efficiency in the greek banking industry: a comparison of foreign and domestic banks

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Efficiency in the Greek BankingIndustry: A Comparison of Foreign andDomestic BanksChrysovalantis Gaganis a b & Fotios Pasiouras ca Department of Production Engineering and Management ,Technical University of Crete , Chania 73100, Greeceb Visiting Fellow, School of Management , University of Bath ,Bath, BA2 7AY, UKc School of Management , University of Bath , Bath, BA2 7AY, UKPublished online: 18 Jun 2009.

To cite this article: Chrysovalantis Gaganis & Fotios Pasiouras (2009) Efficiency in the GreekBanking Industry: A Comparison of Foreign and Domestic Banks, International Journal of theEconomics of Business, 16:2, 221-237, DOI: 10.1080/13571510902917533

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Int. J. of the Economics of Business,Vol. 16, No. 2, July 2009, pp. 221–237

1357-1516 Print/1466-1829 Online/09/020221–17© 2009 International Journal of the Economics of Business

DOI: 10.1080/13571510902917533

Efficiency in the Greek Banking Industry: A Comparison of Foreign and Domestic Banks

CHRYSOVALANTIS GAGANIS and FOTIOS PASIOURASTaylor and FrancisCIJB_A_391925.sgm10.1080/13571510902917533International Journal of the Economics of Business1357-1516 (print)/1466-1829 (online)Original Article2009Taylor & Francis162000000July 2009Dr [email protected]

ABSTRACT This study uses a sample of foreign and domestic banks operating in Greeceduring 1999–2004 to examine the impact of ownership on efficiency. We estimate aninput oriented data envelopment analysis (DEA) model under variable returns to scalewith inputs and outputs selected on the basis of a profit-oriented approach. The resultsindicate an average pure technical efficiency equal to 0.7325 showing that the banks insample could improve their efficiency by 26.75%. Over the same period, scale efficiencywas equal to 0.6830. The comparison of the efficiency scores by group of ownership showsthat domestic banks have higher pure technical efficiency and lower scale efficiency;however, the differences are not statistically significant. A DEA window-analysisconfirms the results of the cross-section estimations. We also estimate a Tobit regressionmodel but consistent with the univariate results we find no evidence to support theargument that ownership has a statistically significant impact on efficiency.

Key Words: Banks; DEA; Efficiency; Foreign; Greece.

JEL classifications: D61, G21.

1. Introduction

Over the last years, several banking sectors in both developed and developing coun-tries witnessed an increase in foreign banks’ entry. This trend generated a debate inthe literature as to the potential benefits and costs of foreign banks’ entry for thedomestic market (Levine, 1996; Stiglitz, 1993; Peek and Rosengren, 2000). It alsoresulted in a number of empirical studies that examined the performance of foreign

We would like to thank two anonymous referees, and Eleanor Morgan (Editor), for valuablecomments that helped us improve an earlier version of the manuscript. Any remaining errors are, ofcourse, our own.Chrysovalantis Gaganis, Department of Production Engineering and Management, Technical University ofCrete, Chania 73100, Greece, and Visiting Fellow, School of Management, University of Bath, Bath BA2 7AY,UK; e-mail: [email protected]. Fotios Pasiouras, corresponding author, School of Management, University ofBath, Bath BA2 7AY, UK; e-mail: [email protected]

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222 C. Gaganis and F. Pasiouras

banks. One strand of this literature focused on the determinants of size and profitsof foreign banks (e.g. Williams, 1998; Kosmidou et al., 2007).

Another strand of the literature approached the performance of banks from adifferent perspective by comparing the efficiency of foreign and domestic institu-tions. The majority of the early studies on foreign banks’ efficiency focus ondeveloped markets and most of them examine the US (e.g. DeYoung and Nolle,1996; Hasan and Hunter, 1996) and more recently Australia (Sathye, 2001; Sturmand Williams, 2004) or selected European countries such as Spain (Hasan andLozano-Vivas, 1998) and France, Germany, Spain, UK (Berger et al., 1999). Agrowing number of recent studies focuses on developing countries, such as CzechRepublic and Poland (Weill, 2003), Croatia (Kraft et al., 2006), United ArabicEmirates-UAE (Rao, 2005), India (Shanmugam and Das, 2004; Sensarma, 2006),Poland (Havrylchyk, 2006), and Thailand (Okuda and Rungsomboon, 2006).Furthermore, Yildirim and Philippatos (2007) adopt a cross-country setting andconsider various Central and Eastern European economies.

The present study provides additional evidence by comparing the efficiencyof foreign and domestic banks in Greece. Although there have been severalstudies that examine the efficiency of domestic banks in Greece,1 to the best of ourknowledge no study examines the efficiency of foreign banks. Analysing theefficiency of both foreign and domestic banks is important due to their intermedi-ation role in the Greek financial market. These banks are responsible for theefficient allocation of funds to firms to finance their investments so improvementsin their ability to transform inputs like savings and deposits to outputs like loanswill result in better outcomes for firms and the economy as a whole.2 While theGreek market is not dominated by foreign banks, these banks account for approx-imately 10% of the total assets of the industry. Therefore, their presence increasesthe competition in the industry, forcing domestic banks to be more efficient.Furthermore, foreign banks usually introduce state-of-the-art banking technolo-gies that may be copied by domestic banks leading to higher overall efficiencylevels. Similarly, foreign banks may contribute to the quality of human capital inthe domestic banking industry either by importing relatively highly skilledmanagers or by training local employees (Lensink and Hermes, 2004), leading to amore efficient transformation of inputs to outputs.

The Greek banking sector provides an interesting context to examine bankefficiency and compare foreign and domestic banks for two reasons. First, whilebanks in Greece operate in a developed country, the banking industry is smallwhen compared to both European and international standards. This distinguishesour study from past research that compares foreign and domestic banks’efficiency while focusing on developing markets or considerably larger bankingsectors in developed markets (e.g. Spain, UK and US). Furthermore, Greekcustomers show a continued preference for transactions through branches andthe Greek banking system relies heavily on them as a distribution network. Inturn, this may result in a disadvantage for foreign banks, which usually rely onalternative distribution channels (e.g. internet banking, phone banking, ATMs) ordo not have an extended branch network. A second interesting aspect of theGreek banking industry is that in recent years, domestic banks have expandedtheir product/service portfolio to include activities such as insurance, brokerageand asset management, and they improved their communication and computingtechnology, and their credit risk measurement and management systems (Bank ofGreece, 2004). These improvements in the operations of domestic banks may have

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Efficiency in the Greek Banking Industry 223

diminished any competitive advantage that foreign banks had in terms ofsuperior managerial skills, risk management or best-practice processes. Conse-quently, the results of our analysis could offer additional insights as to theefficiency of foreign banks compared to domestic banks and be of special interestto several stakeholders such as customers, bank managers and, of course, bankregulators.

The rest of the paper is as follows. Section 2 provides a background discus-sion. Section 3 presents the sample and methodology. Section 4 discusses theresults, and section 5 concludes the study.

2. Background Discussion

2.1 Theoretical Discussion

The literature on international business and management argues that foreignfirms experience additional costs due to unfamiliarity with the foreign environ-ment, what is known as ‘the liability of foreignness’ (Hymer, 1960, published1976). However, to overcome the liability of foreignness and compete successfullyagainst local firms, multinational firms may direct resources to their overseasunits providing them with a competitive advantage in the form of organizationalor managerial capabilities (e.g. Zaheer, 1995).

Berger et al. (1999) link these views with the bank efficiency literature andoffer two potential explanations for possible differences in efficiency betweenforeign and domestic banks. According to the home field advantage hypothesis,domestic banks are generally more efficient than foreign banks. As Berger et al.(1999) argue ‘The home field advantage may be manifested as disadvantages toforeign banks in terms of higher costs of providing the same financial services orlower revenues from problems in providing the same quality and variety ofservices as domestic institutions’ (p. 3). They mention that the main factorsunderlying this disadvantage are organizational diseconomies from operating ormonitoring an institution from a distance. Furthermore, differences in language,culture, currency, regulatory and supervisory structures, other country-specificmarket features, bias against foreign institutions or other explicit or implicitbarriers are highlighted as potential determinants of the home field advantage.

