Estimating the technical and scale efficiency of Greek commercial banks

Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance sheet activities, and international operations Fotios Pasiouras University of Bath School of Management Working Paper Series 2006.17

This working paper is produced for discussion purposes only. The papers are expected to be published in due course, in revised form and should not be quoted without the author’s permission.

University of Bath School of Management Working Paper Series School of Management Claverton Down Bath BA2 7AY United Kingdom Tel: +44 1225 826742 Fax: +44 1225 826473 http://www.bath.ac.uk/management/research/papers.htm 2006 2006.01

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Fotios Pasiouras

Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance sheet activities, and international operations

Estimating the technical and scale efficiency of Greek commercial banks: the impact of credit risk, off-balance sheet activities, and international operations Fotios Pasiouras* School of Management, University of Bath, Bath, BA2 7AY, UK Abstract This paper uses data envelopment analysis (DEA) to investigate the efficiency of the Greek commercial banking industry over the period 2000-2004. We examine the impact of credit risk and off-balance sheet activities, an approach not undertaken in previous studies in Greece, by using loan loss provisions and off-balance sheet items as additional inputs/outputs. We also compare the traditional intermediation approach used in previous studies with the recently proposed (in the context of DEA) profit-oriented approach. Furthermore, we compare banks that undertake only domestic activities with the ones that have expanded their operations abroad. Finally, we use Tobit regression to explain the efficiency of banks. Our results indicate that the inclusion of loan loss provisions as an input increases the efficiency scores, while off-balance sheet items do not have a significant impact. The differences between the efficiency scores obtained through the profit-oriented model and the ones developed through the intermediation approach are in general small. Banks that have expanded their operations abroad appear to be more efficient than the ones operating only at a national level. Higher capitalization, loan activity, and market power increase the efficiency of banks. The number of branches also has a positive significant impact on efficiency, whereas the number of ATMs does not appear to influence efficiency. The results are mixed with respect to variables indicating whether the banks are operating abroad through subsidiaries or branches. Keywords: Banks, DEA, Efficiency, Greece JEL: G21, C24, C67, D61

*

© Fotios Pasiouras, 2006, Tel: +44 1225 384 297, E-mail: [email protected]

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1. Introduction Over the last years, the Greek banking sector has experienced a major restructuring. Important changes that are frequently highlighted by both academics and practitioners are the establishment of the single EU market, the introduction of the euro, the internationalization of competition, the interest rate liberalization, the deregulation, and the recent wave of mergers and acquisition. The Greek banking has also experienced considerable improvements in terms of communication and computing technology, as banks expanded and modernized their distribution networks, which apart from the traditional branches and ATMs, now include alternative distribution channels such as internet banking. As mentioned in the annual report of the Bank of Greece (2004), in recent years, Greek banks have also taken major steps towards upgrading their credit risk measurement and management systems, by introducing credit scoring and probability default models. Furthermore, they expanded their product/service portfolio to include activities such as insurance, brokerage and asset management, and at the same time increased their off-balance sheet operations and non-interest income. Finally, another attribute that is worthwhile mentioning is the increased trend towards globalization that focused on the wider market of Balkans (e.g Albania, Bulgaria, FYROM1, Romania, Serbia) and added to the previously limited international activities of Greek banks in Cyprus and USA. The performance of the subsidiaries operating abroad is expected to have an impact on the performance of parent banks and consequently on future decisions for further internationalisation attempts. The purpose of the present study is to employ data envelopment analysis (DEA) and re-investigate the efficiency of the Greek banking sector, while considering several

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of the issues discussed above. We therefore differentiate our paper from previous ones that focus in the Greek banking industry2 and add insights in several respects, discussed below. First of all, we examine for the first time the impact of credit risk on the efficiency of Greek banks by including loan loss provisions as an additional input as in Charnes et al. (1990), Drake (2001), Drake and Hall (2003), and Drake et al. (2006) among others. As Mester (1996) points out “Unless quality and risk are controlled for, one might easily miscalculate a bank’s level of inefficiency; e.g. banks scrimping on credit evaluations or producing excessively risky loans might be labelled as efficient when compared to banks spending resources to ensure their loans are of higher quality” (p. 1026). We estimate the efficiency of banks with and without this input to adjust for different credit risk levels and examine its impact on efficiency. Second, unlike previous studies in the Greek banking sector, we consider offbalance sheet activities during the estimation of efficiency measures. Several recent studies that examine the efficiency of banks, with data envelopment analysis or stochastic frontier techniques, acknowledge the increased involvement of banks in nontraditional activities and include either non-interest (i.e. fee) income (e.g. Lang and Welzel, 1998; Drake, 2001; Tortosa-Ausina E., 2003) or off balance sheet items (e.g. Altunbas et al., 2001; Altunbas and Chakravarty, 2001; Isik and Hassan, 2003a,b; Bos and Kolari, 2005; Rao, 2005) as an additional output. However, despite their increased importance for Greek banks, such activities have not been considered in the past. Again, we estimate the efficiency of the banks in our sample with and without off-balance sheet activities to observe whether it will have an impact on efficiency.

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Previous studies that focus on the efficiency of the Greek banking sector are: Karafolas and Mantakas (1996), Noulas (1997), Christopoulos and Tsionas (2001), Christopoulos et al. (2002), Tsionas et al. (2003), Halkos and Salamouris (2004), Apergis and Rezitis (2004), Rezitis (2006). These studies are discussed in more detail in the next section.

