Does Bank Performance Benefit from Non-traditional Activities? A Case of Non-interest Incomes in Taiwan Commercial Banks

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1 Special Section on Finance Does Bank Performance Benefit from Non-traditional Activities? A Case of Non-interest Incomes in Taiwan Commercial Banks LI-WEI HUANG 1 AND YI-KAI CHEN 2,* 1 Institute of Economics and Management, National University of Kaohsiung, Taiwan 2 Department of Finance, National University of Kaohsiung, Taiwan ABSTRACT Non-interest incomes havebecome an increasingly important part of banks operating incomes. Most banks regard non-interest incomes as one of the stable sources of bank revenues. In general, the industry believes increasing the ratio of non-interest incomes to operating incomes can not only improve profitability but also reduce the risk to the bank. However, DeYoung and Roland (2001) have stated increasing fee-based activities increases the volatility of bank revenues and earnings. Stiroh (2004) and DeYoung and Rice (2004) showed an increased reliance on non-interest incomes did not reduce the risk level of the bank. The objective of this study is to investigate whether the reliance on different sources of non-interest incomes will affect bank efficiency. We employ the data envelopment approach (DEA) to calculate the cost efficiency of Taiwan domestic commercial banks from 1992 to The findings are that the banks either with relatively higher or lower ratios of non-interest incomes to operating incomes perform more cost-efficiently during the examination periods. The relative optimal level of non-interest incomes exists in the Taiwan banking industry. Key words: bank efficiency, data envelopment analysis (DEA), non-interest incomes, commercial bank. 1. INTRODUCTION Banks are the financial intermediaries providing the platform to connect the capital demanders and suppliers. In 1991, the deregulation of the Taiwan banking industry allowed sixteen newly chartered banks to enter the banking sector. Sixteen additional new commercial banks made the competition more severe in the Taiwan banking industry (Wu, 1994). Since the banking industry has become more competitive and less profitable, not only new but also old commercial banks must pay attention in improving the efficiency and quality of their financial services. However, the incomes from traditional activities, which are interest incomes, were squeezed because of declining interest rates. Figure 1 and Figure 2 show the trend of the weighted average of interest rates of deposits and loans and interest rate spreads of domestic commercial banks is declining. In order to survive the competition, banks might need to increase the sources of incomes to maintain profitability. Since interest incomes and non-interest incomes are two sources of banks operating incomes, banks might rely les on the source from interest incomes and depend more on the source from non-interest incomes. Because of low interest rates and spreads, the bankers may consider * Corresponding author. chen@nuk.edu.tw 359

2 non-traditional financial activities as the niche in which banks can remain profitable and competitive. Weighted Average Interest Rates Average Deposit Rates Year Average Loan Rates Figure 1. Weighted average interest rates of Taiwan domestic commercial banks from 1992 to Rate (%) Year Figure 2. Interest rate spreads of Taiwan domestic commercial banks from 1992 to There are three paths making non-traditional activities grow. They are: improvements in technology, innovation of financial products, and deregulation. Since the 1980s, because of the three above paths, incomes from the non-traditional activities, which are classified as non-interest incomes, have played a more important role in operating incomes. Banks rely on not only the incomes from the traditional lending, but also on various fees from non-traditional activities. The ratio of non-interest incomes to operating incomes has increased over the years. Feldman and Schmidt (1999) showed the ratios of non-interest incomes to operating incomes in large and small banks were 27% and 15% respectively in 360

3 This ratio increased to 46% and 28% respectively in The non-interest income ratios of the Taiwan banking industry rose from 16% to 27% from 1993 to Although getting involved in non-traditional activities increases and diversifies the sources of incomes, nontraditional activities might lead banks into more risks and inputs. Shen (2005) argued the Taiwan banking system is a combination of the US separated and German universal banking systems. The outputs per inputs of the banks might change in terms of the non-traditional banking activities. Thus, the operating efficiency of the bank might be affected by getting involved in those non-traditional activities. Over the past decades, the measurement of bank efficiency has been well documented. Based on the data characteristics, we use the data envelopment analysis (DEA) model to measure the bank efficiency in this study. The objectives of this study are to find whether bank efficiency would be affected by the non-interest ratio and by different sources of non-interest incomes. The major contribution of this study is to elucidate whether the trend in the growing non-interest income ratio will make the bank operate more efficiently to survive in the banking industry. This study is divided into five main sections. Section 2 reviews the literature related to the issue. The research methodologies and the chosen variables are outlined in Section 3. Section 4 analyzes and discusses the empirical results. The last section concludes with the empirical results and the contributions of this study. 2. LITERATURE REVIEW 2.1 The Investigations about Non-Interest Income Although there is the phenomenon of growth in the non-interest income ratio, the effects of non-interest incomes on bank efficiency in the empirical studies are not conclusive. Most bankers believe increasing the non-interest income ratio will help to improve banks profitability and eficiency. From the supporting points of view, DeYoung (1994) and Rogers (1998) showed large amounts of fee-based or non-traditional products or services improved bank efficiency in the1980 s and 1990 s.not only commercial banks but also bank holding companies (BHC) have had similar results. By evaluating the revenue generated by payment services, Radecki (1999) noted the importance of payment revenues to the banking industry, especially to the BHCs, which also generated about 7% more revenues for the companies. But there are some investigations which hold opposing viewpoints. DeYoung and Rice (2004) showed non-interest incomes were generated from traditional and non-traditional activities. They found the non-interest income increases banks 1 Non-interest income ratio represents the ratio of non-interest incomes to operating incomes thereafter. 2 Although non-interest incomes play an increasingly important role in operating incomes of banks, the interest income is still the major source of operating incomes. 361

4 profitability, but also found a higher risk associated with the increase in earning volatility. The risk and earning volatility are considered from the non-interest income in non-traditional activities (DeYoung & Roland, 2001; Stiroh, 2003, 2004, 2006; Smith, Staikouras, & Wood, 2003). Shen (2005) employed a panel smooth threshold model to examine the scale economies of a partial universal banking system, which are a mix of interest incomes and fee revenues. Under the situation of overbanking in Taiwan, the scale economies did not exist but the optimal fixed asset size is of around 10 billion NT dollars. In terms of the diversification, banks should not focus highly on an income source. Although interest incomes from traditional activities, such as making different loans, are the major generator of revenues, diversifying income sources from traditional activities to non-traditional activities might be a good strategy for banks. Yet, banks should implement this strategy with caution. Placing emphasis on nontraditional activities and giving up traditional activities might not be a sound strategy. DeYoung and Rice (2004) indicated some non-traditional activities of banks which were related to traditional activities. Banks can not improve profitability by giving up traditional activities and increasing non-traditional activities. Stiroh (2004, 2006) and Stiroh and Rumble (2006) investigated bank performance in terms of the diversification of different income sources. They found diversifying incomes in non-traditional activities improve bank performance. However, risk adjusted performance was not the case. The results also supported the observation that non-interest incomes generated from involving non-traditional activities are profitable but risky. Therefore, this study employs bank efficiency as a measurement of bank performance to evaluate whether banks improve efficiency by becoming involved in more non-traditional activities. 2.2 The Empirical Measurement of Financial Institution Efficiency Previous documented researches related to the performance of financial institutions have focused on the frontier efficiency. The efficiency is a relative performance removing the effects of exogenous factors. This permits the researchers to focus on quantified measures of costs, inputs, outputs, revenues, profits, etc. to compute efficiency compared to the best practice institutions. In the past decades, four kinds of frontier approaches have been developed to evaluate the efficiency of organizations. They can be categorized into two groups based on assumptions of the distribution of the data. They are the parametric approach and the non-parametric approach. The parametric approach includes the stochastic frontier approach (SFA) (Ferrier & Lovell, 1990; Berger & DeYoung, 1997; Berger & Mester, 1998), the thick frontier approach (TFA) (Berger & Humphrey, 1991; Humphrey & Pulley, 1997; Bauer, Berger, Ferrier, & Humphrey, 1998), and the distribution-free approach (DFA) (Berger, 1993; Berger, Humphrey, & Hancock, 1993; Berger & Mester, 1998; Bauer et al., 1998). These approaches are different in their primary assumptions about how to disentangle inefficiency from random errors and the distribution of the methods. 362

5 Data envelopment analysis (DEA) is a non-parametric linear programming approach. 3 DEA is often used as a quantitative technique to measure the relative efficiency of organizations in the same industry. In essence DEA, which was developed by Charnes, Cooper, and Rhodes (1978), combines all input and output information of the company into a single measure of productive efficiency that lies on zero and unity. DEA is an application of linear programming that can be used to measure the relative efficiency of organizations with the same goals and objectives. Ferrier and Lovell (1990), Bauer et al. (1998), and Allen and Rai (1996) showed DEA frontier enveloped the data more closely than the parametric approaches and the magnitude of inefficiency reported by DEA was lower. Based on the characteristics of the sample size of this study, the approach without the assumption of the data distribution is preferred. 4 Thus, in this study, DEA is used to estimate efficiency as the measurement of bank performance. 3. DATA AND METHODOLOGIES 3.1 Mathematical Foundation of Data Envelopment Analysis DEA extends the idea of the Pareto Optimum in Economics to evaluate the efficiency of a set of peer entities, called decision making units (DMUs). It deals with the cases of multiple outputs and inputs. The original idea of DEA is from Ferrell (1957), which confined cases of single-output. Charnes et al. (1978) developed the dual pair of linear programming problems to solve multiple-output cases. 5 The CCR model assumes the DMUs are a constant return to scale (CRS). Yet, in fact, the return is not always a constant return to scale in the real world. To improve the disadvantage of the CCR model, Banker, Charnes, and Cooper (1984) developed the BCC model, which considers efficiency as a variation return to scale (VRS) and thus the BCC model is considered to be a better fit to realistic circumstances. 6 DEA computes a maximal performance measure for each DMU relative to all other DMUs. The only constraint is that every DMU lies on the efficiency frontier or is enveloped within the frontier. Besides this restriction, it evaluates the efficiency without explicitly formulated assumptions. The DMUs on the frontier are the best practice entities and retain a value of unity. Those enveloped by the frontier are scaled against a convex combination of the DMUs on the frontier facet closest to it and have values somewhere between zero and unity. A demonstration of the efficiency estimation by DEA is shown on Figure 3. In this study, we use a BCC input-oriented DEA model to evaluate the 3 Refer Aly, Grabowski,Pasurka, and Rangan (1990), Chen and Yeh (2000), Jemric and Vujcic (2002), Barr, Killgo, Siems, & Zimmel (2003), Leong, Dollery, & Coelli (2003), and Li (2005). 4 The sample size for each year in this study ranges from 29 to 32 commercial banks. 5 The approaches developed in Charnes et al. (1978) is called the CCR model. 6 The estimation of the efficiency used in Banker et al. (1984) is call the BCC model. 363

6 efficiency of the k-th DMU by solving the following linear program: 7 minθ k s. t. x x 0, r=1, 2,,n i ji k r 0 i ri y y, j=1, 2,, m j0 1, i=1, 2,, h i i 0, (1) i where x ri is the r-th input value of the i-th DMU, y ji is the j-th output value of the i-th DMU,λ i means a nonnegative scalar, andθ k represents the pure technical efficiency value. Y C D A T W B O o E Figure 3. Demonstration of data envelopment analysis (DEA) to estimate bank cost efficiency. X 3.2 Data, Sample and Definition of Input and Output Variables The samples used in this study are commercial banks listed on the Taiwan Stock Exchange Corporation (TSEC) from All input and output variables of these banks are obtained from the Taiwan Economic Journal Corporation (TEJ). In service industries, especial the banking industry, the output and output variables are more difficult to define properly. Three approaches, the production approach, the intermediation approach, and the asset approach, are developed to define the input-output relationship of financial intermediaries. 7 BCC input-oriented model measures the inefficiency between actual input and less input used with same output level. In other words, the efficiency estimated by BCC-input oriented model can be regarded as the cost efficiency of the bank. The efficiency of the most cost saving bank is 1. 8 Since investment banks have different characteristics of products and services, investment banks are excluded from the samples. 364

7 Sherman and Gold (1985) argued the production approach viewed banks as the producer of a product as opposed to offering a service. In general, banks treat loans, deposits, and other quantities of the financial services and serving accounts as the outputs, and treat the capital and labor as inputs. Berger and Humphrey (1991) and Li (2005) indicated the intermediation approach considered banks as an intermediary to supply financial services and examined the bank s ability to take deposits and supply loans to possible profitable borrowers. The loans and investment portfolios are viewed as the outputs. Costs of labor, costs of capital, interest expenses, and fixed assets are considered as the inputs of the bank. The asset approach used by Favero and Papi (1995) recognized the primary role of banks as a creator of loans. In essence, the idea of the asset approach is a variant of the intermediation approach, but the created financial services of banks could not be viewed as the outputs. Besides, whether deposits are treated as an input or an output depend on the objectives of the investigation. In this study, we regard banks as an intermediary to provide services to customers. The costs of providing various services are regarded as the inputs and the outcomes of the services are the outputs. In this study, we focus on the incomes from non-traditional activities of commercial banks. The profitability is used as a proxy measurement of the service outcome. The cost efficiency of banks with various costs of services and profitability of difference sources of incomes are estimated as the measurement of bank performance. Therefore, we employ the intermediation approach as the criteria of input and output variables. The input variables include two items: financial activity costs and operating costs. The two output variables are the two major sources of bank incomes, which are the interest income and the non-interest income. The descriptions of the selected input and output variables used in this study are shown in Table 1. Summarized statistics of the inputs and outputs used in the analysis are reported in Table 2. Based on the selected variables, the BCC input-oriented DEA model is used to evaluate the cost efficiency of commercial banks in Taiwan following the investigation of Barr et al. (2002) and Jemric and Vujcic (2002). The estimated values are the cost efficiency of banks, which are regarded as the proxy of bank performance in this study. 3.3 Nonparametric Test Because of the characteristics of the sample size, we use the nonparametric test to examine the hypotheses in this study. 9 The observations are equally divided into three sub-sample groups based on the percentage of the interest or non-interest incomes to the operating incomes. Three sub-sample groups are categorized as smallest, middle, and largest groups. 9 The sample size within the examination periods ranges from 29 to 32 banks. Furthermore, DEA, the nonparametric estimation of the efficiency, is used to estimate bank efficiency in this study. Thus, the nonparametric tests are used to test the hypotheses in this study. 365

8 There are four major sources of non-interest incomes. They are incomes from fiduciary activities, trading revenues, fee incomes, and non-fee incomes. The definitions of these incomes are shown in Table 3. However, most sample banks within the examination periods had very little incomes from fiduciary activities. Ignoring fiduciary incomes, we take advantage of trading revenues, fee incomes, and non-fee incomes as filtering criteria to test whether the percentages of these three main sources from non-interest incomes influence bank efficiency. Table 1. Definitions of Input and Output Variables Input / Output Variables Input Variables Operating Costs Financial Activities Costs Output Variables Interest Incomes Non-interest Incomes Definition promotion costs, managerial cost, R&D expenses, staff training expenses, expenses about security brokerage, and financial consultant expenses interest expense, losses on short-term investment, losses on long-term equity investment, losses on disposal of investment, losses of investment, reserves for loan losses, commission expenses, foreign exchange losses, security brokerage expenses, expenses of credit card activities, inventory evaluation losses, and other loss subtracting operating losses total incomes from interest related activities total incomes of bank besides interest incomes Table 2. Summarized Statistics of Input and Output Variables Input / Output Variables Mean Standard Error Minimum Maximum Input Variables Operating Cost Financial Activities Cost Output Variables Interest Income Non-interest Income Note: All financial variables are measured by NT$ thousands. Table 3. Definitions of Non-interest Income Items Source of Income Interest Income Non-interest Income Trading Revenue Fee Income Non-fee Income Income from Fiduciary Activities Definition interest incomes total incomes of bank besides interest income interest revenues from securities, gain on sales of securities purchased, investment incomes, and gain on disposal of investment service fees, security brokerage revenues, and incomes from credit card activities other incomes - operating incomes incomes from trust transactions and service 366

9 Five hypotheses are tested based on the largest, middle, and smallest percentage groups of: interest incomes, non-interest incomes, trading revenues, fee incomes, and non-fee incomes to operating incomes, respectively. This is due to the DEA score being ordinal scale data and without the assumption of the production function. Based on the characteristics of the data and methodologies, the Kruskal-Wallis test is used to analyze k-groups (k>2) sample to find the differences among k groups. The test statistics based on Conover (1999) is: N 2 k 2 1 R i N 1 T (2) 2 S i 1 ni N 1 2, n i means the i-th random sample of size, where 2 S R X ij N N 1 all ranks 4 R i is the sum of the ranks assigned to the i-th sample, N is the total observation size, k represents the sample size, and R(X ij ) is the rank of the j-th observation in the i-th group. If the differences among groups are statistically significant, it rejects the hypothesis that the mean efficiencies of largest, middle, and smallest groups are the same. Furthermore, the Kruskal-Wallis pairwise comparison test is employed to examine whether there are significant differences within two sub-sample groups. 10 If the following equation holds, it means the hypothesis of the equality of the means of the i-th and j-th group is rejected. R n i i R n j j t 1 1/ 2 1/ 2 2 N 1 T 1 1 / 2 S (3) N k ni n j Therefore, we use the Kruskal-Wallis test to test the five hypotheses whether largest, middle, and smallest bank groups are equal based on the percentages of the interest incomes, non-interest incomes, trading revenues, fee incomes, and non-fee incomes to operating incomes, respectively. If the hypothesis is rejected, three groups are further tested for every two groups by Kruskal-Wallis pairwise comparison test to examine the differences within three groups. The results can indicate how the percentages of different income sources influence the bank s efficiency. 4. EMPIRICAL RESULT 10 When the Kruskal-Wallis test is rejected, it means the hypothesis that the means of the smallest, middle, and largest groups are equal is rejected. Furthermore, we take advantage of the Kruskal-Wallis pairewise comparison to test the differences within two groups. 367

10 4.1 Summary of DEA Results The summarized descriptive statistics of bank efficiency are provided in Table 4. Under the assumption of the variable return to scale, the efficiency values of the commercial banks are between and and the most efficient banks form about a quarter of the sample size every year. 11 The variations of bank efficiency in each year are small but there has been a rising trend for the last three examination periods. The highest average efficiency occurred in 1999, with a smaller standard deviation, but the lowest average efficiency with a higher standard deviation occurred in The results indicate the sample banks operated relatively more cost-efficiently with less significant differences in Yet, in 2002, there were more significant differences among banks in thebank s cost efficiency. The results appear into two opposing extremes. Since estimated efficiency by DEA is ordinal scale data by years, it is hard to compare the average efficiency across years. The results can be explained to be that the cost efficiencies among banks have greater differences in recent years. Table 4. Summarized Descriptive Statistics of Bank Efficiency Year No. of DMUs No. of efficient DMUs Mean Standard deviation Maximum Minimum Year No. of DMUs No. of efficient DMUs Mean Standard deviation Maximum Minimum The Hypothesis Tests by the Percentage of Interest Incomes to Operating Incomes Although non-interest incomes have recently become increasingly important revenues, interest income is still the largest source of the operating incomes of commercial banks. In Figure 4, the group with the largest interest income percentage out performs other groups. The middle group appears to have a greater variance in its trend. Table 5 shows the 1% significant difference among largest, middle, and smallest groups during the examination periods. The result rejects the hypothesis that the efficiency means of three sub-sample groups are indifferent. 11 The most efficient bank has an efficiency value of one. The less efficient bank has an efficiency value less than one. 368

11 Kruskal-Wallis pairwise comparison tests shown in Table 6 are used to test the differences between every two groups. The result shows the efficiency of the largest and the smallest groups is statistically equal but they are greater than that of the middle group. 12 Therefore, the banks with higher or lower percentages of the interest incomes to the operating incomes have a statistically significant higher average efficiency. The banks with a middle class of the ratio of the interest incomes to the operating incomes operated least efficiently among banks during the examination periods. 1 Efficiency largest group middle group smallest group Year Figure 4. Average annual efficiency based on the sampling criteria on the percentage of interest incomes to operating incomes. Table 5. Kruskal-Wallis Test Based on the Percentage of Interest Incomes to Operating Incomes Group Mean Chi-square p-value Largest Group Middle Group ** Smallest Group Note. ** p <.01. Table 6. Kruskal-Wallis Pairwise Comparison Test Based on the Percentage of Interest Incomes to Operating Incomes μ i -μ j Ri j Largest-Middle * Largest-Smallest Middle-Smallest * Note. * p <.05. n i R n j Critical Value The largest-middle groups and middle-smallest groups have a significant differences at the 5% level. 369

12 4.3 The Hypothesis Tests by the Percentage of Non-Interest Incomes to Operating Incomes Non-interest incomes, when compared with interest incomes, do not influence bank efficiency. In Figure 5, the group with the largest percentage of non-interest incomes to operating incomes has an upward trend and the smallest group has a downward trend versus the grouping criteria based on the percentage of the interest incomes to operating incomes. The middle group during the examination periods had the worst average efficiency in comparison to the other two groups, which is similar to the results in Section 4.2. In Table 7, the null hypothesis is rejected by the Kruskal-Wallis test, which indicates the three groups are not equal. In Table 8, the Kruskal-Wallis comparison test shows similar results to those based on the criteria of interest incomes. 13 Based on the hypothesis tests from interest and non-interest income ratios, relative optimal ratios of two income sources might have existed during the examination periods. To further examine which source of non-interest incomes affects bank efficiency, incomes from three detailed non-interest activities were analyzed to test the effects on bank efficiency. 14 Although the non-interest income is not the major income source of the bank operating income, it has recently played an increasingly important role in bank revenues. There are several ways to generate fee incomes. Therefore, we performed further tests to examine which source of non-interest incomes affected bank efficiency. 15 The analyses are as follows. Table 7. Kruskal-Wallis Test Based on the Percentage of Non-interest Incomes to Operating Incomes Group Mean Chi-square p-value Largest Group Middle Group * Smallest Group Note. * p <.05. Table 8. Kruskal-Wallis Pairwise Comparison Test Based on the Percentage of Non-interest Incomes to Operating Incomes μ i -μ j Ri j Largest-Middle * Largest-Smallest Middle-Smallest Note. * p <.05. n i R n j Critical Value In Table 8, the results show the largest group is greater than the middle group in efficiency at the 5% significant level. Largest and smallest groups are equal in efficiency. Middle and smallest groups are not rejected at the 5% significant level, but the hypothesis is rejected at the 10 % significant level. Therefore, the result in this section is similar to that in Section Three detailed incomes from non-interest activities are trading revenues, fee incomes, non-fee incomes. Although fiduciary incomes are also part of non-interest incomes, most of the banks have no income from this activity. The test of fiduciary incomes is ignored. 15 Surely, non-interest income is not the only factor to affect bank efficiency. 370

13 1 Efficiency largest group middle group smallest group Year Figure 5. Average annual efficiency based on the sampling criteria on the percentage of non-interest incomes to operating incomes Trading revenues Trading revenue is one of the major sources in non-interest incomes. Since non-interest incomes affect bank efficiency, trading revenues may have certain effects on bank efficiency. In Figure 6, the trend lines of the three groups are interlaced during 1992 and The efficiencies of the banks with middle percentages of trading revenues were volatile between 2002 and According to the result of hypothesis test in Table 9, there is no significant difference among three groups based on the percentage of the trading revenues to operating incomes. This result is contrary to our expectation. It implies trading revenues are not the key factor to affect bank efficiency during our examination periods. 1 Efficiency largest group middle group smallest group Year Figure 6. Average annual efficiency based on the sampling criteria on the percentage of trading revenues to operating incomes. 371

14 Table 9. Kruskal-Wallis Test Based on the Percentage of Trading Revenues to Operating Incomes Group Mean Chi-square p-value Largest Group Middle Group Smallest Group Note. * p < Fee incomes In general, bankers believe the fee income is the least risky income in bank revenues. DeYoung and Rice (2004) indicated activities related to fee incomes were highly correlated to the traditional banking activities. Thus, banks can expect a good income source from fee activities when they have good performance from interest incomes. In this case, there is a relative optimal percentage of interest incomes to operating incomes. It means the relative optimal percentage of fee incomes to operating incomes might exist. However, the result is opposite to the intuitive expection. Figure 7 shows the group with the largest fee incomes does not out perform among the three groups as the result of interest incomes. In Table 10, there is no significant difference to the bank efficiency among the three groups. This result indicates the effect on bank efficiency by fee incomes is not correlated to that by interest incomes. As to the fee incomes, the relative optimal size on bank cost efficiency does not exist. Fee income is not the major factor of non-interest incomes to influence bank efficiency. The result is inconsistent to that of DeYoung and Rice (2004). Those banks relying relatively highly on fee incomes might need more inputs, such as employees, branches, etc., to generate more fee incomes. Cash flows related to fee incomes might not be stable. Thus, the input required by unit output might not be minimized. Relatively, the group with the largest percentage of fee incomes to operating incomes might not be superior to that with middle and smallest percentages of the fee incomes to operating incomes. 1 Efficiency largest group middle group smallest group Year Figure 7. Average annual efficiency based on the sampling criteria on the percentage of fee incomes to operating incomes. 372

15 Table 10. Kruskal-Wallis Test Based on the Percentage of Fee Incomes to Operating Incomes Group Mean Chi-square p-value Largest Group Middle Group Smallest Group Note. * p < Non-fee incomes In Figure 8, we observe the group with the largest percentage of non-fee incomes to operating incomes performs more efficiently than the other two groups during the examination periods. The results of the Kruskal-Wallis test and the Kruskal-Wallis comparison test, shown in Table 11 and Table 12, respectively, indicate a conspicuous differences among the three groups. The group with the largest percentage of non-fee incomes to operating incomes has the highest average efficiency. The middle group and the smallest group are statistically equal in bank efficiency. Although the percentage of non-fee incomes to operating incomes has a positive influence on bank efficiency, the percentage of non-fee incomes to operating incomes is about 0.5%- 4% yearly in each sample bank. Non-fee incomes might not play a key role in affecting bank efficiency, but the percentage of non-fee incomes to operating incomes might not be ignored in non-interest incomes Efficiency largest group middle group smallest group Year Figure 8. Average annual efficiency based on the sampling criteria on the percentage of non-fee incomes to operating incomes. Table 11. Kruskal-Wallis Test Based on the Percentage of Non-fee Incomes to Operating Incomes Group Mean Chi-square p-value Largest Group Middle Group *** Smallest Group Note. *** p <

