRS DRS DRS 3Korea First Bank CRS CRS CRS DRS DRS DRS DRS IRS DRS NA DRS DRS 3Korea Mutual Savings Bank CRS CRS CRS CRS DRS CRS CRS 6Korean French Banking Corp. CRS CRS CRS CRS CRS DRS DRS IRS DRS CRS 6Kwangju Bank DRS DRS DRS DRS DRS DRS NA DRS DRS 0Kyongnam Bank DRS DRS DRS DRS DRS DRS DRS 0Peace Bank of Korea Bank DRS IRS DRS 0Pureun Mutual Savings Bank CRS CRS CRS CRS 4Pusan Bank DRS DRS DRS DRS DRS DRS DRS NA DRS IRS DRS DRS 0Seoul Mutual Savings Bank CRS CRS CRS CRS IRS DRS DRS IRS 4Shinhan Bank CRS CRS CRS DRS CRS CRS CRS DRS 6Shinmin Mutual Savings Bank CRS CRS IRS IRS 2Solomon Mutual Savings Bank DRS IRS NA DRS IRS IRS IRS 0Woori Bank DRS CRS DRS DRS DRS DRS DRS DRS DRS 1Count year 6 7 8 6 5 6 9 6 5 6 5 5 74Notes: The table shows the evolution of returns to scale in the Korean banking sector during the period 1992-2003; CRS, DRS, and IRS denote constantreturns to scale, decreasing returns to scale, and increasing returns to scale, respectively; “count year” denotes the number of banks appearing on theefficiency 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 while18,1 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, the122 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 Benchmarking45.45 percent in 1992 to 30.43 percent in 2003. the Korean Overall, the empirical findings from this study seem to suggest that in thecase of the Korean banking sector, technical inefficiency has much to do with the scale banking sectorof production rather than the inefficient utilization of resources. The dominant effectof scale indicates that most of Korean banks have been operating at the “incorrect” ornon-optimal scale of operations. They either experience economies of scale (i.e. IRS) 123due to being at less than the optimum size, or diseconomies of scale (i.e. DRS) due to beingat more than the optimum size. Thus, decreasing or increasing the scale of productioncould result in cost savings, or efficiencies.5. Concluding remarksThe present study examines the efficiency of the Korean banking sector during theperiod 1992-2003. The DEA method is employed to three different approaches todemonstrate how efficiency scores vary with changes in inputs and outputs. Duringthe period under study, Korean banks’ have exhibited a higher level of TE under theoperating approach compared to the intermediation and value-added approaches, whilebanks are characterized by relatively low level of TE under the intermediation approach.The empirical findings suggest that the inefficiency of the Korean banking sector waslargely due to scale rather than pure technical under the operating approach, while scaleinefficiency seems to outweighs pure technical inefficiency under the value-addedapproach. We find that under the intermediation approach, the Korean banking sector’sinefficiency stems largely from pure technical, rather than scale. The empirical findings from this study present important ramifications. From thepolicy-making perspectives, the empirical findings clearly demonstrate the sensitivity ofthe 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 makingdecisions from the results derived from a single approach. The results clearly highlightsthat a bank may be the most efficient under certain approach, but may not be underanother approach. The empirical findings from this study clearly suggest that the decline in the efficiencyof the Korean banks were mainly due to scale. The results imply that banks operating inthe 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 resultsimply 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 bankinginstitutions should be encouraged. This should entail the small banks to reap thebenefits of economies of scale. The larger institutions will also have better capacity toinvest in the state-of-the-art technologies, which could enhance the rate of TFP growthof the Korean banking sector. Furthermore, consolidation among the small bankinginstitutions may also enable them to better withstand macroeconomic shocks like theAsian financial crisis. During the period under study, it is observed that in terms of SE, the larger bankshave