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Third, we compare the results obtained from the intermediation approach that has been followed in most recent studies in banks’ efficiency with a profit-oriented approach that was recently proposed by Drake et al. (2006) in the context of DEA and is in line with the approach of Berger and Mester (2003) in the context of their stochastic frontier approach. This allows us to observe if different input/output definitions affect efficiency scores. Fourth, we compare the efficiency scores of Greek banks that have expanded their operations abroad (i.e. international Greek banks, hereafter IGBs), with the ones of Greek banks whose operations are limited in the domestic market3 (i.e. purely domestic banks, hereafter PDBs). To the best of our knowledge, no study has undertaken such an analysis in Greece. However, in a study of the Turkish banking sector, Isik and Hassan (2002) found evidence that multinational domestic banks are superior to purely domestic banks in terms of all efficiency measures (i.e. cost efficiency, allocative efficiency, technical efficiency, pure technical efficiency) except for scale efficiency. The conclusions drawn from our study could be useful to the managers of other Greek banks or other medium-sized banking sectors in general, considering the internationalization of their operations. Fifth, we run a regression to explain the efficiency of banks, an approach that has been followed only in two of the past studies in Greece (Christopoulos et al., 2002; Rezitis, 2006). However, in our case we examine a most recent period that is after the numerous changes outlined above. 3

One could argue that the IGBs are the large Greek banks, and we therefore actually comparing large versus small banks. While this argument would have a basis, this is obviously the case in numerous studies that compare various groups of banks either in terms of ownership such as state/private (e.g. Noulas, 1997), and foreign/domestic (e.g. Sturm and Williams, 2004; Kasman and Yildirim, 2006) or in terms of specialization such as commercial, savings, cooperative (e.g. Altunbas et al., 2001; Girardone et al., 2004). For example, domestic banks are in most cases quite larger than foreign banks operating in a country (i.e. subsidiaries), as commercial banks are usually larger than cooperative and savings banks. Noulas (1997) also mentions that the private banks in his sample are of much smaller size than the state ones. Hence, while one could kept in mind this note while interpreting the results, we do not believe that it reduces they usefulness of the study.

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The rest of the paper is as follows: Section 2 reviews the literature that focuses on the efficiency of the Greek banking sector. Section 3 provides a brief discussion of DEA. Section 4 presents the data and variables. Section 5 discusses the empirical results, and Section 6 concludes the study.

2. Literature Review Karafolas and Mantakas (1996) use a second-order translog cost function to estimate for the first time an econometric form of the costs in the Greek banking and investigate economies of scale. Using data for eleven banks from the period 1980-1989, they find that although operating-cost scale economies do exist, total cost scale economies are not present. Participation of the dataset in sub-samples by banks’ size (i.e. large and small banks) and time periods (i.e. 1980-84, 1985-89) has not altered the results. Finally, the results indicate that technical change has not played a statistically significant role in the decrease of average cost. Noulas (1997) examines the productivity growth of ten private and ten state banks operating in Greece during 1991 and 1992, using the Malmquist productivity index and the DEA method to measure efficiency. The author follows the intermediation approach and finds that productivity growth averaged about 8%, with state banks showing higher growth than private ones. The results also indicate that the sources of the growth differ across the two types of banks. State banks’ productivity growth is a result of technological progress, while private bank’s is a result of increased efficiency. Christopoulos and Tsionas (2001) estimate the efficiency in the Greek commercial banking sector over the period 1993-1998 using homoscedastic and heteroscedastic frontiers. They find an average technical efficiency about 80% for the

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heteroscedastic model and 83% for the homoscedastic one. They also find that both technical and allocative inefficiencies decrease over time for smaller as well as larger banks. The regression of inefficiency measures against a trend indicates that the improvement in technical and allocative inefficiencies for small banks equal 19.7% and 39.1%, accordingly. The corresponding figures for large banks are 10.4% and 21.1%. Christopoulos et al. (2002) examine the same sample with a multi-input, multioutput flexible cost function to represent the technology of the sector and a heteroscedastic frontier approach to measure technical efficiency. Regression of the efficiency measures over various bank characteristics indicates that larger banks are less efficient than smaller ones, as well as that economic performance, bank loans and investments are positive related to cost efficiency. In a latter study, Tsionas et al. (2003) use the same sample as in Christopoulos and Tsionas (2001) and Christopoulos et al. (2002) but employ DEA to measure technical and allocative efficiency, and the Malmquist Total Factor Productivity approach to measure productivity change. The results indicate that most of the banks operate close to the best market practices with overall efficiency levels over 95%. Large banks appear to be more efficient than smaller ones, while allocative inefficiency costs seem to be more important than technical inefficiency costs. They also document a positive but not substantial technical efficiency change which is mainly attributed to efficiency improvement for medium-sized banks and to technical change improvement for large banks. Halkos and Salamouris (2004) also use DEA but follow a different approach than previous studies by using financial ratios as output measures and with no use of input measures. The sample ranges between 15 and 18 banks depending on the year under consideration. The results indicate a wide variation in average efficiency through

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the 1997-1999, and a positive relationship between size and efficiency. Furthermore, there is non-systematic relationship between transfer of ownership through privatization of public banks and last’s period performance. Apergis and Rezitis (2004) specify a translog cost function to analyze the cost structure of the Greek banking sector, the rate of technical change and the rate of growth in total factor productivity. They use both the intermediation and the production approach and a sample of six banks over the period 1982-1997. Both models indicate significant economies of scale and negative annual rates of growth in technical change and in total factor productivity. Rezitis (2006) uses the same dataset but employs the Malmquist productivity index and DEA to measure and decompose productivity growth and technical efficiency, respectively. He also compares the 1982-92 and 1993-97 sub-periods, while Tobit regression is employed to explain the differences in efficiency among banks. The results indicate that the average level of overall technical efficiency is 91.3%, while productivity growth increased on average by 2.4% over the entire period. The growth in productivity is higher in the second sub-period and is being attributed to technical progress, in contrast to improvements in efficiency that was the main driver until 1992. Furthermore, during the second sub-period pure efficiency is higher, and scale efficiency is lower, indicating that although banks achieved higher pure technical efficiency, they moved away from optimal scale. The regression results indicate that size and specialization have a positive impact on both pure and scale efficiency.

3. Methodology From a methodological perspective, there are several approaches that can be used to examine the efficiency of banks, such as Stochastic Frontier Analysis (SFA), Thick

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Frontier Approach (TFA), Distribution Free Approach (DFA), and DEA. Berger et al (1993), Berger and Humphrey (1997) and Goddard et al. (2001) provide key discussions and comparison of these methods in the context of banking. In the present study, following several recent studies we use DEA to estimate the efficiency of banks4. We only briefly outline DEA here, while more detailed and technical discussions can be found in Coelli et al. (1999), Cooper et al. (2000), and Thanassoulis (2001). DEA is a mathematical programming approach for the development of production frontiers and the measurement of efficiency relative to the developed frontiers (Charnes et al., 1978). The best-practice production frontier for a sample of decision making units (DMUs) is constructed through a piecewise linear combination of actual input-output correspondence set that envelops the input-output correspondence of all DMUs in the sample (Thanassoulis, 2001). In their seminal study Charnes et al. (1978) proposed a model that had an input orientation and assumed constant returns to scale (CRS). However, CRS is only appropriate when all firms are operating at an optimal scale. Nevertheless, as firms may not be operating at optimal scale due to imperfect competition or constraints in finance, Banker et al. (1984) suggested the use of variable returns to scale (VRS) that allows the calculation of technical efficiency (TE) devoid of scale efficiency (SE) effects. The DEA model can be either input or output-oriented. As Coelli et al. (1999) point out the input-oriented technical efficiency measures address the question: “By how much can input quantities be proportionally reduced without changing the output quantities produced?” (p. 137). In contrast, the output-oriented measures of technical efficiency address the question: “By how much can output quantities be proportionally 4

Examples of recent studies that use DEA are among others Haslem et al. (1999), Maudos et al. (2002a), Casu and Molyneux (2003), Drake and Hall (2003), Luo (2003), Ataullah et al. (2004), Hauner (2005), Ataullah and Le (2006), Casu and Girardone (2006), Drake et al. (2006).

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expanded without altering the input quantities used?” (p. 137). It should be mentioned that the two measures always provide the same value under CRS but are unequal when VRS is assumed. However, Coelli et al. (1999) mention that since linear programming does not suffer from statistical problems as simultaneous equation bias, the choice of an appropriate orientation is not as crucial as it is in the case of econometric orientation, and it many instances, the choice of orientation has only a minor influence upon the scores obtained (Coelli and Perelman, 1996). Most of the studies in banking, including the present one, follow the input-oriented approach, although some studies adopt the output-oriented approach (e.g. Ataullah et al., 2004; Ataullah and Le, 2006) or report the results from both (e.g. Casu and Molyneux, 2003; Beccali et al., 2006). One of the well-known advantages of DEA, which is relevant to our study, is that DEA works particularly well with small samples. As Maudos et al. (2002a) point out, “Of all the techniques for measuring efficiency, the one that requires the smallest number of observations is the non-parametric and deterministic DEA, as parametric techniques specify a large number of parameters, making it necessary to have available a large number of observations.” (p. 511). Other advantages of DEA are that it does not require any assumption to be made about the distribution of inefficiency and that it does not require a particular functional form on the data in determining the most efficiency decision making units (DMUs). One the other hand, the shortcomings of DEA are that it assumes data to be free of measurement error and is sensitive to outliers5.

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While many studies that use DEA do not address the issue of sensitivity to outliers, others choose either to perform the analysis with and without the potential outliers and compare the results (e.g. Casu and Molyneux, 2003) or delete observations that are considered outliers (Isik and Hassan, 2002). For example, Havrylchyk (2006) deleted banks whose prices were below or above 1% or 99%. In the present study, considering the small number of observations, we decided to smooth figures above and below the 99% and 1% percentiles respectively, hence reducing the impact of outliers while retaining all observations in sample.

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4. Data and variables Our sample consists of the universe of commercial banks6 with financial statements available in Bankscope database of Bureau van Dijk’s company, operating in Greece between 2000 and 20047. Supplementary data for the banks (e.g. staff number, number of ATMs) were collected from the Hellenic Bank Association. The sample ranges between 12 and 18 banks per year and consists of 78 observations in total. As mentioned in several studies, there is an on-going debate in the banking literature relative to the proper definition of inputs and outputs. The two main approaches are the “production approach” and the “intermediation approach” (Berger and Humphrey, 1997). The production approach assumes that banks produce loans and deposit account services, using labour and capital as inputs, and the number and type of accounts measure outputs. The intermediation approach views banks as financial intermediates that collect purchased funds and use labour capital to transform these funds to loans and other assets. Berger and Humphrey (1997) point out that neither of these two approaches is perfect because they cannot fully capture the dual role of financial institutions as providers of transactions/document processing services and being financial intermediaries. They point out that the production approach may be somewhat better for evaluating the efficiencies of branches of financial institutions and the intermediation approach may be more appropriate for evaluating entire financial institutions. Most recently, Drake et al. (2006) proposed the use of a profit-oriented approach in a DEA context that is in line with the approach of Berger and Mester (2003) in the context of their stochastic frontier approach. They point out that their results 6

On the basis of the classification available in Bankscope. The study begins in 2000 for various reasons. First of all, this is the earliest year for which data were available in the online version of Bankscope to which we had access. Second, prior to 2000 the Greek banking industry witnessed a number of M&As that could complicate our analysis. Third, existing studies already provide evidence for various periods up to 1999. Data for 2005, that was the most recent year with available data, were not considered as the EU imposed the use of International Accounting Standards, and the data would not be comparable across the period of our analysis.

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support the argument of Berger and Mester (2003) that a profit-based approach is better able to capture the diversity of strategic responses by financial firms in the face of dynamic changes in competitive and environmental conditions. In the present study, following most of the recent studies we adopt the intermediation approach. However, we also compare the obtained results with the ones of the profit-oriented approach suggested by Drake et al. (2006). We estimate five DEA models in total (Table 1).

[Insert Table 1 Around Here]

Models 1 to 4 are based on the intermediation approach but different inputs/outputs combinations are examined so as to explore the impact of credit risk and off-balance sheet activities on bank efficiency. In Model 1, we select the following three inputs: fixed assets, customer deposits & short term funding, and number of employees. The two outputs of Model 1 are loans and other earning assets. Hence, this is a classical model under the intermediation approach found in most studies. In Model 2, we introduce off-balance sheet items as an additional output, to account for the fact that in recent years banks are heavily involved in off-balance sheet activities. Model 3 is a reestimation of Model 1 but following Charnes et al. (1990), Drake (2001), Drake and Hall (2003), and Drake et al. (2006) among others, includes loan loss provisions as an additional input in the DEA model to account for credit risk8. Finally, Model 4 is a re-

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Mester (1996), Altunbas et al. (2000) and Drake and Hall (2003) among others point out that failure to adequately account for risk can have a significant impact on relative efficiency scores. Berg et al. (1992) made the original observation and included nonperforming loans in a nonparmetric study of bank production, whereas Hughes and Mester (1993) applied the concept to parametric estimations (Berger and DeYoung, 1997). Some other studies use equity capital as a control for risk (e.g. Altunbas et al., 2001; Maudos et al., 2002b; Akhigbe and McNulty, 2003; Kasman and Yildirim, 2006). However, Laeven and Majnoni (2003) mention that risk should be incorporated into efficiency studies via the inclusion of loan loss provisions that is actually a cost required to build up loan loss reserves. Altunbas et al. (2000) and

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estimation of Model 1 that includes both off-balance sheet items and loan loss provisions, to simultaneously account for off-balance sheet activities and credit risk. Model 5 is the profit-oriented one, in which following Drake et al. (2006) revenue components are defined as outputs and cost components as inputs. The three inputs specified are employee expenses, non-interest expenses, and loan loss provisions. The three outputs are net interest income, net commission income and other income. As Drake et al. (2006) point out “from the perspective of an input-oriented DEA relative efficiency analysis, the more efficiency units will be better at minimizing the various costs incurred in generating the various revenue streams and, consequently, better at maximizing profits” (p. 1451).

5. Empirical results The discussion of the empirical results on the efficiency of banks in Greece is structured in three parts. First, we discuss the efficiency of the full sample of banks obtained through an input-oriented approach with VRS and the various inputs/outputs combination discussed above9. Then we focus on the specific issue of the relative efficiency of IGBs versus PDBs. Finally, we investigate the determinants of efficiency using Tobit regression10.

5.1. Efficiency estimates - full sample Table 2 presents the results from the four models that correspond to input/outputs selected on the basis of the intermediation approach. Table 3 reports the results of Model 5 that corresponds to the profit oriented approach.

Pastor and Serrano (2005) have used loan loss provisions in a stochastic frontier context as have the few recent studies in a DEA context mentioned in the text. 9 Efficiency scores were estimated with DEAP 2.1 discussed in Coelli (1996). 10 Tobit analysis was performed with E-views 5.1.

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[Insert Tables 2 and 3 Around Here]

The average TE obtained by Model 1 ranges between 0.882 (2004) and 0.977 (2000), with an overall mean11 over the entire period equal to 0.950, while the corresponding figures for SE are 0.938 (2003), 0.991 (2001) and 0.966 (overall mean) respectively. Hence, between 2000 and 2004 banks could improve technical efficiency by 5% and scale efficiency by 3.4% on average. These figures increase only slightly when we consider off-balance sheet items as an additional output and equal 0.952 (TE) and 0.971 (SE). However, when we consider loan loss provisions the overall mean technical efficiency increases by almost 1.5%. Thus, controlling for credit risk appears to have some impact on the efficiency scores. This is supported further by the only marginal increase by 0.001 in Model 4 where off-balance sheet items and loan loss provisions are simultaneously considered, indicating that the increase from the base Model (ie. Model 1) is due to loan loss provisions. Our results are similar to the ones obtained in previous studies in Greece that employ DEA and follow the intermediation approach. For example, Rezitis (2006) reports pure technical efficiencies between 0.977 and 0.994, and scale efficiencies between 0.918 and 0.934 depending on the period under consideration, while Tsionas et al. (2003) also report an overall technical efficiency equal to 0.984. Turning to the results obtained from the profit-oriented model (i.e. Model 5) we observe that TE is between 0.924 (2004) and 0.975 (2003) with an overall mean equal to 0.950. The corresponding figures for SE are 0.942 (2002), 0.978 (2004) and 0.960 (overall mean). The contrast between these results and the ones obtained from the 11

This overall mean corresponds to the average calculated by pooling the efficiency scores calculated by year, and not to a model estimated with panel data.

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intermediation approach are mixed. We only partially support the results of Drake et al. (2006) indicating that technical efficiency is generally higher under the intermediation approach than under the profit approach. In our study, this is not always the case and depends upon the models that are compared and the year of observation. Most detailed, we observe that compared to Models 1 and 2 technical efficiency is higher under the profit-oriented approach during 2003 and 2004 and lower over the period 2000-2002. Compared to Models 3 and 4 technical efficiency is higher under the profit-oriented approach only during 2003. However, it should be mentioned that the intermediation oriented model estimated in Drake et al. (2006) is most closely related to Model 4 of the present study12. Looking at the overall mean now, we observe that the profit-oriented approach indeed provides lower efficiency scores than all the models estimated under the intermediation approach. Nevertheless, in any case the differences between the two approaches are much smaller than the ones reported in Drake et al. (2006). Another interesting point that emerges from the contrast of the results obtained by the two approaches is that the range in the efficiency scores is smaller when the profit-oriented approach is used. That is, the average efficiency scores for Model 1 range between 0.882 and 0.992, and those of Model 2 range between 0.887 and 0.992. The corresponding figures for Model 3 are 0.927 and 0.992 and those of Model 4 are 0.930 and 0.992. In contrast, Model 5 the efficiency scores of Model 5 range only between 0.924 and 0.975. This could be in part attributed to the argument of Drake et al. (2006) that “…the profit approach will capture the full impact of any adverse environmental factors on revenues as well as costs, while the intermediation approach tends to focus on the technical efficiency of the financial intermediation approach” (p. 1462). 12

Drake et al. (2006) use personnel expenses as input whereas we use the number of staff members. They also use non-interest income rather than off-balance sheet items as an output for off-balance sheet activities.

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However, in any case the differences among the models are in general quite small and do not allow us to conclude whether the profit-oriented approach provides more reliable efficiency scores or not.

5.2. International versus purely domestic banks We now turn to the efficiency of IGBs as opposed to the efficiency of PDBs. Morck and Yeung (1991) provide some empirical evidence of the multinational advantage based on the transfer of intangible assets such as technology and reputation from home country so subsidiaries. Furthermore, operating abroad gives banks the opportunity to follow their customers and consequently retain them13 (i.e. defensive expansion theory, see Williams, 2002). This is obviously the one of type of transfer in the firm, while the opposite or the transfer from one subsidiary to another is possible as well. Hence, banks that operate abroad might be able to transfer resources such as technology or employees with increased skills and experience in terms of risk management, regulatory and reporting practices, gained from working in more sophisticated and advanced environments (e.g. UK, USA). In that case, the efficiency of IGBs will be higher than the one of PDBs. On the other hand, IGBs will have to transfer efforts and resources to the subsidiaries that would otherwise be available to compete in the domestic market, and this might have a negative effect on their efficiency relative to PDBs. Six of the banks in the sample (i.e. Alpha Bank, EFG Eurobank Ergasias, Egnatia Bank, Emporiki Bank of Greece, National Bank of Greece, Piraeus Bank) had subsidiaries abroad over the entire period of our analysis. Most of these banks as well as Agricultural Bank of Greece had also branches abroad, while Novabank had branches in 2002 and switched to an international presence through subsidiaries in 2003 and 2004. 13

It is possible that otherwise these customers would switch to another bank that provides services both abroad as well as in the home market.

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We therefore adopt two definitions for IGBs. First, we consider as IGBs only those banks that are operating abroad through subsidiaries (SIGBs). Then, we consider as IGBs those banks that have an international presence either through subsidiaries or branches (SBIGBs). Table 4 reports the average efficiency scores for the two types of banks estimated by Models 414 and 5, while distinguishing between PDBs and SIGBs (Panel A) and PDBs and SBIGBs (Panel B).

[Insert Table 4 Around Here]

The contrast of the overall mean efficiency scores obtained from the pooled sample indicates that IGBs are more efficient than PDBs in all cases. The largest difference is observed in the case of TE while comparing SIGBs and PDBs under the profit-oriented approach (0.045). That is, SIGBs can improve technical efficiency by 2.3% whereas PDBs can improve it by 6.8%. In contrast, the smallest difference is obtained in the case of SE while comparing SBIGBs and PDBs under the intermediation approach and equals 0.005. The comparison of the efficiency scores of PDBs and SIGBs by year indicates that under the intermediation approach PDBs have higher SE only in 2000. This finding holds under the profit-oriented approach as well. However, under the latter approach PDBs appear to have a higher TE in 2003 as well. Inclusion of the banks that operate through branches in the group SIGBs does not alter the results, the only difference being that PDBs are now slightly more efficient, in terms of scale efficiency estimated by Model 4, in 2002 as well.

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From this point and for the rest of our analysis we select Model 4 as representative of the intermediation approach, assuming that according to numerous recent studies it represents a more appropriate combination of inputs and outputs that considers off-balance sheet items and credit risk.

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To examine whether the differences between the groups of interest are statistically significant, we perform a Kruskal-Wallis (K-W) non-parametric test. Due to the small number of observations from each group by year, the test is performed on the scores of the pooled sample of the 78 observations. The results of the K-W test indicate that under the intermediation approach IGBs, both SIGBs and SBIGBs, are more efficient than PDBs, in terms of TE that is statistically significant at the 10% and 5% level respectively15. Hence, our results appear to be in line with the ones of Isik and Hassan (2002) in the Turkish banking sector. There are several possible explanations for these findings. First, these banks may transfer resources such as technology or employees with increased skills gained abroad to the home market, hence increasing their technical efficiency. They can also retain their customers by following them abroad or experience gains due to increased reputation and diversification that reduces their risk. As for the insignificant differences in the case of scale efficiency, Isik and Hassan (2002) mention that exploitation of scale economics cannot be the motivation for these large banks’ foreign expansion as multinational banks would have exhausted their scale economies when they were small, pure domestic banks.

5.3. Stage 2 – Tobit analysis In order to investigate the determinants of efficiency we construct an econometric model with technical and scale efficiency as dependent variables. As in previous studies, due to the limited nature of our efficiency measure (i.e. ranges between 0 and 1) we use Tobit analysis. As Saxonhouse (1976) points out, heteroscedacity can emerge when estimated parameters are used as dependent variables in the second stage analysis.

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In the first case, the chi-square equals 3.612 whereas in the later case it equals 5.092. Differences in SE were not significant for none of the two models, as they were not significant in the case of TE obtained from Model 5. The chi-square and p-values are not reported here but are available from the author upon request.

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Hence, following Hauner (2005), QML (Huber/White) standard errors and covariates are calculated. We examine the effect of two groups of factors on efficiency. First, we analyze the influence of various bank financial characteristics. We follow previous studies and examine the following variables: equity to assets (EQAS), return on average assets (ROAA), loan to assets (LOANS), and market power (POWER) as measured by the relative size of bank (i.e. market share in terms of assets). Second, we examine the influence of bank’s strategies in terms of investments in technology (i.e. ATMs and branches) and internationalization of operations. We include the number of ATMs (ATMs), the number of branches (BRANCH), and two dummy variables indicating whether banks are offering their services abroad through subsidiaries (SUB_ABR, that takes the value of 1 if yes and 0 otherwise) or branches (BR_ABR, that takes the value of 1 if yes and 0 otherwise). Considering the small number of observations in our sample, we estimate two specifications of the Tobit model with each one of the two sets of variables examined sequentially. We follow this approach in order not to overload the regressions. The findings are reported in Table 5. Panel A presents the results of the regressions with the bank financial characteristics, while Panel B presents the ones with the variables that proxy for the strategic decisions of the banks.

[Insert Table 5 Around Here]

EQAS is statistically significant and positively related to efficiency in all our specifications. Hence, well-capitalized banks are also more efficient, both in terms of technical and scale efficiency. These results are in line with Isik and Hassan (2003a) in

18

Turkey, Casu and Giradone (2004) in Italy, Rao (2005) in United Arab Emirates and Kwan and Eisenbeis (1997) in the US among others, all reporting a positive relation between capitalization and various measures of efficiency. One potential explanation for these findings is that since EQAS reflects the degree to which shareholders have their own capital at risk in their institution it also reflects their incentives to monitor management and assure that the bank operates efficiently (Eisenbeis et al., 1999). Hence, as Isik and Hassan (2003a) mention these results are in favour of the conjectures of moral hazard theory. ROAA is positively related to the efficiency measures in all cases however, it is statistically significant only in the case of the profit-oriented approach (i.e. Model 5). Even in that case, it has only a marginal impact (i.e. 10% level) on SE. Although Christopoulos et al. (2002) report a positive and significant relationship between profitability and efficiency in the Greek banking sector between 1993-1998, the results from studies in other countries are mixed. For instance, Ataullah and Le (2006) report both negative and positive statistically significant impacts of return on assets on efficiency measures in India depending on the specification of the model. Casu and Molyneux (2003) examine a sample of banks from the principal EU banking sectors16 and find a positive relationship between profitability efficiency, which is however statistically significant in only two of the five years of the analysis. Isik and Hassan (2002a) report a positive and significant correlation between both return on equity and return on assets and efficiency in Turkey. However, Casu and Girardone (2004) report a negative and statistically significant relationship in Italy. LOANS carries a positive sign that is statistically significant in all cases and is consistent with Isik and Hassan (2003a). Casu and Giradone (2004) also report a

16

The principal EU banking sectors are: France, Germany, Italy, Spain, UK.

19

positive relationship although not statistically significant. Isik and Hassan (2003a) argue that the positive relationship between loan activity and efficiency can be attributed to the ability of relatively efficient banks to manage operations more productively, that enables them to have lower production costs and consequently to offer more reasonable loan terms allowing them to gain larger share in the loan market segment. In contrast to the above studies, Havrylchyk (2006) finds a negative relationship between the loans to assets ratio and efficiency, which however becomes positive once the off-balance sheet items are omitted from outputs. POWER is also statistically significant and positively related to TE and SE in both models (i.e. 4 and 5). Since this variable reflects the relative size of the bank and its market power, our findings seem to support the arguments in favour of size as well as market share. While our results contradict the ones of Christopoulos et al. (2002) that report a negative relationship between size and efficiency, they are in line with the studies of Halkos and Salamouris (2004) and Apergis (2006) in Greece and Berger et al. (1993) and Miller and Noulas (1996) in the US that report a positive relationship between size and efficiency. When we interpret our variable as an indicator of market power rather than size, we tend to support the efficient structure hypothesis. As Isik and Hassan (2003a) explain due to their low costs of production, relatively efficient firms might have competed more aggressively, made higher profits and ultimately gained larger market share. Turning to the variables related to the strategic choices of the banks, the results indicate that only BRANCH is significant in all cases (although significant only at the 10% level in the case of TE estimated with model 4). On the other hand, ATM does not have an impact on efficiency in any of our specifications. One potential explanation is that the Greek banking system relies heavily on branches as a distribution network with

20

the number of branches increasing from year to year contrary to other EU countries, where a declining trend takes place. As mentioned in the summary of the Annual Report of the Bank of Greece (2005) the continued increase in the number of bank branches in Greece is associated with the fast growth of retail banking and Greek customer’s continued preference for transaction through branches. While the number of ATMs also increases (e.g. 5,468 in 2003; 5,787 in 2004) such channels are supplementary to branches, which retain their key role as points of sale (Annual Report of Bank of Greece, 2004). Attracting new customers and maintaining the existing ones is a main strategic choice for banks, which as mentioned in the 2004 annual report of the Bank of Greece, can be achieved more effectively through personal contact at branches. Furthermore, an extensive branch network also supports the expansion of Greek banks via cross-selling such as bankassurance products (Summary of Annual Report of Bank of Greece, 2005). These characteristics of the Greek banking sector obviously explain why the number of branches has a positive impact on the efficiency of banks rather than alternative distribution networks such as ATMs. Finally, with respect to the variables that are related to the international presence of banks, the results are mixed. Operating abroad through branches appears to be negatively related to the efficiency of banks, which is however significant only in the case of TE estimated with Model 5. In contrast, operating abroad through subsidiaries appears to have a positive impact on both technical and scale efficiency although this is statistically significant only for Model 4.

6. Conclusions In the present study we have estimated the technical and scale efficiency of Greek banks over the period 2000-2004. We used in-put oriented data envelopment analysis with

21

variable returns to scales and estimated five models to examine several issues not considered in the study of the Greek banking sector in the past. More detailed, we estimated the efficiency of the banks in our sample with and without off-balance sheet items and loan loss provisions to account for different levels of off-balance sheet activities and credit risk. In all cases, the models were estimated following the traditional intermediation approach and the recently proposed (in the context of DEA) profit-oriented approach. We also compared the efficiency of banks that have an international presence with the ones that offer their services only in the domestic market, an issue that has received relatively small attention in the bank efficiency literature. Finally, we used Tobit analysis to regress the efficiency scores obtained from the first stage over several variables reflecting bank financial characteristics and strategic decisions. The results indicate that the inclusion of off-balance sheet items in the outputs does not have an impact on the efficiency scores, while the inclusion of loan loss provisions in the inputs increases the efficiency scores. The contrast of the scores obtained from the models estimated through the intermediation approach with the ones obtained from the profit-oriented approach, by year, provided mixed results. However, we found that in terms of the overall mean, the profit-oriented model provided lower efficiency scores than all the models estimated under the intermediation approach. Nevertheless, the differences between the two approaches were much smaller than the ones reported in Drake et al. (2006). Banks with international operations appeared to be more efficient than the ones operating only at the national level, consistent with Isik and Hassan (2002). We obtained similar results whether we defined as international, banks that offer their services abroad either through subsidiaries or both subsidiaries and branches. However, the differences were statistically significant only in the case of

