Cost and profit efficiency of convention

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J Prod Anal (2010) 34:45–62

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DOI 10.1007/s11123-009-0161-7

Cost and profit efficiency of conventional and Islamic banks in GCC countries

Samir Abderrazek Srairi

Published online: 28 November 2009

Abstract Using stochastic frontier approach, this paper investigates the cost and profit efficiency levels of 71 commercial banks in Gulf cooperation council countries over the period 1999–2007. This study also conducts a comparative analysis of the efficiency across countries and between conventional and Islamic banks. Moreover, we examine the bank-specific variables that may explain the sources of inefficiency. The empirical results indicate that banks in the Gulf region are relatively more efficient at generating profits than at controlling costs. We also find that in terms of both cost and profit efficiency levels, the conventional banks on average are more efficient than Islamic banks. Furthermore, we observe a positive corre- lation of cost and profit efficiency with bank capitalization and profitability, and a negative one with operation cost.

Higher loan activity increases the profit efficiency of banks, but it has a negative impact on cost efficiency.

Keywords Banking · Cost efficiency · Profit efficiency · Islamic banks · Stochastic frontier approach ·

GCC countries

JEL classification C30 · G21

1 Introduction

The banking industry around the world has undergone profound and extensive changes over the last two decades.

S. A. Srairi (&)

Riyadh Community College, King Saud University, Kingdom of Saudi Arabia, P.O. Box 28095, Riyadh 11437, Kingdom of Saudi Arabia

e-mail: [email protected]; [email protected]

The globalization of financial markets and institutions which has been accompanied by government deregulation, financial innovations, information revolution and advanced application in communication and technology, has created a competitive banking environment and modified the technology of bank production. Due to these developments and changes in the modern banking field, banks are trying to operate more efficiently in terms of cost and profit in order to stay competitive (Karim and Gee 2007). Moreover, to assist banks in confronting these challenges, financial authorities in both developed and developing countries have implemented various measures to restructure their financial sectors and to promote a deregulated banking environment. Consistent with the transformation of the banking sectors throughout the world, the literature related to the performance and the efficiency of banks is proliferating, and the majority of these studies cover the US and Euro- pean countries. However, a little empirical work has been undertaken to investigate efficiency in Arabian banking, and especially in Gulf countries despite the importance of this region on political and economic levels.

To fill this gap and to contribute to the existing literature, the main objective of this study is to provide more information on the efficiency of the banking industry in the six Gulf cooperation council (GCC) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates). Thus, we analyze the cost and profit efficiency of GCC banking employing a parametric approach, and using a panel data of 71 commercial banks over a recent period 1999–2007. This paper has extended the literature in two directions. First, to our knowledge, this is the first empirical study that has analyzed profit efficiency of commercial banks in the Gulf region. Second, cost and profit efficiency levels are compared between conventional and Islamic banks in this region.

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Founded in May 1981, the GCC countries produce about 23% of the world’s oil and control more than 40% of the world’s oil reserves. On average, oil represents more than 80% of export receipts and budget revenues, respectively.¹ Over the last 6 years, the GCC incomes grew substantially as a result of the increase in oil prices. In consequence, the economies of these countries show growth rates much above the world average, and are in a relatively strong position as compared to 10 years ago. In 2001, the GCC states decided to establish a common market by 2007, and a monetary union, and to have a single currency before 2010. These goals are likely to promote policy coordina- tion, reduce transaction cost, and provide a more stable environment for business and facilitate investment deci- sions. To reach these objectives and in response to the globalization of financial markets, the financial and monetary authorities in GCC countries, during the last decade, have adopted financial sector liberalization programs to free their economies. These measures included liberalizing trade, encouraging foreign direct investment (FDI), interest rates liberalization, allowing entry of new private banks both domestic and foreign, strengthening the central bank’s supervisory capacity, and implementing regulations that helped in progressively moving the Gulf states toward market-based economies (Elton 2003; Al-Obaidan 2008).

The GCC countries have a fairly high number of banks with an extensive network of branches. But Gulf banks are still small compared to the big international banks. Most banks are family-owned with modest government equity and a large number of specialized banks are fully owned by the government (Elton 2003). Banks in these countries are financially strong, well capitalized and have adopted modern banking services (Srairi 2009). Their operations can be characterized by satisfactory asset quality, adequate liquidity and high levels of profitability (Islam 2003a).

Local banks follow international account standards (IAS) and the central monetary authorities of Gulf countries have strengthened the prudential norms in recent years (Islam 2003b). Furthermore, one important group of banking services that have experienced rapid growth in GCC countries is the Islamic financial services. In 2007, Gulf States capture about 35% ($178 billion) of the total assets of Islamic banks. These are mainly concentrated in Bah- rain, Kuwait and the UAE. During the last 10 years, the concept of Islamic banking has likewise developed to cover activities of other types of financial institutions including insurance, investment and fund management companies. Moreover, to take advantage of Islamic financial instru- ments, many conventional banks in GCC countries have added Islamic banking services to their regular banking operations.

1 Statistics of Global Investment House (2007).

Despite the very favourable economic environment in GCC countries and the robust growth of both conventional and Islamic commercial banks, the Gulf banking industry is facing many challenges especially in view of the pressures of globalization and the changes in the world economy and the impact of the latest financial crises on GCC economy. These changes have a direct impact on the banks’ main activities, and on their performance and ability to develop and expand their international competitive activities. Due to these changes and the new competition from foreign banks and non-financial companies, banks in GCC countries were induced to improve their productive perfor- mances by reducing their costs, controlling the price of funds and improving the pricing and mix of their outputs. This study, using the bank-scope database, focuses on the analysis of cost and profit efficiency of the commercial banks in GCC countries in order to provide some interesting insights on the efficiency of the Gulf banking sys- tems that could be used by managers and policy makers operating in these countries. Thus, the purpose of this paper is threefold. First, we estimate a stochastic cost and profit frontiers using a specific functional form (standard translog function). To follow Perera et al. (2007) and Mamatzakis et al. (2008), country-level variables are incorporated in the common cost and profit frontiers to account for variation in banking technologies that may be related to macro-eco- nomics conditions and to structural and institutional fea- tures of a country. In this research, we use the maximum likelihood procedure of Battese and Coelli (1995) that permits in a single step to estimate the parameters of the cost and profit frontiers and to investigate the determinants of bank efficiency. As a second step in the analysis, we calculate and compare the cost and alternative profit efficiency scores between country and type of banks. The study of the differences in efficiency among GCC countries will explain the competitive starting position of each country, which may also shed light on the capacity to respond to the new changing environment. Level of bank efficiency is also compared between conventional and Islamic commercial banks in order to provide information on comparative managerial performance. This comparison is related to a controversial question about the impact of type of banks on efficiency in the banking industry (Hasan 2004). Measuring the cost efficiency of 34 commercial banks in Malaysia, Majid et al. (2003) show that the efficiency of Islamic banks is not statistically different from the conventional banks. However, other studies (Saaid et al. 2003; Kabir Hassan 2005) conclude that Islamic banking industry is relatively less efficient compared to their conventional counterparts. Finally, yet not less importantly, we also explore the impact of certain factors that may be correlated with bank’s efficiency. Indeed, we include in the cost and profit functions (inefficiency term)

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a

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bank-specific variables such as size, capital adequacy, profitability, operation cost and credit risk.

The paper is structured as follows: in the next section, we discuss the studies on efficiency especially in the Gulf banking industry. Section 3 presents the methodology and the econometric model used to estimate the common cost and profit frontiers. The data and variables concerning outputs, input prices, country-level and bank specific are described in Sect. 4. Section 5 explains the empirical results of the cost and profit efficiency of commercial banks in GCC countries, while the final section summarizes and concludes this study.

2 Literature review

Over the last decades, there has been an extensive literature on the cost and profit efficiency of financial institutions in the competitive banking markets of Western Europe and North America² (e.g., Dietsch and Lozano- Vivas 2000; Berger and Mester 1997; Altunbas et al. 2001;

Weill 2004; Pasiouras 2008). More recently, there have been some studies on countries in transition (e.g., Fries and Taci 2005; Bonin et al. 2005; Kasman and Yildirim 2006;

Mat- matzakis et al. 2008). However, empirical research on bank efficiency in Arabic countries appears relatively scarce (e.g., Bouchaddakh and Salah 2005 in Tunisia; Al- Fayoumi and AlKour 2008 in Jordan). A few studies using single country (Limam 2001; Darrat et al. 2003) or cross- country comparison (Grigorian and Manole 2005; Ariss et al. 2007; Ramanathan 2007) have been done on GCC countries.

Our aim in this section is to survey key studies on efficiency in Gulf banking and summarize the most significant results.

In a study of cost and technology efficiency in Kuwait, Darrat et al. (2003) employed the data envelopment analysis (DEA) to estimate a number of efficiency indices for banks over a period between 1994 and 1997. They find that cost efficiency of Kuwaiti banks averages about 68% and that the sources of the inefficiency are a combination of allocative (regulatory) and technical (managerial) inefficiency. The results also indicate that larger banks are less efficient than smaller ones, and that profitability is positively related to efficiency indices.

For the same country, Limam (2001) estimates the technical efficiency of eight Kuwaiti banks from 1994 to 1999, using the stochastic cost frontier approach. The author follows the intermediation approach and finds that the average

through merging with other banks, than from reducing notably their technical inefficiency. Finally, the results show that larger bank size, higher share of equity capital in assets and greater profitability are associated with better efficiency.

In addition to the single-country studies of cost efficiency in Gulf banking, there have been three recent cross- country studies, Grigorian and Manole (2005), Ariss et al.

(2007), and Ramanathan (2007).

Griogorian and Manole (2005) compare the efficiency indicators of banks for the period 1997–2002 with that of their counterparts in Kuwait, Qatar, the United Arab Emirates, and Singapore, obtained by using DEA approach. The results of this study show that, on average, banks in Bahrain are more technical efficient compared to other GCC countries, but they still lag behind their Singaporean counterparts. The paper also finds that in terms of scale efficiency, banks in Bahrain operate at the same level as banks in Singapore. In addition, the findings of these authors reveal that the inefficiencies seem to be largely caused by pure technical inefficiency and to a lesser extent by scale inefficiency.

The most recent study by Ariss et al. (2007) uses a non- parametric frontier approach (DEA with constant return to scale (CRS) assumption) to compare cost efficiency and Malmquist productivity index (MPI) of 45 banks operating in the six GCC countries during the period 1999–2004. They find an average overall efficiency scores of about 78% for all banks in GCC countries. They also find that there is a decline in the overall efficiency index from 1999 to 2004.

This decline is caused by the decrease in allocative rather than technical efficiency (and its component of pure technical rather than scale efficiency). The results of country specific efficiency indices indicate that banks in Oman on average have been the most efficient among GCC countries followed narrowly by banks from Bahrain and Kuwait, with Saudi Arabia being the least efficient. Finally, the findings of the MPI show that between 1999 and 2004, GCC banks on average have experienced a decline in the productivity of their banking system albeit with different degree. The decline in productivity of banking in Kuwait, Oman, and Qatar was due to both technological regress and decline in overall technological efficiency. However, for Bahrain, Saudi Arabia and UAE, the decline in MPI was the net results of technological regress and improvement in overall technical efficiency.

To assess the efficiency of banks in GCC countries, Ramanathan (2007) examines nearly the same sample (over 9 banks), the same period (2000–2004), and uses the same approach (MPI and DEA: CRS and VRS³) as that cost efficiency is 91% for all banks. He also finds that banks

produce earning assets at constant returns to scale and hence have less to gain from increasing scale of production,

2 See the survey article by Berger and Humphrey (1997).

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3 DEA can run under either CRS or VRS. The main difference between these two models is the treatment of returns to scale. The VRS model ensures that a bank is compared only with banks of a similar size, while the CRS assumption is only justifiable when all banks are operating at an optimal scale.

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adopted by Ariss et al. (2007). He finds, under CRS assumption, that, for the year 2004, all six GCC countries have at least one CRS efficient bank, and all the countries have registered their CRS efficiencies reasonably close the GCC average (90.1%). When the variable return to scale (VRS) assumption is implemented in DEA, all GCC countries have at least two VRS efficient bank, and the average VRS efficiencies (94.2%) is larger than the cor- responding average CRS efficiencies. The study also reveals that all GCC countries have registered reductions in productivity in terms of technology change (a similar result was reached by Ariss et al. 2007). However, banks in four of the six GCC countries (Bahrain, Kuwait, Saudi Arabia, and the UAE) registered progress in terms of MPI during 2000–2004. The highest improvement in MPI (1.009) is registered by the selected banks in Bahrain, while the selected banks in Qatar have presented the highest reductions in productivity during the same period.

Our study differs from the existing literature on banking efficiency in GCC countries on several points: First, we use a larger number of banks (71). Second, we cover a wider range of bank types: conventional and Islamic, and for a longer period of time (9 years). Third, it is the first study of Gulf banking efficiency to consider both cost and profit efficiency using a parametric method (SFA). Fourth, to estimate cost and profit frontier functions, we have intro- duced country-specific variables to account for variation in banking technologies that may be related to macroeconomic conditions and to the structure of the banking sector of a particular country.⁴ Fifth, our paper compares cost and profit efficiencies scores between country and type of banks (conventional and Islamic banks). Finally, this study tries to identify the possible factors explaining the observed differences of cost and profit efficiency between banks in GCC countries.

3 Methodology

In this study, we examine cost and profit efficiency rather than technical efficiency.⁵ According to Pasiouras et al.

(2008), cost efficiency is a wider concept than technical efficiency, since it refers to both technical and allocative efficiency. Likewise, the profit efficiency is also a wider concept as it combines both costs and revenues in the measurement of efficiency.

The definitions of cost and profit efficiency correspond, respectively, to two important economic objectives: cost minimization and profit maximization. Isik and Hassan

4 These variables will be explained in detail in Sect. 4.2.2.

5 Technical efficiency is the ability to produce the maximum output for a given bundle of inputs.

(2002) defined cost efficiency as a measure of how far bank’s cost is from the best practice bank’s cost if both were to produce the same output under the same environmental conditions. It is measured as the ratio between the minimum cost at which it is possible to attain a given volume of production and the observed costs for firm. A cost efficiency score of 0.85 would mean that the bank is using 85% of its resources efficiently or alternatively wastes 15% of its costs relative to a best-practice bank.

Profit efficiency is a broader concept than cost efficiency since it takes into account the effect of the choice of vector of production on both cost and revenues (Ariff and Can 2008). It is defined as the ratio between the actual profit of a bank and the maximum level that could be achieved by the most efficient bank (Maudos et al. 2002). In other words, the number represents the per- cent of the maximum profits that a bank is earning. Thus, profit efficiency level equal to 0.75 means that a bank is losing 25% in terms of profit fund. Two different versions of the profit efficiency concept can be distinguished depending on whether or not market power or output a price is taken into account (Berger and Mester 1997). The standard profit efficiency (SPE) estimates how close a bank is to producing the maximum possible profit given a particular level of input prices and output prices. In this case, the profit function assumes that markets for outputs and inputs are perfectly competitive. In contrast, the alternative profit efficiency (APE) developed by Humphrey and Pulley (1997) assumes the existence of imperfect competition or firms that have market power in setting output prices. In this approach, banks take as given the quantity of outputs and the price of inputs and maximize profits by adjusting the price of outputs and the quantity of inputs, unlike the standard profit efficiency concept. Since our sample includes several countries with different levels of competition, it seems more appropriate to use alternative profit efficiency than standard profit efficiency. Moreover, the latter concept requires information on output prices which is not available.

To examine the efficiency of banks using frontier approaches, there are two models. Parametric technique, such as stochastic frontier analysis (SFA), thick frontier approach (TFA) and distribution free approach (DFA), uses econometric tools and specifies the function form for the cost or profit function. On the contrary, the non- parametric approaches (such as DEA) and free disposable hull analysis (FDHA) do not make an assumption concerning the functional form of frontier and use a linear program to calculate efficiency level. In the present study, we use the SFA, as developed by Aigner et al. (1977), to estimate cost and profit efficiency frontier. The main advantage of SFA over DEA is that it allows us to distinguish between inefficiency and other stochastic shocks in the estimation of efficiency levels. In addition,

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by using this model, it would be easier to add control variables, such as country-level variables, in

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w

v

u

the equation of this model than in non-parametric tech- niques. Hence, this approach allows us to compare efficiency between country, and the efficiency of conventional and Islamic banks.⁶ We illustrate the methodology using cost efficiency first and discuss its application to the profit function later.

In line with the recent developments in the literature (Fries and Taci 2005; Perera et al. 2007; Mamatzakis et al.

2008) and in order to capture heterogeneity across countries, the cost function in this study is extended to accommodate country-specific variables and thus appears as follows:

function of a set of bank-specific characteristics. In order to model inefficiency, we use the following auxiliary model:

u_ijt¼ oZ_ijtþ w_ijt ð2Þ

where Z is a vector of explanatory bank-specific variables, w represents a random variable which has a truncated normal distribution (wijt * N (0, r² ), and q is a vector of unknown parameters to be estimated.

For our cost function, we choose the translog specification.⁷ According to Greene (1980), this function is the most frequently selected model to measure bank efficiency, because it presents the well-known advantage of being a flexible functional form. Moreover, it includes, as a par- TCijt ¼ f Pijt ; Yijt ;

Eijt

þ eijt and eijt ¼ vijt þ uijt ð1Þ ticular case, the Cobb-Douglas specification (Carvallo and where TC is total cost including both interest expenses and

operating costs, P is the vector of outputs (loans and Kasman 2005).

The translog stochastic cost takes the following form:

investment), Y is a vector of input prices (price of labor

and funds), and E is a vector of country-specific variables. X²

ln TCijt ¼ a0 þ X²

a_mln Y_m;ijtþ b_sln Ps;ijt þ l1T The detailed definitions of these variables are presented,

along with those of other variables used in Eq. 2 in Table 2. This approach assumes that total cost deviates from the optimal cost by a random disturbance vijt and an

m¼1

X⁸

þ q_lln Ejt þ 1=2

l¼1

" X^s¼1² X²

m¼1 n¼1

a_m;nln Y_m;ijt

# inefficiency term u_ijt. vijt corresponds to random fluctua- ω ln Y_n;ijtXþ ² X²b_s;rln Ps;ijt ω ln P_r;ijtþ l₂T² tions, it is a two-sided classical statistical error term that

incorporates the effect of errors of measurement of the

explanatory variables. vijt is assumed i.i.d. with [vijt * N X² X² þ

s¼1 r¼1

u_m;sln Y_m;ijtω ln P_s;ijt (0, r²)]. The second error term u_ijt captures inefficiency ^{m¼1 s¼1}

effects, and is assumed to follow an asymmetric half normal distribution in which both the mean u and variance r² may vary. The general procedure adopted in this study is to