lagged behind their smaller counterparts. The optimal size for a firm would be at apoint where it reaches a constant return to scale (CRS). To recap, a DMU operatingunder 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 are18,1 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 than124 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.ReferencesAn, J., Bae, S.K. and Ratti, R.A. (2007), “Political influence and the banking sector: evidence from Korea”, Oxford Bulletin of Economics and Statistics, Vol. 69 No. 1, pp. 75-98.Banker, R.D., Charnes, A. and Cooper, W.W. (1984), “Some models for estimating technical and scale inefficiencies in data envelopment analysis”, Management Science, Vol. 30 No. 9, pp. 1078-92.Beck, T. and Levine, R. (2004), “Stock markets, banks, and growth: panel evidence”, Journal of Banking & Finance, Vol. 28 No. 3, pp. 423-42.Benston, G.J. (1965), “Branch banking and economies of scale”, Journal of Finance, Vol. 20 No. 2, pp. 312-31.Berger, A.N. (2007), “International comparisons of banking efficiency”, Financial Markets, Institutions and Instruments, Vol. 16 No. 3, pp. 119-44.Berger, A.N. and Humphrey, D.B. (1997), “Efficiency of financial institutions: international survey and directions for future research”, European Journal of Operational Research, Vol. 98 No. 2, pp. 175-212.Berger, A.N., Hunter, W.C. and Timme, S.G. (1993), “The efficiency of financial institutions: a review and preview of research past, present and future”, Journal of Banking and Finance, Vol. 17 Nos 2/3, pp. 221-49.Canhoto, A. and Dermine, J. (2003), “A note on banking efficiency in Portugal, new vs. old banks”, Journal of Banking and Finance, Vol. 27 No. 11, pp. 2087-98.Cetorelli, N. and Gambera, M. (2001), “Banking market structure, financial dependence and growth: international evidence from industry data”, Journal of Finance, Vol. 56 No. 2, pp. 617-48.Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision making units”, European Journal of Operational Research, Vol. 2 No. 6, pp. 429-44.Choe, H. and Lee, B.S. (2003), “Korean bank governance reform after the Asian financial crisis”, Pacific-Basin Finance Journal, Vol. 11 No. 4, pp. 483-508.Choi, S. and Hasan, I. (2005), “Ownership, governance, and bank performance: Korean experience”, Financial Markets, Institutions and Instruments, Vol. 14 No. 4, pp. 245-52.Coelli, T. (1996), “A guide to DEAP version 2.1”, CEPA Working Paper 8/96, University of New England, Armidale.Coelli, T., Prasada-Rao, D.S. and Battese, G.E. (1998), An Introduction to Efficiency and Productivity Analysis, Kluwer Academic Publishers, Boston, MA.
  • 20. BIJ Cooper, W.W., Seiford, L.M. and Tone, K. (2000), Data Envelopment Analysis, Kluwer Academic Publishers, Boston, MA.18,1 di Patti, E.B. and Hardy, D.C. (2005), “Financial sector liberalization, bank privatization, and efficiency: evidence from Pakistan”, Journal of Banking and Finance, Vol. 29 Nos 8/9, pp. 2381-406. Drake, L. (2001), “Efficiency in UK banking”, Applied Financial Economics, Vol. 11, pp. 557-71.126 Drake, L., Hall, M.J.B. and Simper, R. (2006), “The impact of macroeconomic and regulatory factors on bank efficiency: a non-parametric analysis of Hong Kong’s banking system”, Journal of Banking and Finance, Vol. 30 No. 5, pp. 1443-66. Evanoff, D.D. and Israelvich, P.R. (1991), “Productive efficiency in banking”, Economic Perspectives, Federal Reserve Bank of Chicago, July/August, pp. 11-32. Fiordelisi, F. (2007), “Shareholder value efficiency in European banking”, Journal of Banking and Finance, Vol. 31 No. 7, pp. 2151-71. Fukuyama, H. (1993), “Technical and scale efficiency of Japanese commercial banks: a non-parametric approach”, Applied Economics, Vol. 25 No. 8, pp. 1101-12. Gilbert, R.A. and Wilson, P.W. (1998), “Effects of regulation on productivity of Korean banks”, Journal of Economics and Business, Vol. 50 No. 2, pp. 133-55. Gregoriou, G.N. and Zhu, J. (2005), Evaluating Hedge Funds and CTA Performance: Data Envelopment Analysis Approach, Wiley, New York, NY. Hao, J., Hunter, W.C. and Yang, W. (2001), “Deregulation and efficiency: the case of private Korean