22

technical efficiency estimated under the intermediation approach. We finally regressed the scores obtained from the profit-oriented model and the full intermediation model over banks’ financial characteristics and variables reflecting strategic decisions. Capitalization, loan activity and market share in terms of total assets were statistically significant and positively related to the efficiency measures in all cases. Profitability was positively related to the efficiency measures in all cases however, was statistically significant only in the case of the profit-oriented approach. Turning to the variables related to the strategic choices of the banks, the results indicated that the number of branches was significant in all cases, while the number of ATMs did not had an impact in any of our specifications. Finally, with respect to the dummy variables indicating whether the banks were operating abroad through branches or subsidiaries the results were mixed. Future research could extend the present study towards numerous directions. First, commercial banks could be compared with cooperative banks, as the later ones have not received any attention in past studies in Greece. Second, domestic banks could be compared with foreign banks, which have received only limited attention and not in the context of efficiency. Finally, it would be worthwhile to consider a longer time period and examine the impact of environmental factors such as GDP, inflation and stock market capitalization on the efficiency of the Greek banking sector.

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Table 1 - Combination of inputs/outputs Model 1

Intermediation approach Model 2 Model 3

Model 4

Profit oriented approach Model 5

Inputs Fixed assets Customer deposits & short term funding Number of employees

Fixed assets Customer deposits & short term funding Number of employees

Fixed assets Customer deposits & short term funding Number of employees Loan loss provisions

Fixed assets Customer deposits & short term funding Number of employees Loan loss provisions

Employee expenses Other Non interest expenses

Loans Other earning assets

Loans Other earning assets Off-balance items

Loans Other earning assets

Loans Other earning assets Off-balance items

Net interest income Net commission income Other operating income

Loan loss provisions

Outputs

29

Table 2 – DEA results with intermediation approach (Models 1-4)

Year 2004 (N =18) 2003 (N =17) 2002 (N = 17) 2001 (N = 14) 2000 (N = 12) Overall (2001-2004; N = 78) 2004 (N =18) 2003 (N =17) 2002 (N = 17) 2001 (N = 14) 2000 (N = 12) Overall (2001-2004; N = 78)

TE SE (VRS) Mean Mean Model 1 0.882 0.974 0.938 0.938 0.980 0.981 0.992 0.991 0.977 0.978 0.950 0.966 Model 3 0.927 0.992 0.954 0.954 0.980 0.981 0.992 0.991 0.977 0.978 0.964 0.975

TE SE (VRS) Mean Mean Model 2 0.887 0.977 0.938 0.938 0.980 0.981 0.992 0.991 0.979 0.979 0.952 0.971 Model 4 0.930 0.994 0.954 0.955 0.908 0.981 0.992 0.991 0.979 0.979 0.965 0.979

Notes: TE: technical efficiency, SE: scale efficiency, VRS: Variable returns on scale; Model 1 is estimated with fixed assets, customer deposits & short term funding, and number of employees as inputs, and loans and other earning assets as outputs; Model 2 is estimated as Model 1 but with off-balance sheet items as an additional output; Model 3 is estimated as Model 1 but with loan loss provisions as an additional input; Model 4 is estimated as Model 1 but with off-balance sheet items as an additional output and loan loss provisions as an additional input.

30

Table 3 – DEA results with profit oriented approach (Model 5) TE (VRS) Mean 0.924 0.975 0.944 0.946 0.968 0.950

2004 (N =18) 2003 (N =17) 2002 (N = 17) 2001 (N = 14) 2000 (N = 12) Overall (2001-2004; N = 78)

SE Mean 0.978 0.976 0.942 0.945 0.966 0.960

Notes: TE: technical efficiency, SE: scale efficiency, CRS: constant return on scale, VRS: Variable returns on scale; Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs

31

Table 4 –Purely domestic versus international Greek banks N

PDBs Model 4 TE (VRS)

SE

Model 5 TE

SE

Model 4 TE (VRS)

IGBs Model 5 SE TE (VRS)

SE

Mean

Mean

Mean

Mean

(VRS)

Mean

Mean

Panel A: operations abroad through subsidiaries (SIGBs) 2004 11 PDBs / 7 SIGBs 0.924 0.992 2003 10 PDBs / 7 SIGBs 0.929 0.967 2002 11 PDBs / 6 SIGBs 0.970 0.954 2001 8 PDBs / 6 SIGBs 0.987 0.993 2000 6 PDBs / 6 SIGBs 0.959 0.981 Overall 46 PDBs / 32 SIGBs 0.951 0.976 (2001-2004)

Mean 0.914 0.978 0.917 0.916 0.936 0.932

Mean

0.973 0.967 0.911 0.900 0.989 0.946

0.941 0.991 0.999 1.000 0.999 0.985

0.998 0.979 0.966 1.000 0.968 0.982

0.941 0.971 0.993 0.987 1.000 0.977

0.985 0.999 0.974 0.971 0.969 0.980

Panel B: Operations abroad through subsidiaries and branches (SBIGBs) 2004 10 PDBs / 8 SBIGBs 0.916 0.991 0.905 0.970 2003 9 PDBs / 8 SBIGBs 0.937 0.964 0.976 0.963 2002 9 PDBs / 8 SBIGBs 0.964 0.959 0.938 0.908 2001 7 PDBs / 7 SBIGBs 0.985 0.992 0.903 0.886 2000 5 PDBs / 7 SBIGBs 0.950 0.977 0.923 0.986 Overall 40 PDBs / 38 SBIGBs 0.948 0.976 0.930 0.942 (2001-2004)

0.948 0.974 0.999 1.000 0.999 0.983

0.998 0.981 0.957 1.000 0.972 0.981

0.949 0.975 0.951 0.988 1.000 0.971

0.987 0.999 0.962 0.975 0.973 0.979

Notes: PDBs: Purely domestic banks, IGBs: International Greek Banks; TE: technical efficiency, SE: scale efficiency, VRS: Variable returns on scale; Model 4 is estimated with fixed assets, customer deposits & short term funding, number of employees and loan loss provisions as inputs, and loans, other earning assets and off-balance sheet items as outputs; Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs

32

Table 5 – Tobit censored regression results Model 5

Model 4 SE

TE Coef.

p-value

Coef.