16 Table 12. Kruskal-Wallis Pairwise Comparison Based on the Percentage of Non-fee Incomes to Operating Incomes μ i -μ j Ri j Largest-Middle * Largest-Smallest * Middle-Smallest Note: * p <.05. n i R n j Critical Value CONCLUSIONS We employ the BCC input-oriented DEA model to evaluate the relative cost efficiency of domestic commercial banks in Taiwan across a 13-year period. The DEA model is regarded as a proxy to estimate the performance of the financial institutions from multiple aspects. DEA creates broader viewpoints and is more applicable to the complex operation of financial institutions in the real world. In this study, we divide banks into three groups based on the percentage of the income sources, including the interest incomes, non-interest incomes, where the non-interest incomes are further categorized into trading revenues, fee incomes and non-fee incomes. We find bank efficiency tends toward extreme opposite cases. The banks either with the largest or smallest percentages of interest and non-interest incomes to operating incomes outperform those with middle percentage of those incomes. The relative optimal percentages of interest and non-interest incomes to operating incomes existed in the Taiwan banking industry during our examination periods. The results imply the banks with a relative high and low concentration in interest and non-interest incomes operate more cost-efficiently. The banks with more diversified income sources, which is the group of the middle percentage of interest and non-interest incomes to operating incomes, are less cost-efficient. Furthermore, we test the detailed items of non-interest incomes. We find the influences of trading revenues and fee incomes are not significant within three groups. However, the percentage of non-fee incomes to operating incomes is the factor that affects bank efficiency. Although non-fee incomes in the banking industry are only around the range of 0.4% - 5% in operating incomes, the level of non-fee incomes differentiate the cost efficiency among banks. In general, the percentage of non-interest incomes would affect bank efficiency while non-fee incomes contribute the major effect among three sources of non-interest incomes. Finally, there is a trend of a growing percentage of non-interest incomes to operating incomes in the Taiwan banking industry. Although interest incomes from traditional banking activities are still the major income source of banks, non-interest incomes from non-traditional banking activities play an increasingly important role because of falling interest rates and spreads. Both in the U.S. and in Taiwan, the trend of increasing non-interest incomes has been formed. The effect caused by non-interest incomes on bank performance 374

17 can not be ignored. In order to maintain the relative optimal level in cost efficiency, banks might not only focus on the non-interest incomes related activities, but also emphasize the traditional interest related incomes activities. This study demonstrates the evidence that relative optimal ratios of interest and non-interest incomes exist based on the aspect of bank cost efficiency. In other words, to maintain more cost-efficient operation, banks can either increase or decrease the ratios of non-interest or interest incomes to operating incomes. Banks with moderate interest and non-interest income ratios can not outperform in cost efficiency. Furthermore, the sub-items of non-interest incomes, non-fee incomes, also have a positive effect on bank efficiency. The higher the level of non-fee incomes, the higher the cost efficiency of the bank. It seems specializing in specific activities, such as traditional interest related activities or non-traditional non-interest related activities, can reduce costs and create economies of scale. Thus, banks can either increase or decrease non-interest related incomes to operate more efficiently. The existing literature shows becoming involved in non-interest incomes improves profitability but not risk-adjusted profitability. To diversify income sources, generating more non-interest incomes might not be a good choice. There is an optimal bank size to generate fee based incomes based on bank cost efficiency. We find the relative optimal ratios of interest and non-interest incomes to operating incomes on bank cost efficiency. The major contribution of this study is that the largest group of either non-interest or interest income ratios operate most cost-efficiently. Banks with moderate non-interest income ratios because of the purpose of the diversification do not work to a cost efficient perspective. The results are consistent with those of Stiroh (2004, 2006) and Stiroh and Rumble (2006). Although banks in the largest group of non-interest incomes are more cost-efficient, non-fee incomes are the major factor which contributes to the effect on cost efficiency. Based on DeYoung and Rice (2004), non-fee incomes are somewhat highly correlated to interest incomes. Giving up incomes from traditional banking activities might not be a good choice. The results are consistent with the conclusion in DeYoung and Rice (2004). Since certain sources of non-interest incomes are related to the traditional banking activities, maintaining traditional interest activities and extending non-interest revenues might be a better strategy for the banking industry in Taiwan. REFERENCES Allen, L., & Rai, A. (1996). Operational efficiency in banking: An international comparison. Journal of Banking and Finance, 20(4), Aly, H. Y., Grabowski, R., Pasurka, C., & Rangan, N. (1990). Technical, scale, and allocative efficiencies in banking: An empirical investigation. The Review of Economics and Statistics, 72(2),