The global advantage hypothesis argues that some foreign banks are able to over-come the cross-border disadvantages and operate more efficiently than theirdomestic counterparts. Berger et al. (1999) mention that these organizations mayhave higher efficiency when operating in other nations by (i) spreading their supe-rior managerial skills or best-practice policies and procedures over more resourcesso lowering costs and (ii) raising revenues through superior investment or riskmanagement skills, by providing superior service quality/variety that somecustomers prefer, or by obtaining diversification of risks that allows them to under-take higher risk-higher expected return investments. Furthermore, Berger et al.(1999) discuss the following two forms of the global advantage hypothesis: (i) thegeneral form, under which efficiently managed foreign banks headquartered inmany nations are able to overcome any cross-border disadvantages and operatemore efficiently than domestic banks in other nations; and (ii) the limited form inwhich only efficiently managed foreign banks headquartered in nations withspecific favourable conditions in their home countries are able to overcome cross-border disadvantages and operate more efficiently than the domestic banks.

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2.2 Empirical Evidence

The majority of the US studies, such as the ones of Hasan and Hunter (1996),Mahajan et al. (1996) and Chang et al. (1998) find that foreign banks are less costefficient than domestic banks. DeYoung and Nolle (1996) find foreign banks to beless profit efficient. The results in the Australian banking sector are mixed. Sathye(2001) employ data envelopment analysis (DEA) to estimate the efficiency of 17domestic and 12 foreign banks operating in Australia during 1996. The resultsindicate that foreign banks are more efficient than domestic ones but the differ-ences are not statistically significant. In contrast, Sturm and Williams (2004) useDEA, Malmquist indices and stochastic frontier analysis, and find that foreignbanks are more efficient than domestic banks but this does not result in superiorprofits.

The results from the EU banking sectors are also mixed. Hasan andLozano-Vivas (1998) indicate that foreign banks in Spain are almost as profitefficient as domestic banks. Berger et al. (1999) estimate cost and profit frontiersto compare the efficiency of foreign and domestic banks in France, Germany,Spain, the UK and the US. With respect to the EU countries, they find that costefficiency and profit efficiency are higher for domestic banks in three cases(France, Germany, UK) but the differences are not statistically significant.Kosmidou et al. (2004) use a multicriteria methodology to examine the UKbanking sector between 1998 and 2001. They find that domestic banks performbetter than foreign ones.

For the transition economies of Central and Eastern Europe, the resultsgenerally indicate that foreign banks are more efficient than the domestic ones.Weill (2003) uses the stochastic frontier approach to compute the cost efficiencyscores in Czech Republic and Poland during 1997 and finds that foreign-ownedbanks are more efficient than domestic-owned banks. Kraft et al. (2006) findthat foreign banks have substantially higher efficiency scores than domesticbanks in Croatia between 1994 and 2000. Havrylchyk (2006) estimates averageefficiencies in Poland which are equal to 52.92% (domestic banks) and 73.23%(foreign banks) and indicates that foreign banks have exhibited higher produc-tivity of their inputs and are superior in choosing the right mix of inputs inlight of given prices. Bonin et al. (2005) use data from 1996 to 2000 to investi-gate the effects of ownership on bank efficiency for four northern Europeancountries, four southern European countries, and three Baltic countries, a totalof eleven countries in transition. They find that a majority foreign ownershiphas a positive impact on both cost and profit efficiency. A strategic foreignowner has also a positive impact on cost efficiency whereas an internationalinstitutional investor generates an additional positive impact on profitefficiency. Fries and Taci (2005) examine 15 East European countries over theperiod 1994–2001 and find that among different ownership structures, priva-tised banks with majority foreign ownership are the most cost efficient ones.Yildirim and Philippatos (2007) examine 12 Central and Eastern Europeancountries between 1993 and 2000. They find that foreign banks are more costefficient but less profit efficient relative to domestically owned private banksand state-owned banks.

Results from other emerging markets such as UAE also indicate that domesticbanks are less cost-efficient than their foreign counterparts (Rao, 2005).Shanmugam and Das (2004) examine India during 1992–1999 and report that state

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and foreign banks are more efficient than private domestic and nationalizedbanks. In contrast, Sensarma (2006) reports that foreign banks have been theworst performers throughout the period 1986–2000 as compared with state-owned and private-owned banks. In the case of Latin America, the results aremixed. Barajas et al. (2000) find that foreign banks are more productive thandomestic ones whereas Crystal et al. (2001) report small differences in the perfor-mance of foreign and domestic banks.

3. Methodology and Sample

3.1 Data Envelopment Analysis (DEA)

As in several recent studies, we use DEA that is a mathematical programmingapproach for the development of production frontiers and the measurementof efficiency relative to these frontiers (Charnes et al., 1978).3 We only brieflyoutline DEA here, while more detailed and technical discussions can be found inThanassoulis (2001) and Coelli et al. (2005). The notations adopted below are thoseused in Coelli (1996) and Coelli et al. (2005) since we use their computer programDEAP 2.1 to estimate the efficiency scores.

The best-practice production frontier is constructed through a piecewiselinear combination of actual input-output correspondence set that envelops theinput-output correspondence of all banks in the sample (Thanassoulis, 2001).The efficiency for each bank in sample ranges between 0 and 1, with higherscores indicating higher efficiency with respect to other banks in the sample.DEA efficiency estimates can be obtained by assuming either constant returns toscale (CRS) or variable returns to scale (VRS). In their seminal study, Charneset al. (1978) proposed a model that had an input orientation and assumed CRS.Hence, the output of this specification is a score indicating the overall technicalefficiency.

We assume that there is a dataset of K inputs and M outputs for each one ofthe N banks. For the i-th bank these are represented by the vectors xi and yi,respectively. The K × N input matrix, X, and the M × N output matrix, Y, representthe data for all N banks. The input oriented measure of a particular DMU, underCRS, is calculated as:

where θ ≤ 1 is the scalar efficient score and λ is N × 1 vector of constants. If θ = 1the bank is efficient as it lies on the frontier, whereas if θ π 1 the bank is inefficientand needs a 1 − θ reduction in the inputs levels to reach the frontier. The linearprogramming is solved N times, once for each bank in sample, and a value of θ isobtained for each bank representing its efficiency score.

Banker et al. (1984) suggested the use of VRS that decomposes overalltechnical efficiency into a product of two components. The first is technicalefficiency under VRS or pure technical efficiency (PTE) and relates to the abilityof managers to utilize firms’ given resources. The second is scale efficiency (SE)

Min

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and refers to exploiting scale economies by operating at a point where theproduction frontier exhibits CRS. The CRS linear programming is modified toconsider VRS by adding the convexity N1′λ=1, where N1 is a N×1 vector ofones.

In the present study, the efficiency scores were obtained under the VRSassumption and through an input-oriented model. We assume VRS because CRSis appropriate only when all firms are operating at an optimal scale and there aremany reasons that may not allow a firm to do so, including imperfect competi-tion, government regulations, constraints on finance, etc. (Coelli et al., 2005). AsDrake et al. (2006) point out ‘from the perspective of an input-oriented DEArelative efficiency analysis, the more efficient units will be better at minimizingthe various costs incurred in generating the various revenue streams and,consequently, better at maximizing profits’ (p. 1451).

One of the advantages of DEA that motivated us to select it over alternativefrontier techniques (e.g. stochastic frontier analysis) is that it works relatively wellwith small samples. As Maudos et al. (2002) point out, ‘Of all the techniques formeasuring efficiency, the one that requires the smallest number of observations isthe non-parametric and deterministic DEA, as parametric techniques specify a largenumber of parameters, making it necessary to have available a large number ofobservations’ (p. 511). Other advantages of DEA are that it does not require anyassumptions about the distribution of inefficiency and there is no need to specify aparticular functional form on the data in determining the most efficient banks.However, DEA is also subject to few limitations. For instance, it assumes data to befree of measurement error and it is sensitive to outliers. Furthermore, Coelli et al.(2005) also point out that having few observations and many inputs and/or outputswill result in many firms appearing on the DEA frontier, an issue known as the‘self-identifiers’ problem.

3.2 Sample

Our sample includes all banks with available financial information in the officialwebsite of the Hellenic Bank Association. It consists of 18 foreign and 21 domesticbanks operating in Greece between 1999 and 2004.4 Our dataset is unbalanced inthe sense that observations are not available for all banks in all years. The samplesize by year is as follows: 19 (1999), 19 (2000), 20 (2001), 29 (2002), 37 (2003),36 (2004). Table 1 reveals the inclusion of banks in sample by ownership andyear.

Following recent studies such as Ataullah et al. (2004), Das and Ghosh (2006),Drake et al. (2006), Pasiouras (2008a) and Pasiouras et al. (2008) we select inputsand outputs on the basis of a profit-oriented approach (also termed the operatingor income based approach), which defines revenue components as outputs andcost components as inputs. Drake et al. (2006) point out that their results supportthe argument of Berger and Mester (2003) that a profit-based approach is betterable to capture the diversity of strategic responses by financial firms in the face ofdynamic changes in competitive and environmental conditions. In the presentstudy, we use two inputs and two outputs. The two inputs are staff expenses (X1)and other administrative expenses (X2). The two outputs are net interest income(Y1) and net commission income plus other operating income (Y2). These inputsand outputs were selected after considering data availability and potential ‘self-identifiers’ problems.5

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4. Empirical Results

4.1 Base Results

Table 2 presents mean values for the inputs and outputs of banks by ownership andyear. Table 3 presents the efficiency scores obtained from a cross-section commonfrontier, estimated for foreign and domestic banks by year.

Pure technical efficiency (PTE) ranges between 0.6279 (2004) and 0.7857 (1999)with an average equal to 0.7325 over the period of our analysis. Thus, banks oper-ating in Greece could improve their efficiency by 26.75% on average or, in otherwords, they could have used only 73.25% of the resources actually employed (i.e.expenses) to produce the same level of outputs (i.e. revenues). Scale efficiency(SE) ranges between 0.5772 (2001) and 0.8703 (1999) with an average equal to0.6830, indicating that the banks in sample deviated 31.70% (on average), fromtheir efficient size of scale. Turning to the comparison between foreign anddomestic banks, the results indicate that domestic banks are more technically effi-cient in almost all cases (with the exception of 2000) resulting in an average PTEequal to 0.7583 for domestic banks and 0.6906 for foreign banks. By contrast,foreign banks are more scale efficient (with the exception of 1999) with averagesover the entire period being 0.7220 (foreign banks) and 0.6589 (domestic banks).

To examine whether the differences between the foreign and domestic banksare statistically significant, we use a Kruskal-Wallis (K-W) non-parametric test.Due to the small number of observations from each group by year, the test isperformed on the scores of the pooled sample (i.e. 160 observations). The chi-square values are equal to 2.328 (PTE) and 2.256 (SE) indicating that the meandifferences, both in the case of PTE and SE, are not statistically significant.

Table 1. Observations by ownership and year

Year Ownership Number of banks in sample

1999 Foreign 6Domestic 13All 19






Total (1999–2004) Foreign 61Domestic 99All 160

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Table 2. Mean values of inputs and outputs (figures in million euros)

Y1 Y2 X1 X2

1999 All 156.86 144.57 83.86 36.93Foreign 100.16 24.63 14.27 14.93Domestic 183.03 199.93 115.98 47.08






Total sample All 173.61 82.50 82.96 41.49Foreign 61.46 16.30 13.28 14.20Domestic 247.07 123.28 125.89 58.31

Y1: net interest income, Y2: net commission income plus other operating income, X1: staff expenses, X2:other administrative expenses.

Table 3. DEA efficiency scores

Year Ownership N PTE SE

1999 All 19 0.7857 0.8703Foreign 6 0.6997 0.8033Domestic 13 0.8255 0.9012






Average (1999–2004) All 160 0.7325 0.6830Foreign 61 0.6906 0.7220Domestic 99 0.7583 0.6589

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Table 4 presents the number of banks that experience increasing returns (IRS),constant returns (CRS), or decreasing returns to scale (DRS). Looking at the fullsample, only 14.38% (i.e. 23 out of 160) of the banks are scale efficient whereas themajority (85.63%) is scale inefficient (i.e. IRS, DRS). Of the scale inefficient banks,27.50% experience IRS and as many as 58.13% experience DRS. Observation of theresults by group of ownership, shows that the within-group scale efficient banksremain at around 14%. That is 9 out of the 61 foreign banks (14.75%) and 14 out ofthe 99 domestic banks (14.14%) experience CRS. However, the comparison of thetwo groups in terms of the banks that experience IRS and DRS reveals a differentpicture. More detailed, 44.26% of the foreign banks operate under IRS (27 out of the61) and 40.98% operate under DRS (25 out of the 61). The corresponding figures fordomestic banks are 17.17% (17 out of 99) and 68.69% (68 out of 99).

Figures 1 and 2 plot the efficiency measures against the logarithm of total assets(logas) as in Kumbhakar and Tsionas (2008) among others.6 In the present study, weuse LOESS (i.e. locally weighted polynomial regression), a non-parametricapproach that is useful for fitting smooth curves to scatterplots (Cleveland, 1979;Cleveland and Devlin, 1988). As Jacoby (2000) points out the strength of nonpara-metric smoothers is their flexibility as to the exact nature of the relationshipbetween the variables, that they are relatively local, and that they are less sensitiveto discrepant points within the scatterplot.Figure 1. Plot of PTE against log (total assets), LOESS fit (α =0.5)Figure 2. Plot of SE against log (total assets), LOESS fit (α =0.5)LOESS requires the specification of two parameters. The first is the bandwithspan, α, which takes values between 0 and 1 and determines which observationsshould be included in the local regressions. This span controls the smoothness ofthe local fit, with a larger α giving a smoother fit. A low value of α, may result in

Table 4. Number of banks by returns to scale

IRS CRS DRS Total

1999 All 10 5 4 19Foreign 5 0 1 6Domestic 5 5 3 13






Total sample 1999–2004 All 44 23 93 160Foreign 27 9 25 61Domestic 17 14 68 99

IRS: Increasing returns to scale; CRS: Constant returns to scale; DRS: Decreasing returns to scale.

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(a) Linear polynomial

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Figure 1. Plot of PTE against log (total assets), LOESS fit (α =0.5).

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(a) Linear polynomial

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local regressions sensitive to noise variation within the data values, while a highvalue can provide a very smooth curve which however fails to pass through thecentre of the cloud. Jacoby (2000) suggests that the value of α should usually bebetween 0.40 and 0.80, although that depends on the nature of the bivariaterelationships between the variables and the noise in the data. In the present study,we set α equal to 0.5 which provides a reasonable compromise between over-and-under smoothing (see Bell et al., 1996).7 The second parameter is the degree of thepolynomial, λ, to fit in each local regression. As discussed in Jacoby (2000) in thecase of monotonic patterns, λ should be set equal to 1 for locally linear fitting.However, if the data show a nonmonotone pattern, with local minima and/ormaxima, then λ should be set equal to 2 for locally quadratic equations. Wepresent the results both with linear (Figures 1a and 2a) and quadric (Figures 1band 2b) equations. Figure 1 shows that while PTE is reported throughout all assetsizes, there appears to be a U shaped relationship between total assets andefficiency. Figure 2 indicates an inversed U shaped relationship between totalasses and scale efficiency, with SE diminishing when logas exceeds 3.

4.2 Robustness Tests

As discussed before, one of the drawbacks of DEA is that small samples can leadto the ‘self-identifiers’ problem. Although the yearly estimations performed in theprevious section meet various rules of thumb, we test the robustness of ourresults further using the window technique suggested by Charnes et al. (1985) andadopted in banking applications by Webb (2003), Avkiran (2004), Asmild et al.(2004), Sufian and Majid (2007) and Abdul Majid et al. (2008).8 This window anal-ysis uses a series of pooled models in which data of a given bank in different timeperiods are treated as different entities.

We use a window width of three years which is consistent with the originalwork by Charnes et al. (1985) as well as recent studies by Sufian and Abdul Majid(2007), Abdul Majid and Sufian (2008) and Chung et al. (2008) among others.9 Thismeans that observations are compared to other observations within a three-yeartime period. This time period is small to minimize the problem of unfair compari-sons over time while providing a quite sufficient sample size. The first windowincorporates 1999, 2000, and 2001. As each bank is considered to be a differententity, this increases the sample to 58 observations. Then, we shift the window byone year, and we introduce a new year while dropping the earliest year. Thus,the second window consists of observations from 2000, 2001 and 2002 (i.e. 68observations). Similarly, the third window includes observations from 2001–2003(i.e. 86 observations) and the last window incorporates 2002–2004 (i.e. 102 obser-vations). Consequently, this approach ensures a higher degree of freedom thanthe cross-section estimations performed earlier and can be useful in detectingtrends in performance over time. The results of the DEA window-analysisconfirm that domestic banks are more pure technical efficient and less scaleefficient than foreign banks, however with the exception of SE in window 2, thereare no significant differences between the two groups in any of the fourwindows.10

As a second robustness test that allows us to explore further the impact ofownership on efficiency, we estimate an econometric model as in Sathye (2001),Sturm and Williams (2004), Weill (2003), Havrylchyk (2006), Yildirim andPhilippatos (2007). Hence we estimate a Tobit regression using PTE as the

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dependent variable and ownership (Foreign = 1; Domestic = 0) as the independentvariable, while controlling for bank’s size and time.11 We use Tobit instead of OLSdue to the limited nature of our dependent variable that takes values between 0 and1. As Saxonhouse (1976) points out, heteroscedasticity can emerge when estimatedparameters are used as dependent variables in the second stage analysis.Consequently, as in Hauner (2005) and Pasiouras (2008a, 2008b), QML (Huber/White) standard errors and covariates are calculated. The results in Table 5 show anegative but insignificant impact of foreign ownership on efficiency that isconsistent with the results of the Kruskal-Wallis (K-W) test.12

5. Conclusions

In recent years, most of the banking sectors around the world have witnessed anincrease in cross-border activities. This trend has attracted the attention of severalresearchers that conducted studies on the impact of foreign entry on the domesticmarkets as well as the performance of foreign banks. We add to the literature bycomparing the efficiency of foreign and domestic banks in Greece which providesan interesting context for various reasons. We estimated an input oriented DEAmodel under variable returns to scale, with inputs and outputs selected on thebasis of a profit-oriented approach.

We found that the average pure technical efficiency during 1999–2004 was0.7325 indicating that banks in Greece could improve their efficiency by 26.75%.Over the same period, scale efficiency was between 0.5772 and 0.8703 with anaverage equal to 0.6830. The comparison of the efficiency scores by group ofownership revealed that domestic banks were more technically efficient in almostall years. By contrast, foreign banks were more scale efficient. However, the resultsof a K-W test indicated that the mean differences, both in the case of PTE andSE, were not statistically significant. Using DEA window analysis instead of cross-

Table 5. Tobit Regression results

Constant 0.492**(0.045)

Ownership −0.027(0.7416)

Logas 0.123**(0.034)

Y2000 −0.052(0.712)

Y2001 0.022(0.899)

Y2002 −0.014(0.922)

Y2003 −0.009(0.941)

Y2004 −0.185(0.160)

Notes: N = 157; *** Statistically significant at the 1% level; ** Statistically significant at the 5% level; *Statistically significant at the 10% level; The dependent variable is pure technical efficiency;Ownership is a dummy variable taking the values of 1 for foreign banks and 0 for domestic banks;Logas is the logarithm of total assets; Y2000-Y2004 are year dummy variables; p-values are given inparentheses; QML (Huber/White) standard errors and covariates have been calculated.

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section estimations confirms these results. We also estimated a Tobit regressionmodel using pure technical efficiency as the dependent variable and ownership asthe independent variable, while controlling for size and time. Consistent with theresults of the K-W test, we found no evidence to support the argument that domes-tic banks are more efficient.

Notes

1. See for example Noulas (2001), Tsionas et al. (2003), Pasiouras (2008a), Pasiouras et al. (2008) andDelis et al. (2009), among others.

2. For example, using data from 1993 to 2000 on 49 nations, Berger et al. (2004) find that the cost andprofit efficiency ranks of community banks are associated with faster economic growth. In a morerecent study, Hasan et al. (2007) also provide evidence of a positive association between bank-levelprofit efficiency and regional economic growth, using a large sample of banks operating in 254different NUTS 2 regions in the EU-25. See Levine (2005) for a more general discussion on therelationship between efficiency of financial intermediaries and economic growth.

3. For other recent applications of DEA in banking see among several others Webb (2003), Havryl-chyk (2003), Drake et al. (2006), Pasiouras (2008a, 2008b).

4. Our analysis focuses on the 1999–2004 period due to data (un)availability in the website of theHellenic Bank Association. Data before 1999 and after 2004 were available only for a very limitednumber of cases, not allowing us to proceed to a meaningful analysis. Information from the 2004annual report of the Bank of Greece indicates that at end-2004 there were 23 foreign and 21 domes-tic banks in Greece, showing that our sample is quite representative.

5. As an anonymous referee suggested, additional inputs/outputs that could be considered are loanloss provisions (LLP) and off-balance-sheet items (OBS). Unfortunately, such information was notavailable in our case for foreign banks. Furthermore, including OBS could be inconsistent withpast studies that use the profit-oriented approach. To some extent, non-traditional activities suchas OBS are captured by net commission income as in Rogers (1998) and Stiroh (2000) amongothers. Furthermore, keeping the number of inputs and outputs low, allows us to meet variousrules of thumb. Soteriou and Zenios (1998) and Boussofiane et al. (1991) state that the number ofunits should be larger than the product of the number of inputs and outputs. Dyson et al. (2001)argue that the number of units should be at least twice the product of the number of inputs andoutputs. Nunamaker (1985) mentions that the sample size should be at least three times largerthan the sum of the number of inputs and outputs.

6. We would like to thank an anonymous referee for recommending this analysis.7. Experimenting with other values of α in the range of 0.4 to 0.8 does not alter the presented curve.8. We would like to thank an anonymous referee for suggesting this analysis.9. Since our sample covers fives years in total, it was not possible to consider a five-year window as

in Asmild et al. (2004) and Webb (2003). Charnes et al. (1985) actually use a window of 3 months,since their analysis is performed on a monthly basis between October 1981 and May 1982.

10. The p-values in the case of the K-W test were as follows: Window 1 (PTE: 0.218, SE: 0.275),Window 2 (PTE: 0.697, SE: 0.030), Window 3 (PTE: 0.471, SE: 0.263), Window 4 (PTE: 0.431, SE:0.121). To conserve space we do not present details on the efficiency scores, which are availablefrom the authors upon request.

11. Bank’s size is measured by the logarithm of total assets. As for time, we use dummy variables with1999 as the base year. Several other bank specific characteristics could be potentially used in theregression as control variables. However, we include only size and ownership for two reasons. First,to minimize (to the extent that it is possible) potential endogeneity problems. Second, to preservethe quality of the regression due to the small number of observations in the sample (see Weill, 2003).During the Tobit regression we excluded 3 observations due to missing values for total assets.

12. The same results were obtained in a univariate Tobit regression with ownership being the onlyindependent variable.

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efficiency in the greek banking industry: a comparison of foreign and domestic banks

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