TE p-value

Coef.

SE p-value

Coef.

p-value

Panel A: TE & SE regressed over bank financial characteristics EQAS ROAA LOANS POWER

0.065537 0.005145 0.006359 0.038415

0.0000 0.8566 0.0031 0.0000

0.030122 0.010771 0.011457 0.021705

0.0000 0.7022 0.0000 0.0000

0.049830 0.120047 0.009719 0.025861

0.0000 0.0011 0.0000 0.0000

0.019114 0.071777 0.013829 0.015411

0.0007 0.0567 0.0000 0.0000

Panel B: TE & SE regressed over strategic decisions related variables ATM 0.001783 0.2603 0.000312 0.7624 0.001306 0.3930 -0.000336 BRANCH 0.005875 0.0805 0.004469 0.0017 0.010266 0.0083 0.006977 BR_ABR -0.641537 0.4266 -0.363895 0.5715 -1.715891 0.0309 -0.489866 SUB_ABR 0.768489 0.0285 0.725462 0.0007 0.711865 0.1073 0.330059

0.7571 0.0051 0.4573 0.1962

Notes: N=78 observations; TE: technical efficiency, SE: scale efficiency; Model 4 is estimated with fixed assets, customer deposits & short term funding, number of employees and loan loss provisions as inputs, and loans, other earning assets and off-balance sheet items as outputs, Model 5 is estimated with employee expenses, other non interest expenses and loan loss provisions as inputs, and net interest income, net commission income and other operating income as outputs; EQAS: equity to assets, ROAA: return on average assets, LOANS: loans to assets, POWER: market share in terms of total assets, ATM: the number of bank’s ATMs, BRANCH: the number of bank’s branches, BR_ABR: dummy variable that equals 1 if the bank has branches abroad and 0 otherwise, SUB_ABR: dummy variable that equals 1 if the bank has subsidiaries abroad and 0 otherwise; QML (Huber/White) standard errors and covariates have been calculated to control for heteroscedacity

33

University of Bath School of Management Working Paper Series Past Papers School of Management Claverton Down Bath BA2 7AY United Kingdom Tel: +44 1225 826742 Fax: +44 1225 826473 http://www.bath.ac.uk/management/research/papers.htm 2005 2005.01

Bruce A. Rayton

Specific Human Capital as an Additional Reason for Profit Sharing

2005.02

Catherine Pardo, Stephan C. Henneberg, Stefanos Mouzas and Peter Naudè

Unpicking the Meaning of Value in Key Account Management

2005.03

Andrew Pettigrew and Stephan C. Henneberg (Editors)

Funding Gap or Leadership Gap – A Panel Discussion on Entrepreneurship and Innovation

2005.04

Robert Heath & Agnes Nairn

Measuring Affective Advertising: Implications of Low Attention Processing on Recall

2005.05

Juani Swart

Identifying the sub-components of intellectual capital: a literature review and development of measures

2005.06

Juani Swart, John Purcell and Nick Kinnie

Knowledge work and new organisational forms: the HRM challenge

2005.07

Niki Panteli, Ioanna Tsiourva and Soy Modelly

Intra-organizational Connectivity and Interactivity with Intranets: The case of a Pharmaceutical Company

2005.08

Stefanos Mouzas, Stephan Henneberg and Peter Naudé

Amalgamating strategic possibilities

2005.09

Abed Al-Nasser Abdallah

Cross-Listing, Investor Protection, and Disclosure: Does It Make a Difference: The Case of Cross-Listed Versus Non-Cross-Listed firms

2005.10

Richard Fairchild and Sasanee Lovisuth

Strategic Financing Decisions in a Spatial Model of Product Market Competition.

2005.11

Richard Fairchild

Persuasive advertising and welfare in a Hotelling market.

2005.12

Stephan C. Henneberg, Catherine Pardo, Stefanos Mouzas and Peter Naudé

Dyadic ‘Key Relationship Programmes’: Value dimensions and strategies.

34

2005.13

Felicia Fai and Jing-Lin Duanmu

Knowledge transfers, organizational governance and knowledge utilization: the case of electrical supplier firms in Wuxi, PRC

2005.14

Yvonne Ward and Professor Andrew Graves

Through-life Management: The Provision of Integrated Customer Solutions By Aerospace Manufacturers

2005.15

Mark Ginnever, Andy McKechnie & Niki Panteli

A Model for Sustaining Relationships in IT Outsourcing with Small IT Vendors

2005.16

John Purcell

Business strategies and human resource management: uneasy bedfellows or strategic partners?

2005.17

Richard Fairchild

Managerial Overconfidence, Moral Hazard, and Financing and Investment Decisions

2005.18

Wing Yee Lee, Paul Goodwin, Robert Fildes, Konstantinos Nikolopoulos, & Michael Lawrence

Providing support for the use of analogies in demand forecasting tasks

2005.19

Richard Fairchild and Sasanee Lovisuth

Product Differentiation, Myopia, and Collusion over Strategic Financing Decisions

2005.20

Steven Brammer, Andrew Millington & Bruce Rayton

The Contribution of Corporate Social Responsibility to Organisational Commitment

2005.21

Richard Fairchild and Ganggang Zhang

Repurchase and Dividend Catering, Managerial Myopia, and Long-run Value-destruction

35