18 Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), Barr, R. S., Killgo, K. A., Siems, T. F., & Zimmel, S. (2002). Evaluating the productive efficiency and performance of U.S. Commercial Banks. Managerial Finance, 28(8), Bauer, P. W., Berger, A. N., Ferrier, G. D., & Humphrey, D. B. (1998). Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economics and Business, 50(2), Berger, A. N. (1993). Distribution-free estimates of efficiency in the U.S. banking industry and test of the standard distributional assumptions. Journal of Productivity Analysis, 4(3), Berger, A. N., & Humphrey, D. B. (1991). The dominance of inefficiencies over scale and product mix economies in banking. Journal of Monetary Economics, 28(1), Berger, A. N., Humphrey, D. B., & Hancock, D. (1993). Bank efficiency derived from the profit function. Journal of Banking and Finance, 17(2-3), Berger, A. N., & Mester, L. J. (1998). Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking and Finance, 21(7), Berger, A. N., & DeYoung, R. (1997). Problem loans and cost efficiency in commercial banks. Journal of Banking and Finance, 21(6), Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), Chen, T. Y., & Yeh, T. L. (2000). A measurement of bank efficiency: Ownership and productivity changes in Taiwan. The Service Industries Journal, 20(1), Conover, W. J. (1999). Practical Nonparametric Statistics. New York, USA: Wiley. DeYoung, R. (1994). Fee-based services and cost efficiency in commercial banks. Proceedings of Conference on Bank Structure and Competition, Federal Reserve Bank of Chicago. DeYoung, R., & Roland, K. P. (2001). Product mix and earnings volatility at commercial banks: Evidence for a degree of total leverage model. Journal of Financial Intermediation, 10(1), DeYoung, R., & Rice, T. (2004). Noninterest income and financial performance at U.S. Commercial Banks. The Financial Review, 39(1), Favero, C. A., & Papi, L. (1995). Technical efficiency and scale efficiency in the Italian banking sector: A nonparametric approach. Applied Economics, 27(4), Ferrell, M. J. (1957). The measurement of productivity efficiency. Journal of the Royal Statistical Society, 120(3),

19 Ferrier, G. D., & Lovell, C. A. K. (1990). Measuring cost efficiency in banking: Econometric and linear programming evidence. Journal of Econometrics, 46(1-2), Feldman, R., & Schmidt, J. (1999). Non-interest income: A potential for profits, risk reduction and some exaggerated claims. Fedgazette, Humphrey, D. B., & Pulley, L. B. (1997). Banks responses to deregulation: Profits, technology, and efficiency. Journal of Money, Credit, and Banking, 29(1), Jemric, I., & Vujcic, B. (2002). Efficiency of banks in Croatia: A DEA approach. Comparative Economic Studies, 44, Leong, W. H., Dollery, B., & Coelli, T. (2003). Measuring the technical efficiency of banks in Singapore for the period : An application and extension of the Bauer et al. (1997) technique. ASEAN Economic Bulletin, 20(3), Li, Y. (2005). DEA efficiency measurement with undesirable outputs: An application to Taiwan s commercial banks. International Journal Services Technology and Management, 6(6), Radecki, L. J. (1999). Banks payments-driven revenue. FRBNY Economic Policy Review, Rogers, K. E. (1998). Nontraditional activities and the efficiency of U.S. commercial banks. Journal of Banking and Finance, 22(4), Smith, R., Staikouras, C., & Wood, G. (2003). Non-interest income and total income stability. Bank of England Working Paper. Shen, Chung-Hua (2005). Cost efficiency and banking performances in a partial universal banking system: Application of the panel smooth threshold model. Applied Economics, 37(9), Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with data envelopment analysis. Journal of Banking and Finance, 9(2), Stiroh, K. J. (2003). Revenue shifts and performance of U.S. bank holding companies. The Evolving Financial System and Public Policy, Proceeding of a Conference Held by Bank of Canada, Ottawa Ontario, Bank of Canada. Stiroh, K. J. (2004). Diversification in banking: Is non-interest income the answer? Journal of Money, Credit, and Banking, 36(5), Stiroh, K. J. (2006). A portfolio view of banking with interest and noninterest activities. Journal of Money, Credit, and Banking, 38(5), Stiroh, K. J., & Rumble, A. (2006). The dark side of diversification: The case of US financial holding companies. Journal of Banking and Finance, 30(8), Wu, L. Y. (1994). Liberalization and competition in banking. Taiwan Economic Research Monthly, 17(5),

20 Yi-Kai Chen received his M.B.A. and Ph.D. degrees in Finance from Drexel University, Philadelphia, U. S. A. in 1997 and 2001, respectively. Dr. Chen joined the faculty of Emporia State University, Kansas, U. S. A. from 2001 to He has served as an Assistant Professor at Department of Finance, National University of Kaohsiung, Kaohsiung, Taiwan since Dr. Chen s major research interests are in commercial banking and corporate governance. Li-Wei Huang received his B.B.A. from Fu Jen Catholic University, Taipei, Taiwan in 2004 and earned his M.E.M degree in Economics and Management from National University of Kaohsiung, Kaohsiung, Taiwan in

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