banks”, Journal of Economics and Business, Vol. 53 Nos 2/3, pp. 237-54. Hardy, D.C. and di Patti, E.B. (2001), “Bank reform and bank efficiency in Pakistan”, working paper, International Monetary Fund (IMF), Washington, DC. Iimi, A. (2004), “Banking sector reforms in Pakistan: economies of scale and scope, and cost complementarities”, Journal of Asian Economics, Vol. 15 No. 3, pp. 507-27. Jeon, Y., Miller, S.M. and Natke, P.A. (2006), “Do foreign bank operations provide a stabilizing influence in Korea”, The Quarterly Review of Economics and Finance, Vol. 46 No. 1, pp. 82-109. Leightner, J.E. and Lovell, C.A.K. (1998), “The impact of financial liberalization on the performance of Thai banks”, Journal of Economics and Business, Vol. 50 No. 2, pp. 115-31. Levine, R. (1998), “The legal environment, banks, and long run economic growth”, Journal of Money, Credit and Banking, Vol. 30 No. 3, pp. 596-613. Levine, R. and Zervos, S. (1998), “Stock markets, banks, and economic growth”, American Economic Review, Vol. 88 No. 3, pp. 537-58. McAllister, P.H. and McManus, D.A. (1993), “Resolving the scale efficiencies puzzle in banking”, Journal of Banking and Finance, Vol. 17 Nos 2/3, pp. 389-405. Matthews, K. and Ismail, M. (2006), “Efficiency and productivity growth of domestic and foreign commercial banks in Malaysia”, Cardiff Working Paper E2006/2. Miller, S.M. and Noulas, A.G. (1996), “The technical efficiency of large bank production”, Journal of Banking and Finance, Vol. 20 No. 3, pp. 495-509. Noulas, A.G., Ray, S.C. and Miller, S.M. (1990), “Returns to scale and input substitution for large US banks”, Journal of Money, Credit and Banking, Vol. 22 No. 1, pp. 94-108. Park, K.H. and Weber, W.L. (2006), “A note on efficiency and productivity growth in the Korean banking industry, 1992-2002”, Journal of Banking and Finance, Vol. 30 No. 8, pp. 2371-86.
  • 21. Pasiouras, F. (2008), “Estimating the technical and scale efficiency of Greek commercial banks: Benchmarking the impact of credit risk, off-balance sheet activities, and international operations”, Research in International Business and Finance, Vol. 22 No. 3, pp. 301-18. the KoreanRajan, R.G. and Zingales, L. (1998), “Financial dependence and growth”, American Economic banking sector Review, Vol. 88 No. 3, pp. 559-86.Sathye, M. (2001), “X-efficiency in Australian banking: an empirical investigation”, Journal of Banking and Finance, Vol. 25 No. 3, pp. 613-30. 127Sathye, M. (2003), “Efficiency of banks in a developing economy: the case of India”, European Journal of Operational Research, Vol. 148 No. 3, pp. 662-71.Sealey, C. and Lindley, J.T. (1977), “Inputs, outputs and a theory of production and cost at depository financial institutions”, Journal of Finance, Vol. 32, pp. 1251-66.Shanmugam, K.R. and Das, A. (2004), “Efficiency of Indian commercial banks during the reform period”, Applied Financial Economics, Vol. 14 No. 9, pp. 681-6.Siriopoulos, C. and Tziogkidis, P. (2010), “How do Greek banking institutions react after significant events? – A DEA approach”, Omega, Vol. 38 No. 10, pp. 294-308.Sturm, J.E. and Williams, B. (2008), “Characteristics determining the efficiency of foreign banks in Australia”, Journal of Banking and Finance, Vol. 32 No. 11, pp. 2346-60.Sufian, F. (2007), “Mergers and acquisitons in the Malaysian banking industry: techncial and scale efficiency effects”, International Journal of Financial Services Management, Vol. 2 No. 4, pp. 304-26.Thanassoulis, E. (2001), Introduction to the Theory and Application of Data Envelopment Analysis: A Foundation Text with Integrated Software, Kluwer Academic Publishers, Boston, MA.Unite, A.A. and Sullivan, M.J. (2003), “The effect of foreign entry and ownership on the Philippine domestic banking market”, Journal of Banking and Finance, Vol. 27 No. 12, pp. 2323-45.Webb, R.W. (2003), “Levels of efficiency in UK retail banks: a DEA window analysis”, International Journal of the Economics of Business, Vol. 10 No. 3, pp. 305-22.Corresponding authorFadzlan Sufian can be contacted at: fsufian@gmail.comTo purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints