The impact of non-traditional activities on the estimation of bank efficiency: International evidence

Journal of Banking & Finance 34 (2010) 1436–1449

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The impact of non-traditional activities on the estimation of bank efficiency: International evidence Ana Lozano-Vivas a, Fotios Pasiouras b,c,* a

Department of Economics, University of Malaga, Spain Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, Greece c Centre for Governance and Regulation, University of Bath School of Management, UK b

a r t i c l e

i n f o

Article history: Received 24 March 2008 Accepted 7 January 2010 Available online 11 January 2010 JEL classification: G21 G28 Keywords: Efficiency Non-traditional activities Off-balance sheet Environmental variables Regulatory conditions

a b s t r a c t This paper investigates the relevance of non-traditional activities in the estimation of bank efficiency levels using a sample of 752 publicly quoted commercial banks from 87 countries around the world, allowing comparison of the impact of such activities under different levels of economic development, geographical regions and other country characteristics. We estimate both cost and profit efficiency of banks using a traditional function that considers loans and other earnings assets as the only outputs, and two additional functions to account for non-traditional activities, one with off-balance sheet (OBS) items and the other with non-interest income as an additional output. Controlling for cross-country differences in regulatory and environmental conditions, we find that, on average, cost efficiency increases irrespective of whether we use OBS or non-interest income, although the results for profit efficiency are mixed. Our results also reveal that while the inclusion of non-traditional outputs does not alter the directional impact of environmental variables on bank inefficiency, regulations that restrict bank activities and enhance monitoring and supervision provisions improve both cost and profit efficiency. Ó 2010 Elsevier B.V. All rights reserved.

1. Introduction Over recent years, numerous banks around the world have broadened their portfolio to offer non-traditional services. For instance, as Clark and Siems (2002) pointed out, off-balance sheet (OBS) activities such as loan origination, securitization, standby letters of credit, and derivative securities among others have been expanding at a rapid pace. As a result, the share of feebased and other non-interest income to total income has increased dramatically. Ten years ago, Rogers (1998) identified the estimation of frontier efficiency as an area of bank research that had largely ignored non-traditional activities, since most bank efficiency models and measures then were based essentially only on traditional balance sheet figures. Both Siems and Clark (1997) and Rogers (1998), among others, argued that models that ignored non-traditional outputs penalized against banks that are heavily involved in such activities, because resources used to produce these non-traditional services were included in the input vector without accommodating the relevant variables in the output vector. * Corresponding author. Address: Financial Engineering Laboratory, Department of Production Engineering and Management, Technical University of Crete, Greece. Tel.: +30 28210 37239; fax: +30 28210 69410. E-mail addresses: [email protected] (A. Lozano-Vivas), [email protected], [email protected] (F. Pasiouras). 0378-4266/$ - see front matter Ó 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2010.01.006

While some recent studies have addressed the issue of the increased importance of non-traditional activities, by including OBS items or non-interest income in the output vector (e.g. Altunbas et al., 2000), many other studies continue to estimate efficiency frontiers without accounting for such non-traditional activities (e.g. Lensink et al., 2008). Therefore, there is no general consensus in the literature as regards the definition of the relevant output vector. Furthermore, relatively few studies provide a comparison of models developed both with and without proxies for non-traditional activities, thereby offering a bleak picture of their relative significance in bank efficiency estimates. Most of the studies report that ignoring measures of non-traditional activities in the estimation of bank efficiency can be misleading, although at least two studies in the literature find little or no impact of OBS (Jagtiani et al., 1995; Pasiouras, 2008a) while the results of Clark and Siems (2002) are mixed, dependent on examination of cost or profit efficiency. As these studies investigate mainly the US (e.g. Siems and Clark, 1997; Rogers, 1998; Stiroh, 2000) and a few developed countries such as Spain (Tortosa-Ausina, 2003), Switzerland (Rime and Stiroh, 2003), Taiwan (Lieu et al., 2005), and Greece (Pasiouras, 2008a), our knowledge with regard to broader range of countries, in particular the transition and less developed countries of the world, remains limited. The purpose of the present study is to provide, for the first time to the best of our knowledge, international evidence about the

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relevance of non-traditional activities in the estimation of bank efficiency scores. Particularly, our cross-country analysis attempts to show whether banking diversification across the borders, at the margin, and away from traditional financial intermediation (margin) activities towards ‘‘off-balance-sheet” and fee income businesses affects bank efficiency levels. In doing so, we use stochastic frontier analysis to estimate bank-specific measures of cost and profit efficiency, both with and without the inclusion of non-traditional outputs. By using a sample of banks from 87 countries, we are also able to compare the impact of non-traditional activities across different levels of economic development (e.g. advanced, transition and developing) and across different geographic regions. Furthermore, we are able to incorporate other countrybased characteristics such as the relative presence of foreign and government owned banks in the market, the degree of shareholders’ protection, and the degree of stock and derivative markets’ development. We model bank efficiency using a global best-practice frontier.1 However, to make a global frontier meaningful for cross-country comparisons one has to account for country-specific differences in the environment in which the banks operate (Dietsch and LozanoVivas, 2000; Lozano-Vivas et al., 2002). This is possible using the approach of Battese and Coelli (1995) that allows the single-step estimation of efficiency, in which firm effects are directly influenced by a number of variables.2 In the present study, we control for various cross-country environmental factors such as macroeconomic conditions, concentration, activity in the banking sector, and the country’s overall level of development. Following recent studies (e.g. Pasiouras, 2008b; Pasiouras et al., 2009) we also control for regulations related to the three pillars of Basel II (i.e. capital adequacy requirements, official supervisory power, private monitoring), and restrictions on bank activities. Additionally, in differentiating our paper further from previous studies, we investigate how the above-mentioned environmental conditions affects (in)efficiency across different specifications of the model, resulting from the introduction of non-traditional outputs in the cost and profit equations. Our underlying motive for considering non-traditional outputs, juxtaposed with the regulatory and environmental conditions, is derived from the assumption that capital requirements, restriction on activities, and the disclosure of OBS items or non-interest income to regulators and the public may influence the decision of bank managers as regards their relative scale of on-and off-balance sheet activities. However, managers may not be equally efficient in producing and selling onbalance and off-balance sheet services which may thereby affect their overall operating efficiency. For example, dissimilarities in the inefficiency levels across our specifications would imply that different types of banks’ activities have a different effect on the technological frontier of banks (and consequently their efficiency), given the economic, regulatory and environmental conditions in which they operate.3

1 The first advantage of estimating a global frontier is that it increases the number of available observations. As Berger and Humphrey (1997) argue a second advantage is that ‘‘a frontier formed from the complete data set across nations would allow for a better comparison across nations, since the banks in each country would be compared against the same standard” (pp. 187–188). However, following the suggestion of one referee, we have also estimated separate (i.e. specific) frontiers by different levels of country’s development obtaining similar results. 2 See Pasiouras et al. (2009) and Koutsomanoli-Filippaki et al. (2009) for other recent applications of the Battese and Coelli (1995) model in cross-country banking studies. 3 For example, requirements in relation to the disclosure of OBS items to regulators and the public, implicitly captured by the official supervisory power and private monitoring measures that we use in our empirical analysis, could increase the monitoring of banks. This could motivate managers to take more prudent decisions and be more efficient in the generation of OBS services.

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We examine a sample of 4960 observations from 752 publicly quoted commercial banks during the period 1999–2006, and find evidence consistent with past studies that support the importance of including a measure of non-traditional activities in the cost function. However, the results for profit efficiency are less supportive, as the impact on efficiency in this case depends on the proxy used for non-traditional activities. Although non-interest income contributes to an increase in profit efficiency in all of the cases considered, OBS results indicate only a slight increase in profit efficiency (on average). Moreover, differences are observed in terms of the impact of non-traditional activities in banking performance across different levels of economic development, geographical regions and country-specific characteristics reflecting shareholder protection and stock and derivatives market development. Our results also reveal that while the inclusion of non-traditional outputs does not alter the directional impact of environmental variables on bank inefficiency, regulations that restrict bank activities and enhance monitoring and supervision provisions improve both cost and profit efficiency. The rest of the paper is structured as follows. Section 2 describes the methodology and Section 3 presents the dataset used in our study. Section 4 discusses the results, and Section 5 concludes the study.

2. Methodology To investigate the relevance of non-traditional activities on the estimation of banks’ efficiency, we specify models with and without non-traditional outputs. Given the international context of our analysis, the models are defined on the basis of a global (i.e. common) frontier in order to obtain an overview of the impact of non-traditional activities on bank efficiency around the world. As it is well known in the cross-border literature the assumption of a common frontier entails the definition of a benchmark that allows the banks from different countries to be compared against the same standard.4 This study employs the stochastic frontier approach (SFA) to generate cost and profit efficiencies for each bank along the sample during the period under analysis. More specifically, we estimate bank efficiency using the Battese and Coelli (1995) SFA model to obtain an unbiased systematic measure of efficiency across countries, based on the assumption that efficiency differences between banking industries are determined by country-specific characteristics.5 This specification allows us to control for general environmental factors by estimating simultaneously the parameters of the stochastic frontier and the inefficiency model. Such a specification is referred to as the single-step estimation procedure and is assumed to be superior to a two-step procedure, in which the (unadjusted) estimated efficiency scores obtained from the stochastic frontier are then regressed during a second step on a set of explanatory variables (see Coelli et al., 2005). We estimate two models, one for cost (Model A) and one for profit (Model B) efficiency. In each case, we estimate three specifications. Model A1 is a ‘‘traditional” cost model under the interme4 It should be pointed out that an alternative methodology would be to construct a meta-frontier that allows the calculation of technology gaps and adjusted efficiency scores. While this approach is quite interesting, it permits consideration of only those countries for which a sufficiently large number of observations is available (Bos and Schmiedel, 2007, p. 2088). In the present study, we focus on publicly quoted commercial banks only, and therefore the number of available banks per country is limited, which rules out the possibility of using meta-frontiers. 5 We follow recent cross-country studies (e.g. Dietsch and Lozano-Vivas, 2000; Lozano-Vivas et al., 2002) that account for differences arising from country-specific aspects of technology, macroeconomic conditions, and regulatory conditions by including indicators of these environmental factors in a more comprehensive definition of a common frontier.

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diation approach where we assume that banks have two outputs, namely loans (Q1) and other earning assets (Q2). In models A2 and A3 we consider (interchangeably) as an additional output the two most commonly used measures of banks’ non-traditional activity: the so-called OBS, which is as a measure of aggregated off-balance-sheet activity, and the non-interest income as proxy of the OBS fee and commission income.6,7 Thus, Model A2 is identical to Model A1 but with OBS (Q3a) used as an additional output. In Model A3, we replace OBS by non-interest income (Q3b). Models B1 to B3 are identical to Models A1-A3, but they correspond to profit rather than cost functions. Therefore, profits before taxes, (PBTit) replace total cost (TCit) as the dependent variable.8 As in most previous studies, we estimate an alternative profit frontier, which ignores output price data by assuming imperfect competition.9 Additionally, as in previous studies, since a number of banks in the sample exhibit negative profits (i.e. losses), the dependent variable in the profit model is transformed to lnðPBT þ jðPBTÞmin j þ 1Þ, where jðPBTÞmin j is the minimum absolute value of PBT over all banks in the sample. In all the specifications, we use three input prices. Consistent with most previous studies, these are: cost of borrowed funds (W1), calculated as the ratio of interest expenses to customer deposits and short term funding; cost of physical capital (W2), calculated by dividing overhead expenses other than personnel expenses by the book value of fixed assets; and cost of labour (W3), calculated by dividing the personnel expenses by total assets.10 To impose linear homogeneity restrictions we normalize the dependent variable and all input prices by W3. A time trend (T = 1 for 1999, T = 2 for 2000, . . ., T = 8 for 2006) is included in each specification to allow for technological change, using both linear and quadratic (i.e. T and T2) terms as in Lensink et al. (2008). Following Berger and Mester (1997), we specify equity as a quasi-fixed input to control for differences in risk preferences, which may arise due to regulation, financial distress, or informational asymmetries.11 However, raising equity is associated with higher costs than raising deposits and the mix of these liabilities can have a direct impact on cost (Berger and Mester, 1997). 6 We use the OBS figure reported in the global format of Bankscope, which is calculated as the summation of the nominal values of the following four categories: (1) acceptances, (2) documentary credits, (3) guarantees, (4) other contingent liabilities. Our definition of OBS is consistent with other recent studies such as Bos and Kolari (2005), and Bos and Schmiedel (2007). 7 As an anonymous referee argued, if fee income is generated by on-balance sheet activities, this would be already captured in other earning assets leading to a doublecounting in the models. Unfortunately, data (un)availability does not allow us to distinguish between income generated by on-balance and off-balance sheet activities. However, as Clark and Siems (2002) mention, in the case of large banks (as examined in this study) non-interest income is disproportionately generated as a result of OBS fee services. Our approach to use both other earning assets and non-interest income as outputs is consistent with prior studies (e.g. Tortosa-Ausina, 2003; Stiroh, 2000; Lieu et al., 2005). 8 Our approach is consistent with other cross-country studies (e.g. Bos and Kolari, 2005; Kasman and Yildirim, 2006). Since tax rates differ across countries, using profit after tax would possibly make banks in countries with higher rates to appear as less efficient while in fact they are not. 9 See Berger and Mester (1997) for a discussion of the conditions under which the alternative profit function may provide more useful information than the standard one. The choice of an alternative profit function in international studies is also supported by Kasman and Yildirim (2006). 10 In calculating W3, we use total assets rather than the number of employees due to data unavailability. Our approach is consistent with several other studies (e.g. Altunbas et al., 2000). 11 Berger and Mester (1997) argue that failure to control for equity could yield a scale bias, while the efficiency of banks could be mismeasured even if they behave optimally given their risk preferences. For more in-depth discussions, see Mester (1996), Berger and Mester (1997) and Berger and Bonaccorsi di Patti (2006). As suggested in Mester (1996), and consistent with Berger and Mester (1997), Altunbas et al. (2000), Rime and Stiroh (2003), among others, we use the level of equity rather than the equity to assets ratio.

As we perform our analysis for a wide sample of countries around the world, rather than introduce individual country dummy variables we assess for differences in the level of economic development across countries by classifying them in four groups and introduce dummy variables for each group. Thus, we define MADV, ADV and TRANS as dummy variables to indicate whether a country belongs to the group of major-advanced, advanced or transition economies, respectively. By default, the fourth group, comprising developing countries, forms the reference category, identified by putting zero values in all three dummy variables.12 Using the multi-product translog specification, the cost function in the case of Model A2 is given as:13

ln

  TC W1 ¼ b0 þ b1 lnðQ 1 Þ þ b2 lnðQ 2Þ þ b3 lnðQ3a Þ þ b4 ln W3 W3   W2 1 2 þ b6 ðlnðQ1ÞÞ þ b7 lnðQ 1Þ lnðQ 2Þ þ b5 ln W3 2 1 þ b8 lnðQ 1Þ lnðQ3a Þ þ b9 ðlnðQ2ÞÞ2 2   2 1 1 W1 þ b10 lnðQ2Þ lnðQ 3a Þ þ b11 ðlnðQ 3a ÞÞ2 þ b12 ln 2 2 W3       2 W1 W2 1 W2 þ b13 ln ln þ b14 ln W3 W3 2 W3     W1 W2 þ b16 lnðQ1Þ ln þ b15 lnðQ1Þ ln W3 W3     W1 W2 þ b17 lnðQ2Þ ln þ b18 lnðQ2Þ ln W3 W3     W1 W2 þ b19 lnðQ3a Þ ln þ b20 lnðQ 3a Þ ln W3 W3 1 2 þ b21 T þ b22 T þ b23 lnðQ 1ÞxT þ b24 lnðQ2ÞxT 2     W1 W2 xT þ b27 ln xT þ b25 lnðQ3a ÞxT þ b26 ln W3 W3 þ b28 lnðEQUITYÞ þ b29 MADV þ b30 ADV þ b31 TRANS þ ui;t þ v i;t :

ð1Þ

The Battese and Coelli (1995) models allow us to estimate simultaneously the parameters of the stochastic frontier and the countryspecific and bank-specific determinants of inefficiency in one step using maximum likelihood. Therefore, the inefficiency effects (ui,t)14 in Eq. (1) are specified as:

uit ¼ d0 þ d1 INF t þ d2 GDPGRt þ d3 ln ðEQUITY it Þ þ d4 CLAIMSt þ d5 C3t þ d6 CAPRQ t þ d7 SPOWERt þ d8 PRMONt þ d9 RESTRt þ d10 MADV t þ d11 ADV t þ d12 TRANSt þ wit :

ð2Þ

12 To assign countries in the four categories we combine information from the International Monetary Fund (IMF) and the European Bank for Reconstruction and Development (EBRD). IMF classifies countries in the following three categories: (i) major advanced economies, (ii) advanced economies, (iii) other emerging and developing economies. According to the EBRD classification, 29 countries can be characterized as transition economies (e.g. Czech Republic, Estonia, etc.). We therefore, create a fourth category (i.e. transition) that includes these countries. 13 Some other studies rely on the Fourier Flexible (FF) specification to estimate efficiency (e.g. DeYoung and Hasan, 1998; Rossi et al., 2009). Berger and Mester (1997) found that both the translog and the FF function form yielded essentially the same average level and dispersion of measure efficiency, and both ranked the individual banks in almost the same order. However, Altunbas and Chakravarty (2001) compare the FF and translog specifications and urge caution about the growing use of the former to investigate bank efficiency. We therefore, use the tranlog specification as in several other recent studies (e.g. Berger et al., 2009; Hasan et al., 2009). We arbitrarily select this model for presentation, the remaining models being subject to similar transformations. 14 For the case of profit the sign of the inefficiency term in Eq. (1) becomes negative (uit).

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In the present study, bank-level risk preferences, along with macroeconomic variables and other country-level variables that explain the peculiar features of each country’s banking industry – such as the structure and activity in the banking sector as well as regulatory conditions – are used to control for differences in bank inefficiency. We categorize these variables into three groups. The first group is called ‘economic conditions’ and includes measures of inflation rate and GDP growth. These indicators describe the main macroeconomic conditions under which the banks of each country are operating. Thus, INF is the annual rate of inflation and it is measured using the percentage change in the consumer price index. High inflation may affect behaviour and induce banks to compete through excessive branch networks, which deteriorates cost efficiency (Kasman and Yildirim, 2006) but at the same time has a positive impact on bank margins (Demirguc-Kunt et al., 2004). GDP growth is measured in real terms (GDPGR). Favourable economic conditions will affect the demand and supply of banking services positively, and may improve bank efficiency. The second group is called ‘banking conditions’, and include the volume of financial intermediation, bank equity level, and concentration. These variables give information on the features of the structure of the banking markets. Financial intermediation is measured by the bank claims (i.e. credit) to the private sector over GDP (CLAIMS). This is a measure of intermediation activity used in a number of recent studies (e.g. Pasiouras, 2008b). Higher intermediation ratios imply higher banking activity due to the increase of loans, and can result in higher efficiency. In turn, this higher efficiency obtained through higher CLAIMS can be linked with the risk preferences of the banks. To capture such issues we introduce banklevel equity (EQUITY). Altunbas et al. (2000) and Färe et al. (2004) find that capital can significantly influence bank cost and profit efficiency measures. Finally, consistent with other bank efficiency studies (e.g. Dietsch and Lozano-Vivas, 2000), we control for the degree of concentration (C3).15 Higher concentration may be associated with either higher or lower efficiency. On the one hand, assuming that higher concentration results from market power, concentration and inefficiency effects go in the same direction. On the other hand, higher concentration could be the result of greater efficiency in the production processes. The last category of environmental variables can be labelled ‘regulatory conditions’ and includes variables that control for the main regulations in each country’s banking industry, as in Pasiouras (2008b) and Pasiouras et al. (2009). The specific regulations of concern in this study are related to restrictions on banks’ activities and the three pillars of Basel II, namely capital requirements (Pillar 1), official supervisory power (Pillar 2), and market discipline (Pillar 3). These regulatory conditions could affect bank efficiency in various ways discussed below. CAPRQ is a measure of capital requirements that accounts for both initial and overall capital stringency. Capital requirements can impact bank efficiency and productivity through at least three channels (Pasiouras et al., 2009). First, the introduction of binding regulatory capital requirements can reduce aggregate lending, which may improve or worsen loan quality (Kopecky and VanHoose, 2006), while loan quality and efficiency use to be related (Berger and DeYoung, 1997). Second, VanHoose (2007) argues that stricter capital standards may influence banks in substituting loans with alternative forms of assets. In turn, this could influence their cost and profit efficiency, because different asset portfolios will generate different returns, and they require different resources to be managed. Third, capital requirements may influence the deci-

15 In our study, the concentration of the banking industry is measured by the proportion of total assets held by the three largest banks in the country, following Beck et al. (2006a).

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sions of banks with regard to the mix of deposits and equity, which bear different costs. SPOWER is a measure of the power of the supervisory agencies indicating the extent to which they can take specific actions against bank management and directors, shareholders, and bank auditors. PRMON is an indicator of private monitoring and shows the degree to which banks are forced to disclose accurate information to the public and whether there are incentives to increase private monitoring.16 These two variables can be considered as general proxies for the second and third pillar of Basel II and are related to the official supervision hypothesis and the private monitoring hypothesis. On the one hand, the official supervision hypothesis suggests that supervisors can avoid market failure by directly overseeing, regulating, and disciplining banks. Therefore, a powerful supervisor could improve the corporate governance of banks, reduce corruption in bank lending, and improve the functioning of banks as financial intermediaries (Beck et al., 2006b) and so their efficiency. On the other hand, proponents of the private monitoring hypothesis argue that powerful supervision might be related to corruption or other factors that impede bank operations17, whereas regulations that promote market discipline through private monitoring from depositors, debt-holders and equity holders, will result in better outcomes for the banking sector. Thus, under the private monitoring view, one would expect that regulations that improve market discipline would boost the functioning of banks (Barth et al., 2007) and so their efficiency. RESTR is a proxy for the level of restrictions on banks’ activities. Its effect is determined by considering whether securities, insurance, real estate activities, and ownership of non-financial firms are unrestricted, permitted, restricted, or prohibited. Barth et al. (2004) point out that allowing a wide range of financial activities may lead to increased risk exposure of banks, or to the establishment of complex and powerful banks that will be difficult to monitor and discipline, which may reduce competition and efficiency. Particularly, Barth et al. (2003) point out that while fewer restrictions could provide greater profit opportunities, banks may also systematically fail to manage a diverse set of financial activities beyond traditional banking, and hence experience lower profitability. Empirical results from the impact of RESTR on efficiency are mixed (see e.g. Pasiouras, 2008b; Pasiouras et al., 2009). Finally, we also introduce dummy variables for the country groups (MADV, ADV and TRANS) defined above. These can influence X-efficiency through the inefficiency term where the dummy variables represent the effect of unobserved environmental factors that are common within different levels of economic development.18

16 For the construction of the capital requirements (CAPRQ), power of supervisory agencies (SPOWER) and private monitoring (PRMON) indices, we use the summation of the 0/1 quantified answers as in Barth et al. (2001, 2008), Pasiouras et al. (2009) and Pasiouras (2008b) among others. Further information on the calculation of these variables is available is Appendix A. 17 For instance, politicians and supervisors may use their power to benefit certain constitutes, attract campaign donations, and extract bribes. As Beck et al. (2006b) argue, under these circumstances, enhancing the power of supervisors may result in a decrease in the integrity of bank lending with adverse implications on the efficiency of credit allocation. 18 Including the same variables (equity and the three dummy variables, MADV, ADV and TRANS) in the inefficiency model and in the stochastic frontier does not violate the assumption of the independence when the equations are estimated simultaneously as in the Battese and Coelli (1995) model. As Battese and Coelli (1995) mention ‘‘The explanatory variables in the inefficiency model may include some input variables in the stochastic frontier” (p. 327). We also investigated the asymptotic properties of the ML estimators against the ones obtained by alternative models that did not include common variables in both the inefficiency model and the stochastic frontier. The tests showed that the specification with the dummies and equity in both the frontier function and the inefficiency term provide the best fit of the data. It should be mentioned that the idea of using common variables in the stochastic frontier and in the inefficiency model is not unique to this paper (see Coelli and Battese, 1996; Lundvall and Battese, 2000, among others).

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3. Data Our sample consists of an unbalanced dataset of 4960 observations from 752 publicly quoted commercial banks operating in 87 countries between 1999 and 2006.19 Table 1 presents the observations by year, geographical region, and economic development.20 Descriptive statistics of the bank-level variables used in the cost and profit functions are presented in Table 2. Although natural logarithms of these variables are used in computing the efficiency scores, we present the mean and standard deviations in levels to be more informative. All bank-specific data were obtained from Bankscope database of Bureau van Dijk and converted to US dollars. Unconsolidated data were selected but where these were not available, we chose consolidated data instead. We used reports prepared under international accounting (or international financial reporting) standards (IAS/IFRS) wherever possible, but also relied on those prepared under local generally accepted accounting principles (GAAP) where these were the only ones available.21 Finally, following Pasiouras et al. (2009) among others, we expressed the data in real (1995) terms using country GDP deflators. Our starting list consisted of the population of publicly quoted commercial banks that appeared to have financial records in Bankscope. After excluding banks with: (i) missing, negative or zero values for inputs/outputs, and (ii) missing values in the case of the country-specific control variables, we obtained a sample of 752 banks operating in 87 countries. Information on bank regulation and supervision variables (i.e. CAPRQ, PRMON, SPOWER, RESTR) were obtained from the World Bank (WB) database developed by Barth et al. (2001) and updated by Barth et al. (2006, 2008).22 Data for concentration (i.e. C3) were collected from the updated version of the WB database on financial development and structure (Beck et al., 2006a). Finally, data for the macroeconomic and financial development variables (i.e. GDPGR, INF, CLAIMS) were obtained from the Global Market Information Database (GMID). 4. Results 4.1. Cost efficiency and alternative profit efficiency scores 4.1.1. Basic results Panel A in Table 3 presents the average scores for cost and profit efficiency. In both cases, the estimated average efficiency increases 19 We focus on publicly quoted banks because it enhances comparability across countries. Furthermore, we focus on commercial banks for two reasons. First, it allows us to examine a more homogenous sample in terms of services, and consequently inputs and outputs, enhancing further the comparability among countries. Second, as mentioned in Demirguc-Kunt et al. (2004), since the regulatory data of the WB database are for commercial banks, it is more appropriate to use bank-level data only for this type of banks. 20 Additional information about the specific countries that are included in each group is given in Appendix B. 21 Our approach is consistent with that used in other cross-country studies (e.g. Pasiouras, 2008b). Around 40% of the banks in our sample report their financial statements under IFRS/IAS. 22 The WB database is available for three vintage points only. Version I was released in 2001 (Barth et al., 2001). For most of the countries, the regulatory information corresponds to 1999, although for some countries it is either from 1998 or 2000. Version II describes the regulatory environment at the end of 2002 (Barth et al., 2006) while Version III covers the period 2005/06 (Barth et al., 2008). Consequently, in order to provide coverage for the whole period, we had to work under the assumption that the scores of our regulatory variables (CAPRQ, PRMON, SPOWER, RESTR) remain constant within short windows of time. More precisely, we used information from Version I for bank observations covering the period 1999–2000, Version II for bank observations from the period 2001–2003, and Version III for bank observations from 2004–2006. While acknowledging the shortcoming of combining information in this way, we do not believe that it has an impact on our results. Other studies that have used this database across a number of years have obviously worked under a similar assumption (e.g. Demirguc-Kunt et al., 2004; Pasiouras et al., 2009).

with the inclusion of either the OBS or the non-interest income in the output vector. For instance, the mean cost efficiency in Model A1 is 0.8622, and increases to 0.8716 in Model A2 and to 0.8778 in Model A3. As for profit efficiency, the corresponding figures are 0.7532 (Model B1), 0.7540 (Model B2), and 0.7929 (Model B3).23 To assess whether the differences in the mean efficiency scores between the traditional models (A1 and B1) and the models that account for non-traditional activities (i.e. A2, A3, B2, B3) are statistically significant we use a non-parametric Kruskal–Wallis (K–W) test. The results indicate that, in the case of cost efficiency, the mean scores obtained from Models A2 and A3 are significantly higher than those obtained from Model A1. In the case of profit efficiency, the results are mixed. Hence we find, as in Clark and Siems (2002) that the inclusion of OBS in the profit function (Model B2) does not yield efficiency scores that are significantly different from the ones obtained from the traditional model B1. However, the difference between the profit efficiency scores obtained from Model B3 and Model B1 is statistically significant, with the former yielding slightly higher scores. This suggests that the OBS and noninterest income proxies that we use do not necessarily measure the same aspects of bank non-traditional activities.24 To explore our findings further, we present in Panel B, the mean cost and profit efficiency scores disaggregated by group of countries on the basis of their economic development. As with Panel A results, the average cost efficiency scores increase in all cases when we control for non-traditional activities, and the increase is always higher with non-interest income than with OBS. The results of the K–W test indicate that the null hypothesis of no difference in the mean efficiency can be rejected in all cases. The inclusion of OBS in the profit function results in a statistically significant increase (decrease) in the average scores of major advanced (developing) countries. As before, the comparison of models B1 and B3 indicates that the inclusion of non-interest income in the output vector results in mean profit efficiency scores that are higher and the differences are statistically significant irrespective of the level of development. In Panel C of Table 3, we perform an additional analysis by disaggregating cost and profit efficiency scores in terms of geographical regions. The results indicate that, as before, cost efficiency increases with the inclusion of OBS for all the geographical regions that we examine. With the exception of Australia, the cost efficiency scores increase further when we replace OBS by non-interest income. However, while the cost efficiency scores increase in all cases, the results of the K–W test reveal that in the case of Africa and Middle East, Australia, and North America, the differences between Models A1 and A2 are not statistically significant. When we replace OBS by non-interest income, the difference in the cost efficiency estimates remains insignificant only for Australian banks. Turning to the estimates of the profit function the null hypothesis of no difference in efficiency between models

23 The mean profit efficiency estimates are higher than the ones reported in some past studies (e.g. Berger and Mester, 1997; DeYoung and Hasan, 1998; Maudos et al. 2002) but are similar to the ones obtained by Rogers (1998), Stiroh (2000), Clark and Siems (2002), and Akhigbe and McNulty (2003) in the US (0.650–0.705; 0.606–0.720; 0.6462–0.7483; 0.6967–0.7862, respectively), Bos and Kolari (2005) in Europe (0.721) and the US (0.749) and Pasiouras et al. (2009) in an international context (0.768). The differences between our findings and theones of some earlier studies could be due to the large number of countries that we examine (i.e. global instead of country-specific benchmark), estimations using single years and many small US banking organisations in past studies, different time periods, etc. (see also Clark and Siems, 2002), while compared to Maudos et al. (2002) one additional factor could be that their approach to estimate a cross-country frontier does not control for environmental differences. 24 Table 3 also presents the K-W test for differences between Models A2–A3 and B2– B3. Looking at the total sample, we observe that there are statistically significant differences between the two specifications.

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A. Lozano-Vivas, F. Pasiouras / Journal of Banking & Finance 34 (2010) 1436–1449 Table 1 Observations by year, geographical region and country’s economic development. Africa and Middle East

Asia Pacific

Australia

Eastern Europe

Latin America and Caribbean

North America

Western Europe

9 1 9 9 9 9 9 9 9 8

92 14 51 63 66 70 70 66 66 51

104 16 70 82 84 78 67 79 80 73

21 2 17 18 19 20 21 21 21 19

149 16 129 127 131 135 133 113 127 109

503

613

Panel A: Observations by year and geographical region No of banks No. of countries 1999 2000 2001 2002 2003 2004 2005 2006

119 22 101 106 108 105 114 112 109 93

Total

848

258 16 156 214 226 228 235 235 241 230 1765 Major advanced

71 Advanced

Transition

156

1004 Developing

Panel B: Observations by year and country’s development No of banks No. of countries 1999 2000 2001 2002 2003 2004 2005 2006

147 6 74 124 137 139 143 137 141 128

114 16 104 105 106 104 99 84 94 84

105 16 59 72 77 81 82 78 78 62

386 49 296 318 323 321 325 336 340 309

Total

1023

780

589

2568

Note: Countries in Panel A were assigned in the geographical regions following the classification of the Global Market Information Database (GMID). Countries in Panel B were classified in the four categories by combining information from the International Monetary Fund (IMF) and the European Bank for Reconstruction and Development (EBRD). See Appendix B.

Table 2 Descriptive statistics of inputs/outputs in cost/profit function. PBT

TC

Q1

Q2

Q3a

W1

W2

W3

EQ

All

Mean St. Dev.

130,653 528,453

755,923 3,050,567

9,053,666 30,328,321

7,586,812 37,706,095

4,122,011 21,567,636

Q3b 192,205 855,547

0.050 0.108

1.359 4.160

0.016 0.013

928,149 2,766,422

Africa and Middle East

Mean St. Dev.

66,849 113,729

208,277 418,131

2,263,637 4,568,657

1,503,793 2,328,134

1,116,837 2,014,204

44,377 98,243

0.042 0.024

0.848 0.596

0.016 0.011

383,462 546,593

Asia

Mean St. Dev.

82,584 392,815

355,622 821,349

8,836,444 18,991,301

5,082,113 14,255,966

1,660,388 5,907,170

90,175 233,270

0.042 0.082

0.845 1.461

0.010 0.006

811,229 2,037,580

Australia

Mean St. Dev.

922,123 983,177

3,277,587 3,178,474

45,955,631 44,477,677

11,543,541 13,720,499

11,915,850 15,589,325

857,685 848,806

0.052 0.016

4.940 7.668

0.008 0.003

4,506,112 4,473,500

Eastern Europe

Mean St. Dev.

15,481 46,844

71,822 145,097

460,368 907,964

398,796 959,316

779,001 3,562,440

23,360 51,989

0.054 0.032

1.472 9.379

0.022 0.011

92,976 202,856

Latin America and Caribbean

Mean St. Dev.

35,729 113,057

264,659 902,401

1,003,637 2,320,494

870,892 3,040,701

340,330 896,040

69,566 277,165

0.076 0.061

1.525 2.173

0.028 0.021

195,185 454,211

North America

Mean St. Dev.

573,162 961,254

2,920,588 4,601,597

27,573,759 41,254,503

20,902,134 34,950,862

7,956,574 1,696,9491

703,060 1,242,975

0.027 0.013

1.878 1.454

0.016 0.004

2,772,382 4,178,372

Western Europe

Mean St. Dev.

259,978 876,391

2,050,205 6,034,723

18,903,531 55,932,127

22,480,741 78,184,456

13,824,503 45,091,918

529,464 1,712,232

0.056 0.206

2.203 5.272

0.016 0.014

1,920,104 4,761,276

Major advanced

Mean St. Dev.

237,839 748,988

1,816,260 4,979,060

24,681,579 47,110,486

21,848,368 63,517,367

10,104,866 41,141,880

500,708 1,473,746

0.026 0.155

1.354 2.308

0.011 0.013

2,272,063 4,119,776

Advanced

Mean St. Dev.

289,914 791,046

1,430,751 4,604,383

15,888,511 42,630,632

12,355,628 54,188,609

7,560,819 23,638,858

356,249 1,202,933

0.033 0.020

2.177 6.035

0.015 0.007

1,592,808 3,768,431

Transition

Mean St. Dev.

14,117 43,608

64,588 135,708

425,528 857,982

353,308 895,576

671,093 3,302,087

20,927 48,488

0.056 0.032

1.417 8.673

0.022 0.012

84,004 189,880

Developing

Mean St. Dev.

66,309 325,989

287,117 868,134

2,731,018 13,153,689

2,116,128 10,915,045

1,485,664 5,010,027

58,767 197,464

0.063 0.111

1.100 1.660

0.017 0.014

384,514 1,547,961

Notes: PBT = profit before taxes, TC = total cost, Q1 = loans, Q2 = other earning assets, Q3a = off-balance sheet items, Q3b = non-interest income, W1 = interest expenses/ deposits and short term funding, W2 = non-personnel administrative expenses/fixed assets, W3 = personnel expenses/total assets, EQ = equity. Nominal values are in thousands in 1995 US dollars terms.

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Table 3 Cost and alternative profit efficiency scores. Model

Kruskal–Wallis

Panel A: Cost and alternative profit efficiency estimates Cost efficiency

A1

A2

A3

A1 vs. A2

A1 vs. A3

A2 vs. A3

Mean

0.8622

0.8716

0.8778

27.8700***

71.4680***

10.0760***

Profit efficiency

B1

B2

B3

B1 vs. B2

B1 vs. B3

B2 vs. B3

Mean

0.7532

0.7540

0.7929

0.0010

141.6610***

140.9230***

Panel B: Disaggregation of efficiency estimates by level of economic development Cost efficiency A1 A2

A3

A1 vs. A2

A1 vs. A3

A2 vs. A3

Major advanced Advanced Transition Developing

0.8899 0.8984 0.8139 0.8512

0.9010 0.9041 0.8334 0.8588

0.9026 0.9084 0.8431 0.8666

23.0850*** 9.6310*** 7.9330*** 5.6700**

25.8650*** 31.4500*** 16.8550*** 25.8270***

0.1330 6.6420** 1.6200 7.4410***

Profit efficiency

B1

B2

B3

B1 vs. B2

B1 vs. B3

B2 vs. B3

Major advanced Advanced Transition Developing

0.7270 0.8251 0.8292 0.7243

0.7591 0.8303 0.8211 0.7134

Panel C: Disaggregation of efficiency estimates by geographical region Cost efficiency A1 A2

***

***

0.7846 0.8414 0.8509 0.7682

13.4540 0.6870 1.7830 4.9420**

50.1650 7.3330*** 10.4770*** 91.9110***

12.5020*** 3.6170* 21.2790*** 136.4670***

A3

A1 vs. A2

A1 vs. A3

A2 vs. A3

*

1.1320 1.4710 0.3440 1.0320 2.1070 4.7260** 12.7320***

Africa and Middle East Asia Australia Eastern Europe Latin America and Caribbean North America Western Europe

0.8896 0.8656 0.8768 0.8110 0.8062 0.9166 0.8834

0.8941 0.8729 0.8916 0.8301 0.8217 0.9192 0.8928

0.8988 0.8774 0.8877 0.8396 0.8323 0.9245 0.8997

0.6170 13.1990*** 2.0960 6.8370*** 4.7750** 0.2030 8.8080***

3.2830 24.1000*** 0.7270 13.4480*** 13.3600*** 7.2010*** 39.4320***

Profit efficiency

B1

B2

B3

B1 vs. B2

B1 vs. B3

B2 vs. B3

Africa and Middle East Asia Australia Eastern Europe Latin America and Caribbean North America Western Europe

0.7829 0.8009 0.7722 0.8266 0.6021 0.5759 0.7259

0.7758 0.7973 0.7743 0.8190 0.5823 0.6350 0.7489

0.8086 0.8317 0.8103 0.8501 0.6703 0.6701 0.7755

1.2470 0.0800 0.0260 1.2010 4.0150** 9.2720*** 5.4160**

21.3460*** 64.6050*** 4.7070** 9.6500*** 45.8270*** 19.0570*** 22.7020***

32.6320*** 65.7800*** 5.8450** 17.9730*** 72.3720*** 2.0760 5.9000**

Notes: Chi-square values are shown for the Kruskal–Wallis test. Models A1 and B1 are traditional models with two outputs, namely, loans and other earning assets; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; countries were assigned to geographical regions on the basis of the GMID classifications. * Statistically significant at the 10% level. ** Statistically significant at the 5% level. *** Statistically significant at the 1% level.

B1 and B2 can be rejected in only three of the seven geographical regions. Furthermore, even in those cases where the null hypothesis is rejected, there is no consistency in the change of the efficiency scores. The inclusion of OBS reduces the profit efficiency of banks from Latin America and Caribbean, while the opposite happens in the case of North America and Western Europe. The latter, taken together with our findings with regard to major advanced countries, shows that the inclusion of OBS improves profit efficiency in countries where banks have a longer history of being involved in OBS and could possibly be more efficient in generating higher income. The inclusion of non-interest income in the profit function results in mean efficiency scores that are significantly higher in all cases than the ones obtained from the traditional model B1. With regard to differences between cost and profit efficiency, one explanation could be associated with the fact that OBS carry out higher risk activities for banks, which can improve cost efficiency but also deteriorate revenue efficiency. If the second effect dominates, profit efficiency will decrease, as is possibly the case in Eastern European countries.

4.1.2. Robustness test: Estimation of specific frontiers To examine the robustness of our results we estimated separate frontiers for the groups of countries classified by their level of economic development (i.e. major advanced, advanced, transition and developing).25 We performed such an exercise under the assumption that estimating a global (common) frontier with environmental factors and regulatory conditions does not sufficiently control for differences in technology (i.e. despite the inclusion of country group dummies). Thus, by estimating separate frontiers we explicitly account for potential differences in technology across different levels of economic development. As discussed further below, the results are very similar and consistent with those obtained from the common frontier, thus not changing the orientation of our conclusions. To test the consistency of the results obtained from the estimation of the specific frontiers with the ones obtained from the common (global) frontier we use a 25 We would like to thank an anonymous reviewer for constructive comments that motivated us to undertake this analysis.

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set of conditions proposed by Bauer et al. (1998). More precisely, the comparison of the two approaches shows that: (i) both Pearson and Spearman’s rho estimated for pairs of level of efficiency scores were relatively high (e.g. 0.906, 0.833, 0.867) and statistically significant at the 1% level in most cases, hence the scores obtained by the two approaches are highly correlated; (ii) the specific frontiers and the global frontier rank the institutions approximately in the same order;26 (iii) the proportion of banks that are identified by both approaches as having efficiency scores in the top 25% quartile is between 95% and 100%. The similarity of the above results indicates that the global frontier is controlling not only for differences arising from environmental and regulatory conditions but also from unobserved countryspecific technological differences, through the inclusion of group dummy variables.

4.2. Impact of explanatory variables on inefficiency Our efficiency estimates shown in the previous section are obtained by considering environmental conditions and bank-level capitalizations explanatory variables of the inefficiency levels. As discussed in the introductory section, an additional aim of our analysis, not undertaken in previous studies, is to investigate if these conditions have a different influence on inefficiency across different specifications of the model. Table 4 presents the parameters estimates of bank-level capitalization and country-specific control factors used in the cost (Panel A) and profit (Panel B) functions. The results reveal that irrespective of the model used to obtain inefficiency (i.e. with or without non-traditional activities) the explanatory variables retain their sign. Hence the inclusion of non-traditional activities does not influence the direction of the impact of these determinants of inefficiency. Panel A in Table 4 shows that while inflation has a positive impact on cost inefficiency, the growth of GDP is negatively associated with inefficiency. In accordance with the findings of Mester (1996), cost inefficiency is inversely correlated with the level of bank’s financial capital. Financial development (CLAIMS) appears to have a negative influence on cost inefficiency. Furthermore, more concentrated banking industries are less cost inefficient. Finally, with regard to the impact of the regulatory factors, the findings illustrate that, as expected by the regulators, higher capital requirements have a negative impact on cost inefficiency. Additionally, the results show that private monitoring also reduces cost inefficiency. Thus, consistent with Pasiouras (2008b) and Pasiouras et al. (2009) our results support the argument of the private monitoring hypothesis that requirements related to the disclosure of accurate and timely information to the public allow private agents to overcome information and transactions costs and monitor banks more effectively. On the other hand, the results reveal that granting broad powers to supervisors has a positive impact on cost inefficiency, which can be explained by the fact that powerful supervision may impede bank operations (Barth et al., 2004). Moreover, in accordance with Barth et al. (2004) greater regulatory restrictions on bank activities are associated with higher banking sector inefficiency. As regards the impact of the explanatory variables on profit inefficiency (Panel B in Table 4), the results indicate a similar relationship with the macroeconomic environmental conditions, infla26 Appendix C contains Spearman rank-order correlation coefficients showing how close the rankings of banks are among each of the 24 specific frontiers with the results obtained from the common frontier, regarding the four group of countries in terms of economic developments More detailed, the rank-order correlation is very high, between 0.7824 and 0.9917, and all of these correlations are statistically significant at the 1% level.

Table 4 Effect of determinants on cost and profit inefficiency. Model A1

Model A2

Model A3

Panel A: Cost inefficiency (parameter estimates) Constant 1.2472*** 0.9580** INF 0.0341*** 0.0365*** GDPGR 0.0468*** 0.0423*** LN(EQUITY) 0.2224*** 0.0725** CLAIMS 0.3927*** 0.4098*** C3 0.0138*** 0.0123*** CAPRQ 0.1581*** 0.1745*** SPOWER 0.0309** 0.0255** PRMON 0.2724*** 0.2995*** RESTR 0.2581*** 0.4272*** MADV 0.4659*** 1.1218*** ADV 0.1251 0.4221*** TRANS 0.5669*** 0.5969*** Panel B: Alternative profit inefficiency (parameter estimates) Model B1 Model B2 Constant INF GDPGR LN(EQUITY) CLAIMS C3 CAPRQ SPOWER PRMON RESTR MADV ADV TRANS

2.6618*** 0.0311*** 0.1076*** 0.3102*** 2.3783*** 0.0039** 0.0555** 0.1389*** 0.5763*** 0.9696*** 0.3320** 1.3044*** 3.0141***

2.5392*** 0.0302*** 0.1076*** 0.2953*** 2.3119*** 0.0030* 0.0562** 0.1172*** 0.5675*** 0.9171*** 0.2064* 1.5161*** 2.8887***

1.4471*** 0.0298*** 0.0479*** 0.1097*** 0.3146*** 0.0070*** 0.1794*** 0.0873*** 0.1874*** 0.3770*** 0.6578*** 0.3296*** 0.6336*** Model B3 3.1508*** 0.0451*** 0.1501*** 0.2062*** 2.0943*** 0.0205*** 0.0687** 0.1269*** 0.7222*** 1.4887*** 0.1583* 2.2614*** 4.0082***

The parameter estimates reported in this Table were obtained simultaneously with the parameters of the stochastic frontier using the Battese and Coelli (1995) model. Models A1 and B1 are traditional models with two outputs, namely, loans and other earning assets; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; countries were assigned to geographical regions on the basis of the GMID classifications. * Statistically significant at the 10% level. ** Statistically significant at the 5% level. *** Statistically significant at the 1% level.

tion and growth of GDP, as in the cost specifications although, differently to cost inefficiency, profit inefficiency is positively correlated with the level of financial capital.27 Intuitively, inflation has a positive impact on profit inefficiency while the GDP growth is negatively associated with profit inefficiency, since in countries that are less prosperous, banks have worse access to new technology (Lensink et al., 2008). As with cost inefficiency, financial development (CLAIMS) has a negative influence on profit inefficiency. In contrast, higher concentration in the banking sector results in higher profit inefficiency. Turning to the impact of regulatory conditions, we ob27 The differences in the relationship between equity and cost (profit) inefficiency could be explained as follows. Higher levels of equity capital reduce the probability of financial distress, which as mentioned in Berger and Bonaccorsi di Patti (2006) reduces costs by lowering risk premia on substitutes for other potentially more costly risk management activities. Second, while one would expect that relying on equity rather than deposits would result in higher costs, this is not necessarily the case. As Berger and Mester (1997) point out while the initial cost of raising capital is high, the interest expense on capital is zero. These reasons could explain the negative association between cost inefficiency and equity. However, as higher equity is associated with less risk, risk-averse managers and shareholders may accept lower than maximum profits (Berger and Bonaccorsi di Patti (2006). At the same time, due to the lower bankruptcy risk, shareholders may exercise less monitoring on managers. From a corporate governance perspective, this could induce managers to allocate funds less efficiently resulting in higher revenue inefficiency. If the improvement in cost efficiency is not enough to counterbalance the deterioration in revenue efficiency, one would then observe a positive relationship between equity and profit inefficiency. The latter finding is similar to that of Berger and Bonaccorsi di Patti (2006) who report that a lower equity capital is associated with higher profit efficiency.

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serve that higher capital requirements affect positively profit inefficiency (less profit due to captive earning resources), while deterioration in private monitoring conditions reduces profit inefficiency (as in the case of cost inefficiency). However, granting more broad powers to supervisors and greater restrictions on bank activities are associated with lower profit inefficiency, consistent with the results of Pasiouras et al. (2009). While the above factors reveal their individual influence on bank inefficiency, a more realistic and interesting scenario would be to investigate the average effect that the different determinants of inefficiency have across countries, distinguished by their level of economic development, viz. Major Advanced, Advanced, Transition, and Developing countries. To perform this exercise, we compute the average effect of the regulatory conditions and the net total average effect of all the explanatory variables (including the regulatory ones) on both cost and profit inefficiency, for each group of countries. Additionally, given that we use dummy variables to distinguish the different country groups we also calculate the average country effect.28 Thus, Table 5 shows the results of the average country effect, average effect of regulatory conditions, and the net total average effect on cost and profit efficiency (Panels A and B) by group of countries for each one of the estimated models (evaluated at the average level of each explanatory variable). The results in Panel A of Table 5 reveal that banks operating in Transition countries experience a reduction in cost inefficiency that is lower than other country groups, in all the cases (column 2).29 These results suggest that upon conditioning for environmental factors, banks in Transition countries attained lower (higher) cost efficiency (inefficiency), which is consistent with the results presented in Table 3 (Panel B). Comparing the average impact of the regulatory conditions across all three models (A1–A3), the results show a general decline in cost inefficiency for all country groups (except for Developing countries in model A3). On average, however, the regulatory conditions have contributed less to the reduction of cost inefficiency in Transition and Developing countries than in Major Advanced and Advanced countries. Moreover, the regulatory conditions exercise a higher absolute effect on models A1 and A2 than on model A3. Looking at the impact of the net effect of the explanatory variables (column 4), the results show a general decline in the cost inefficiency across all three models, with the lowest decline being recorded for the Transition countries. These results suggest that while the determinants of inefficiency have the same directional impact (in terms of sign) in all three models, the impact is higher when non-traditional activities are taken into account. Overall, the results suggest that, on average, the environmental factors have a positive impact on cost efficiency, but this impact is not uniform across all the countries and depends on other conditioning factors. The picture changes, however, when we turn to the profit inefficiency results (Panel B in Table 5). The Transition countries are the only ones that have a negative impact on profit inefficiency (hence a positive effect on profit efficiency), while the Major Advanced and the Developing countries both experience the highest decrease (increase) in profit efficiency (inefficiency). These results

28

Since the Developing countries group has been used as the base case against which all the others groups are compared, the estimated coefficient of each country group can be interpreted as reflecting the difference between each country group and the Developing countries group, giving the average country level effect on cost and profit inefficiency. 29 More detailed, comparing the three models A1–A2–A3, banks in Major Advanced countries have reduced their cost inefficiency between 1.71% and 2.10%; in Advanced countries between 1.37% and 1.78% and in Developing countries between 0.96% and 1.45%; while banks in Transition countries have reduced their cost inefficiency between 0.36% and 0.81%.

Table 5 Country, Regulatory and Net Total Effect on cost and profit inefficiency by country group. Country effect

Regulatory effect

Net total effect

Model A1 Major advanced Advanced Transition Developing

1.7131 1.3723 0.6803 1.2472

1.2739 1.5090 1.1822 1.2869

3.2888 3.2734 1.2365 2.1693

Model A2 Major advanced Advanced Transition Developing

2.0798 1.3801 0.3611 0.9580

1.1271 1.4259 1.0696 1.1194

4.0775 3.7906 1.7913 2.5697

Model A3 Major advanced Advanced Transition Developing

2.1049 1.7767 0.8136 1.4471

0.0154 0.3261 0.0062 0.0801

4.2871 4.1506 2.3838 3.2195

Model B1 Major advanced Advanced Transition Developing

2.9937 1.3574 0.3524 2.6618

6.8001 6.4499 6.1465 7.2758

4.6480 2.4385 1.9638 5.1441

Model B2 Major advanced Advanced Transition Developing

2.3328 1.0231 0.3495 2.5392

6.3838 6.0629 5.7454 6.8148

3.8161 1.9226 1.7762 4.8210

Model B3 Major advanced Advanced Transition Developing

3.3091 0.8895 0.8573 3.1508

8.7233 8.2294 7.8136 9.3186

4.5182 2.2376 1.3334 5.4004

Panel A: Cost inefficiency

Panel B: Alternative profit inefficiency

Notes: Models A1 and B1 are traditional models with two outputs, namely, loans and other earning assets; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; countries were assigned to geographical regions on the basis of the GMID classifications. Net total effect includes the country and regulatory effect plus the effect of the rest of the environmental variables included as explicative variables on inefficiency (i.e. INF, EQUITY, CLAIMS, C3).

are consistent with the results presented in Table 3 (Panel B). However, the regulatory conditions have a greater impact on profit than on cost inefficiency when conditioning for the remaining explanatory variables (column 3), although, as in the case of cost inefficiency they contribute to a reduction of profit inefficiency. In contrast, when the net average effect of all the determinants is accounted for (column 4), the results show an increase in profit inefficiency. The highest increase in profit inefficiency is recorded in the case of Model 1, suggesting that the inclusion of non-traditional activities moderates the impact of environmental factors on profit inefficiency. 4.3. Further analysis: Additional country characteristics As mentioned in the previous section, the impact of environmental factors on inefficiency is not uniform across all the groups of countries and it could depend on other conditioning factors. Therefore, to explore our results further, we investigate whether and how our cost and profit efficiency estimates relate to additional country characteristics reflecting the structure of bank ownership, financial market development, and regulation relating to

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A. Lozano-Vivas, F. Pasiouras / Journal of Banking & Finance 34 (2010) 1436–1449 Table 6 Disaggregation of Cost and Alternative profit efficiency scores by various country characteristics. A1

A2

Panel A: Average efficiency scores by group and Kruskal–Wallis test GOV > country mean 0.8562 0.8639 GOV < country mean 0.8663 0.8769 *** 37.504*** Kruskal–Wallis 14.405

A3 0.8732 0.8808 10.174***

FOR > country mean FOR < country mean Kruskal–Wallis

0.8287 0.8717 195.716***

0.8408 0.8804 195.884***

0.8464 0.8868 231.189***

SMDEV > country mean SMDEV < country mean Kruskal–Wallis

0.8965 0.8483 172.434***

0.9033 0.8584 203.352***

0.9057 0.8690 156.491***

SPROT > country mean SPROT < country mean Kruskal–Wallis DERIV – top 25 DERIV – non top 25 Kruskal–Wallis

0.8758 0.8528 3.461*

0.8833 0.8640 4.343**

0.8886 0.8735 1.105

0.8905 0.8613 40.055***

0.8990 0.8702 68.764***

0.9008 0.8790 24.862***

Panel B: Kruskal–Wallis test – traditional versus non-traditional models A1 vs. A2 A1 vs. A3 GOV > country mean GOV < country mean FOR > country mean FOR < country mean SMDEV > country mean SMDEV < country mean SPROT > country mean SPROT < country mean DERIV – top 25 DERIV – non top 25

4.248** 28.532*** 6.224** 23.665*** 24.408*** 5.890** 19.308*** 4.022** 26.032*** 7.569***

29.933*** 40.964*** 11.962*** 65.559*** 45.423*** 30.814*** 44.035*** 16.251*** 27.951*** 41.079***

A2 vs. A3 12.208*** 1.110 0.931 10.532*** 3.929** 10.01*** 5.281** 4.182** 0.014 14.004***

B1

B2

B3

0.7258 0.7720 89.016***

0.7230 0.7753 103.371***

0.7739 0.8059 41.945***

0.7393 0.7567 5.044**

0.7277 0.7611 23.868***

0.7769 0.7971 7.022***

0.7827 0.7310 118.265***

0.7962 0.7275 190.166***

0.8169 0.7788 79.184***

0.7744 0.6875 180.713***

0.7753 0.7087 125.664***

0.8074 0.7562 110.026***

0.7474 0.7618 2.364

0.7611 0.7611 1.792

0.7909 0.8006 0.036

B1 vs. B2

B1 vs. B3

B2 vs. B3

0.217 0.148 2.544 0.851 5.048** 0.665 0.326 6.347** 4.751** 0.232

84.39*** 60.423*** 25.443*** 118.884*** 44.34*** 81.338*** 52.892*** 70.961*** 51.265*** 68.593***

91.471*** 55.456*** 44.037*** 99.516*** 19.961*** 96.185*** 43.289*** 36.166*** 25.205*** 77.228***

Notes: The efficiency estimates reported in this Table were obtained using the Battese and Coelli (1995) model. Models A1 and B1 are traditional models with two outputs, namely, loans and other earning assets; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Model A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS. * Statistically significant at the 10% level. ** Statistically significant at the 5% level. *** Statistically significant at the 1% level.

investor protection.30 Specifically, we disaggregate and present our efficiency estimates on the basis of: (i) the degree of involvement of government owned banks in the industry (GOV), (ii) the degree of involvement of foreign owned banks in the industry (FOR), (iii) the degree of stock market development (SMDEV), (iv) the degree of shareholders’ rights protection (SPROT), and (v) the degree of derivative markets development (DERIV). In the first four cases, we determine the degree of involvement or development in each country as above or below the country sample average (e.g. government banks’ presence above or below the country mean, etc). In the last case, we consider a country’s derivative market to be developed if it hosts one of the top 25 worldwide derivatives exchange; and non-developed otherwise.31 The results of this analysis for all the models are presented in Table 6. In particular, the results in panel A show that banking sectors with the proportion of government owned and foreign banks presence below (above) the mean are on average more (less) cost and profit efficient. In contrast, relatively greater degree of stock market development and shareholder protection (above 30 We would like to thank an anonymous referee for constructive comments that motivated us to undertake this analysis. 31 To classify each one of the banking sectors as above or below the country sample mean in terms of the percentage of assets held by government (foreign) owned banks we use information from the Barth et al. (2001, 2006, 2008) database. Similarly, using yearly information from GMID we classify the countries as above (below) the country sample average in terms of the stock market development measured by the ratio of stock market capitalization to GDP. As it concerns the protection of shareholders’ rights, we classify each one of the countries as above (below) the country sample mean using the revised anti-director index by Djankov et al. (2008). Finally, using yearly data from the Futures Industry association, we characterize a country as developed derivative market if it hosts one of the top 25 worldwide derivative exchanges.

the mean) results in higher cost and profit efficiency, with pairwise differences being statistically significant in most cases; the only exception being model A3 in the case of shareholders’ protection. With regard to the development of the derivative markets, we observe that the differences are statistically significant only in the case of cost efficiency, revealing that banking sectors in relatively more developed derivative markets are more cost efficient on average. In panel B, we assess whether the differences in the mean efficiency scores between the traditional models and the models that account for non-traditional activities are statistically significant, based on a pairwise comparison of two models for each one of the ten categories. Consistent with the results in Table 3, we observe that in all but one case the average cost efficiency increases when we include OBS or non-interest income in the output vector. The inclusion of OBS in the profit function results in statistically significant differences only for countries with relatively more developed stock and derivatives markets, and lower protection of shareholders right. Finally, the comparison of models B1 and B3 indicates that the profit efficiency scores of the latter model are higher and statistically significant in all cases, which is consistent with the results presented in Table 3.

5. Conclusions This study investigates the impact of including non-traditional activities as an output in estimating cost and profit efficiency of banks. As a consequence of the growing demand for such activities, a handful of recent studies have included off-balance sheet items

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or non-interest income as part of bank output, thus providing evidence on the impact of non-traditional activities on bank efficiency. The present paper attempts to contribute to this literature by using a sample of banks drawn from different countries, comprising major advanced, advanced, transition and developing economies, thus allowing a comparison of the impact of such activities on bank efficiency across different levels of economic development. Additionally, we highlight differences in the impact of regulatory and environmental conditions on cost and profit efficiency. Our approach to estimating cost and profit efficiency is to use the stochastic-frontier based Battese and Coelli (1995) model that allows us to control for cross-country differences in economic, environmental and regulatory conditions. In each case, using a sample of 4960 observations from 752 publicly quoted commercial banks operating in 87 countries between 1999 and 2006, we estimate a traditional function that considers loans and other earnings assets as the only outputs, and two additional functions that account for non-traditional activities, the first through the inclusion of OBS, and the second through the inclusion of non-interest income in the output vector. We find that on average cost efficiency increases whether we use OBS or non-interest income as an indicator of non-traditional activities. With respect to profit efficiency, the results are mixed. On the one hand, the inclusion of OBS does not have a statistically significant influence on profit efficiency. On the other hand, the non-interest income based model produces profit efficiency scores that are higher from those of the traditional model without nontraditional activities, and the differences are statistically significant. When we explore the results further, by distinguishing banks on the basis of countries’ overall level of economic development and geographic regions, we find that there is some degree of consistency as regards the impact of non-interest income. However, the influence of OBS on cost and profit efficiency is not robust across different levels of development or geographical regions. Since we consider environmental and regulatory conditions as determinants of cost and profit efficiency, we are able to investigate, for the first time, whether the inclusion of non-traditional activities affect the direction in which these conditions affect inefficiency. More specifically, we examine differences in the impact of environmental and regulatory conditions on cost and profit inefficiency depending on whether non-traditional activities are included in the output vector or not. The results show that the inclusion of non-traditional activities does not significantly influence the directional impact of environmental conditions on cost or profit inefficiency, although environmental factors lead to high-

er efficiency when non-traditional activities are taken into account. Furthermore, regulatory conditions that enhance banking supervision and monitoring, and regulations that restrict bank activities, generally contribute to improvement in bank efficiency, with the impact being much higher on profit efficiency than on cost efficiency. Finally, the results indicate that the impact of environmental factors on inefficiency is not uniform across all the groups of countries and our further analysis reveals that these could depend on other conditioning factors. In particular we find that in countries where the presence of government owned and foreign owned banks are lower than average, banks tend to be more cost and profit efficient. In contrast, countries with higher than average stock market development and shareholder protection results in more efficient banks. Banks operating in countries with more developed derivatives stock exchanges are generally more cost efficient but not necessarily profit efficiency. Consistent with our earlier observations, when we disaggregate our efficiency estimates on the basis of these additional country characteristics reflecting the structure of bank ownership, financial market development, and so on, the inclusion of either non-interest income or OBS in the output vector increases cost efficiency, while our conclusions about the impact of OBS on profit efficiency remain mixed.

Acknowledgements Lozano-Vivas acknowledges financial support from Ministerio de Educación y Ciencias and FEDER Grant reference ECO200804424. Earlier versions of the manuscript were prepared while Fotios Pasiouras was Lecturer at the University of Bath School of Management. We would like to thank three anonymous reviewers and the editors of this issue (Rajiv Banker, J. David Cummins, Paul J.M. Klumpes) for valuable comments that helped us improve earlier versions of the manuscript. We also thank the participants of the conference ‘‘Performance Measurement in the Financial Services Sector: Efficiency Frontier Methodologies and Other Innovative Techniques” held at Imperial College Business School (London, UK) as well as the ones of the workshop ‘‘Fostering a European Network on Financial Efficiency (IFRESI-CNRS) held in Lille (France) for numerous suggestions. Last but not least, we would like to thank, without implicating, George Battese, Timothy Coelli, Manthos Delis, Iftekhar Hasan, Subal Kumbhakar, and Sailesh Tanna for clarifications and suggestions. Any remaining errors are of course our own.

Appendix A. Information on regulatory variables Variable

Category

Description

CAPRQ

Capital requirements

This variable is determined by adding 1 if the answer is yes to questions 1–6 and 0 otherwise, while the opposite occurs in the case of questions 7 and 8 (i.e. yes = 0, no = 1). (1) Is the minimum required capital asset ratio risk-weighted in line with Basle guidelines? (2) Does the ratio vary with market risk? (3–5) Before minimum capital adequacy is determined, which of the following are deducted from the book value of capital: (a) market value of loan losses not realized in accounting books? (b) unrealized losses in securities portfolios? (c) unrealized foreign exchange losses? (6) Are the sources of funds to be used as capital verified by the regulatory/supervisory authorities? (7) Can the initial or subsequent injections of capital be done with assets other than cash or government securities? (8) Can initial disbursement of capital be done with borrowed funds?

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A. Lozano-Vivas, F. Pasiouras / Journal of Banking & Finance 34 (2010) 1436–1449 Appendix A (continued)

Variable

Category

Description

PRMON

Private monitoring

This variable is determined by adding 1 if the answer is yes to questions 1–6 and 0 otherwise, while the opposite occurs in the case of questions 7 and 8 (i.e. yes = 0, no = 1). (1) Is subordinated debt allowable (or required) as part of capital? (2) Are financial institutions required to produce consolidated accounts covering all bank and any non-bank financial subsidiaries? (3) Are off-balance sheet items disclosed to public? (4) Must banks disclose their risk management procedures to public? (5) Are directors legally liable for erroneous/misleading information? (6) Do regulations require credit ratings for commercial banks? (7) Does accrued, though unpaid interest/principal enter the income statement while loan is non-performing? (8) Is there an explicit deposit insurance protection system?

SPOWER

Official disciplinary power

This variable is determined by adding 1 if the answer is yes and 0 otherwise, for each one of the following fourteen questions: (1) Does the supervisory agency have the right to meet with external auditors to discuss their report without the approval of the bank? (2) Are auditors required by law to communicate directly to the supervisory agency any presumed involvement of bank directors or senior managers in illicit activities, fraud, or insider abuse? (3) Can supervisors take legal action against external auditors for negligence? (4) Can the supervisory authorities force a bank to change its internal organizational structure? (5) Are off-balance sheet items disclosed to supervisors? (6) Can the supervisory agency order the bank’s directors or management to constitute provisions to cover actual or potential losses? (7) Can the supervisory agency suspend director’s decision to distribute dividends? (8) Can the supervisory agency suspend director’s decision to distribute bonuses? (9) Can the supervisory agency suspend director’s decision to distribute management fees? (10) Can the supervisory agency supersede bank shareholder rights and declare bank insolvent? (11) Does banking law allow supervisory agency or any other government agency (other than court) to suspend some or all ownership rights of a problem bank? (12) Regarding bank restructuring and reorganization, can the supervisory agency or any other government agency (other than court) supersede shareholder rights? (13) Regarding bank restructuring and reorganization, can supervisory agency or any other government agency (other than court) remove and replace management? (14) Regarding bank restructuring and reorganization, can supervisory agency or any other government agency (other than court) remove and replace directors?

RESTR

Restrictions on banks activities

The score for this variable is determined on the basis of the level of regulatory restrictiveness for bank participation in: (1) securities activities (2) insurance activities (3) real estate activities (4) bank ownership of non-financial firms. These activities can be unrestricted, permitted, restricted or prohibited that are assigned the values of 1, 2, 3 or 4, respectively. We use an overall index by calculating the average value over the four categories

Note: The individual questions and answers were obtained from the World Bank database developed by Barth et al. (2001, 2006, 2008).

Appendix B. Classification of countries by region and development status Country

Region

Development status

Country

Region

Development status

Country

Region

Development status

Argentina Armenia Australia Austria

LAC ASIA AUST WEUR

DEVEL TRANS ADV ADV

Hong Kong Hungary Iceland India

ASIA EEUR WEUR ASIA

ADV TRANS ADV DEVEL

AME ASIA LAC ASIA

DEVEL DEVEL DEVEL DEVEL

Bahrain Bangladesh Bolivia Botswana Brazil Bulgaria Canada Chile China Colombia Costa Rica

AME ASIA LAC AME LAC EEUR NAM LAC ASIA LAC LAC

DEVEL DEVEL DEVEL DEVEL DEVEL TRANS MADV DEVEL DEVEL DEVEL DEVEL

Indonesia Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Korea Kuwait Latvia

ASIA AME WEUR LAC ASIA AME ASIA AME ASIA AME EEUR

DEVEL ADV MADV DEVEL MADV DEVEL TRANS DEVEL ADV DEVEL TRANS

Oman Pakistan Panama Papua New Guinea Peru Philippines Poland Portugal Qatar Romania Russia Saudi Arabia Singapore Slovakia Slovenia

LAC ASIA EEUR WEUR AME EEUR EEUR AME ASIA EEUR EEUR

DEVEL DEVEL TRANS ADV DEVEL TRANS TRANS DEVEL ADV TRANS TRANS (continued on next page)

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Appendix B (continued)

Country

Region

Development status

Country

Region

Development status

Country

Region

Development status

Croatia Cyprus Czech Denmark Ecuador El Salvador Finland France Gambia Germany Ghana Greece Guyana Honduras

EEUR WEUR EEUR WEUR LAC LAC WEUR WEUR AME WEUR AME WEUR LAC LAC

TRANS ADV TRANS ADV DEVEL DEVEL ADV MADV DEVEL MADV DEVEL ADV DEVEL DEVEL

Lebanon Lithuania Macedonia Malawi Malaysia Malta Mauritius Moldova Morocco Namibia Netherlands Nicaragua Niger Nigeria

AME EEUR EEUR AME ASIA WEUR AME EEUR AME AME WEUR LAC AME AME

DEVEL TRANS TRANS DEVEL DEVEL DEVEL DEVEL TRANS DEVEL DEVEL ADV DEVEL DEVEL DEVEL

South Africa Spain Sri Lanka Swaziland Sweden Switzerland Thailand Trinidad Tunisia Turkey UAE Ukraine USA Venezuela

AME WEUR ASIA AME WEUR WEUR ASIA LAC AME WEUR AME EEUR NAM LAC

DEVEL ADV DEVEL DEVEL ADV ADV DEVEL DEVEL DEVEL DEVEL DEVEL TRANS MADV DEVEL

Notes: AME = Africa and Middle East, ASIA = Asia Pacific, AUST = Australia, EASTEUR = Eastern Europe, LAC = Latin America and Caribbean, NAM = North America, WESTEUR = Western Europe; MADV = Major advanced, ADV = Advanced, TRANS = Transition, DEVEL = Developing; Countries were assigned in the regions following the classification of the Global Market Information Database. Countries were classified in the four categories by development status by combining information from the International Monetary Fund and the European Bank for Reconstruction and Development.

Appendix C. Spearman rank-order correlation among the efficiency scores created by common and separate frontiers Panel A: Cost rank-order correlation Model A1

Model A2

Model A2

0.8578** 0.8779** 0.9142** 0.9407**

0.8568** 0.9428** 0.9917** 0.9431**

0.8172** 0.9155** 0.8566** 0.9334**

Model B1

Model B2

Model B3

0.9108** 0.7824** 0.9307** 0.8963**

0.8831** 0.7856** 0.9392** 0.8586**

0.8303** 0.8996** 0.9562** 0.9336**

Panel A: Cost rank-order correlation Major advanced Advanced Transition Developing

Panel B: Profit rank-order correlation Major Advanced Advanced Transition Developing

Notes: *Correlation is statistically significantly different from zero at the 5% level, two-sided. **Correlation is statistically significantly different from zero at the 1% level, two-sided; Models A1 and B1 are traditional models with two outputs, namely, loans and other earnings; Models A2 and B2 include an additional output namely off-balance sheet activities (OBS); Models A3 and B3 are re-estimations of Models A2 and B2 but with non-interest income instead of OBS; Common Frontier refers to a global frontier. Separate frontiers have been estimated by level of economic development. All estimates were obtained using the Battese and Coelli (1995) model.

References Akhigbe, A., McNulty, J.E., 2003. The profit efficiency of small US commercial banks. Journal of Banking and Finance 27, 307–325. Altunbas, Y., Chakravarty, S.P., 2001. Frontier cost functions and bank efficiency. Economic Letters 72, 233–240. Altunbas, Y., Liu, M.-H., Molyneux, P., Seth, R., 2000. Efficiency and risk in Japanese banking. Journal of Banking and Finance 24, 1605–1628. Barth, J.R., Caprio, G., Levine, R., 2001. The regulation and supervision of banks around the world: A new database. In: Litan, R.E., Herring, R. (Eds.), Integrating Emerging Market Countries into the Global Financial System. BrookingsWharton Papers in Financial Services. Brooking Institution Press, Washington. Barth, J.R., Nolle, D.E., Phumiwasana, T., Yago, G., 2003. A cross-country analysis of the bank supervisory framework and bank performance. Financial Markets, Institutions and Instruments 12, 67–120. Barth, J.R., Caprio, G., Levine, R., 2004. Bank regulation and supervision: What works best? Journal of Financial Intermediation 13, 205–248.

Barth, J.R., Caprio, G., Levine, R., 2006. Rethinking Bank Regulation: Till Angels Govern. Cambridge University Press, New York. Barth, J.R., Caprio, G., Levine, R., 2007. The microeconomic effects of different approaches to bank supervision. In: Haber, S., North, D., Weingast, B. (Eds.), The Politics of Financial Development. Stanford University Press, California. Barth, J.R., Caprio, G., Levine, R., 2008. Bank Regulations are Changing: For Better or Worse? Policy Research Working Paper 4646. World Bank. Battese, G.E., Coelli, T.J., 1995. A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics 20, 325–332. Bauer, P.W., Berger, A.N., Ferrier, G.D., Humphrey, D.B., 1998. Consistency conditions for regulatory analysis of financial institutions: A comparison of frontier efficiency methods. Journal of Economics and Business 50, 85–114. Beck, T., Demirguç-Kunt, A., Levine, R., 2006a. A New Database on Financial Development and Structure (1960–2006). World Bank. Beck, T., Dermiguc-Kunt, A., Levine, R., 2006b. Bank supervision and corruption in lending. Journal of Monetary Economics 53, 2131–2163. Berger, A.N., Bonaccorsi di Patti, E., 2006. Capital structure and firm performance. A new approach to testing agency theory and an application to the banking industry. Journal of Banking and Finance 30, 1065–1102. Berger, A.N., DeYoung, R., 1997. Problem loans and cost efficiency in commercial banks. Journal of Banking and Finance 21, 849–870. Berger, A.N., Humphrey, D.B., 1997. Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research 98, 175–212. Berger, A.N., Mester, L.J., 1997. Inside the black box: What explains differences in the efficiencies of financial institutions? Journal of Banking and Finance 21, 895–947. Berger, A.N., Hasan, I., Zhou, M., 2009. Bank ownership and efficiency in China: What will happen in the world’s largest nation? Journal of Banking and Finance 33, 113–130. Bos, J.W.B., Kolari, J.W., 2005. Large bank efficiency in Europe and the United States: Are there economic motivations for geographic expansion in financial services? Journal of Business 78, 1555–1592. Bos, J.W.B., Schmiedel, H., 2007. Is there a single frontier in a single European banking market? Journal of Banking and Finance 31, 2081–2102. Clark, A.J., Siems, T.F., 2002. X-efficiency in banking: Looking beyond the balance sheet. Journal of Money, Credit and Banking 34, 987–1013. Coelli, T., Battese, G., 1996. Identification of factors which influence the technical inefficiency of Indian farmers. Australian Journal of Agricultural Economics 40, 103–128. Coelli, T.J., Prasada Rao, D.S., O’Donnell, C.J., Battese, G.E., 2005. An Introduction to Efficiency and Productivity Analysis, second ed. Springer, USA. Demirguc-Kunt, A., Laeven, L., Levine, R., 2004. Regulations, market structure, institutions, and the cost of financial intermediation. Journal of Money, Credit and Banking 36, 593–622. DeYoung, R., Hasan, I., 1998. The performance of de novo commercial banks: A profit efficiency approach. Journal of Banking and Finance 22, 565–587. Dietsch, M., Lozano-Vivas, A., 2000. How the environment determines banking efficiency: A comparison between French and Spanish industries. Journal of Banking and Finance 24, 985–1004.

A. Lozano-Vivas, F. Pasiouras / Journal of Banking & Finance 34 (2010) 1436–1449 Djankov, S., La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2008. The law and economics of self-dealing. Journal of Financial Economics 88, 430–465. Färe, R., Grosskopf, S., Weber, W.L., 2004. The effect of risk-based capital requirements on profit efficiency in banking. Applied Economics 36, 1731–1743. Hasan, I., Koetter, M., Wedow, M., 2009. Regional growth and finance in Europe: Is there a quality effect of bank efficiency? Journal of Banking and Finance 33, 1446–1453. Jagtiani, J., Nathan, A., Sick, G., 1995. Scale economies and cost complementarities in commercial banks: On-and off-balance-sheet activities. Journal of Banking and Finance 19, 1175–1189. Kasman, A., Yildirim, C., 2006. Cost and profit efficiencies in transition banking: The case of new EU members. Applied Economics 38, 1079–1090. Kopecky, K.J., VanHoose, D., 2006. Capital regulation, heterogeneous monitoring costs, and aggregate loan activity. Journal of Banking and Finance 30, 2235– 2255. Koutsomanoli-Filippaki, A., Margaritis, D., Staikouras, C., 2009. Efficiency and productivity growth in the banking industry of Central and Eastern Europe. Journal of Banking and Finance 33, 557–567. Lensink, R., Meesters, A., Naaborg, I., 2008. Bank efficiency and foreign ownership: Do good institutions matter? Journal of Banking and Finance 32, 834–844. Lieu, P., Yeh, T., Chiu, Y.-H., 2005. Off-balance sheet activities and cost inefficiency in Taiwan’s banks. The Service Industries Journal 25, 925–944. Lozano-Vivas, A., Pastor, T.J., Pastor, M.J., 2002. An efficiency comparison of European banking systems operating under different environmental conditions. Journal of Productivity Analysis 18, 59–77. Lundvall, K., Battese, G.E., 2000. Firm size, age and efficiency: Evidence from Kenyan manufacturing firms. The Journal of Development Studies 36, 146–163. Maudos, J., Pastor, J.M., Perez, F., Quesada, J., 2002. Cost and profit efficiency in European banks. Journal of International Financial Markets, Institutions and Money 12, 33–58.

1449

Mester, L.J., 1996. A study of bank efficiency taking into account risk-preferences. Journal of Banking and Finance 20, 1025–1045. Pasiouras, F., 2008a. Estimating the technical and scale efficiency of Greek commercial banks: The impact of credit risk, off-balance sheet activities, and international operations. Research in International Business and Finance 22, 301–318. Pasiouras, F., 2008b. International evidence on the impact of regulations and supervision on banks’ technical efficiency: An application of two-stage data envelopment analysis. Review of Quantitative Finance and Accounting 30, 187– 223. Pasiouras, F., Tanna, S., Zopounidis, C., 2009. The impact of banking regulations, on banks’ cost and profit efficiency: Cross-country evidence. International Review of Financial Analysis 18, 294–302. Rime, B., Stiroh, K.J., 2003. The performance of universal banks: Evidence from Switzerland. Journal of Banking and Finance 27, 2121–2150. Rogers, K.E., 1998. Nontraditional activities and the efficiency of US commercial banks. Journal of Banking and Finance 22, 467–482. Rossi, S.P.S., Schwaiger, M.S., Winkler, G., 2009. How loan portfolio diversification affects risk, efficiency and capitalization: A managerial behavior model for Austrian banks. Journal of Banking and Finance 33, 2218–2226. Siems, T.F., Clark, J.A., 1997. Rethinking bank efficiency and regulation: How offbalance-sheet activities make a difference. Federal Reserve Bank of Dallas, Financial Industry Series. Stiroh, K.J., 2000. How did bank holding companies prosper in the 1990s? Journal of Banking and Finance 24, 1703–1745. Tortosa-Ausina, E., 2003. Nontraditional activities and bank efficiency revisited: A distributional analysis for Spanish financial institutions. Journal of Economics and Business 55, 371–395. VanHoose, D., 2007. Theories of bank behavior under capital regulation. Journal of Banking and Finance 31, 3680–3697.