X þ2

m¼ 1

k_mT ln Ym;ijt

þ

X

2 s¼ 1

WsT ln Ps;ijt þ e ð3Þ

estimate coefficients and e of Eq. 1, and to calculate efficiency score for each bank in the sample. The cost frontier can be estimated by maximum likelihood, and efficiency levels are estimated using the regression error. In the estimation, the terms r² and r² are reparameterized by

where subscripts i denote banks, j countries and t time horizon and lnTC the natural log of total costs, ln Ym the natural log of input prices, ln Ps, the natural log of output values, while E is a vector of country-level variables in natural log. T is the time trend variable used to capture

u v

r² = r²? r² and c = r²/r². The parameter, c, lies technical change; a, b, l, q, A, k, and w are the parameters

u v u

between 0 and 1. If it is close to zero, little inefficiency exists and the model can be consistently estimated using ordinary least squares. But a large value of c suggests a deterministic frontier (Coelli 1996).

to be estimated, and e the composite error term. To ensure that the estimated cost frontier is well behaved (Fries and Taci 2005), we impose constraints on symmetry:

am;n ¼ an;m 8m; n; and b_s;r¼ b_r;s8s; r The measure of cost efficiency for any bank at time t is

calculated from the estimated frontier as CEit = 1/exp (uit).

This measure takes a value between 0 and 1. Banks with scores closer to one are more efficient.

Homogeneity in prices P²

km ¼ 0:

m

P

2 m¼ 1

P am ¼ 1;n

m

P a_m;n¼s

m

u_m;s¼

In order to identify factors that are correlated with bank inefficiency, we use the model of Battese and Coelli (1995) which permits in a single–step to calculate individual bank efficiency score (Eq. 1) and to investigate the determinants of inefficiency (Eq. 2). Specifically, u is

assumed to be a

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Moreover, the linear homogeneity conditions are imposed by normalizing TC and Ym (the price of labor

and the price of funds) by the price of physical capital before the log transformation.

6 The thick frontier approach (TFA) only provides average efficiency scores for the whole sample.

7 Berger and Mester (1997) have compared the translog to the alternative fourrier flexible form. They find negligible difference between both methods.

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In this study we also employ the profit efficiency concept that implies that managers should not only pay attention to reducing a marginal dollar of costs, but also to raising a marginal dollar of revenue. Our approach follows Pulley and Humphrey (1993) and Berger et al. (1996) by assuming that firms have some market power in output markets. Hence we choose alternative profit function (APE) which takes output quantities as given instead of taking output prices as given. This approach incorporates differences across banks in market power and their ability to exploit it (Dietsch and Weill 1999). For the APF, we use the same translog form of the cost function, except that total costs in Eq. 3 are replaced by total profits before tax.

To avoid a log of negative number, we transform the profit variable as follows: ln (p ?h ?1), where h indicates the absolute value of the minimum value of profit (p) over all banks in sample. Thus for the bank with the lowest profit value for the year, the dependent variable of profit function will be equal to ln (1) = 0. Also for measuring efficiency score under the profit function the composite error is e = vi-ui.

The measure of profit efficiency is defined as PEit = exp (-uit). In this case efficiency scores take a value between 0 and 1 with values closer to one indicating a fully efficient bank.

The stochastic frontiers for cost and profit are estimated using Frontier version 4.1 program developed by Coelli’s (1996). The software estimates in a single–step the cost or profit model using maximum likelihood estimation technique, and identifies potential correlates of the cost and profit efficiency scores.

4 Data and definition of variables 4.1 Data

Our sample is an unbalanced panel data of 71 commercial banks (48 conventional and 23 Islamic) from six GCC countries: 14 banks in Bahrain, 11 banks in Kuwait, 5 banks in Oman, 8 banks in Qatar, 11 banks in Saudi Arabia, and 22 banks in the United Arab Emirates.

Altogether the

Since all countries have different currencies, all the annual financial values are converted in US dollar using appropriate average exchange rates for each year. Also, to ensure comparability of data across countries, all values are deflated to the year 1999 using each country’s consumer price index (CPI).

4.2 Variables definition for estimation of cost and profit efficiency functions

4.2.1 Outputs, input prices, total cost, and total profit In the present study, and following the most recent studies in the field, we adopt the intermediation approach to define bank outputs and inputs in both cost and profit models.

According to Bos and Kool (2006), this approach is appropriate when the banks in the sample operate as inde- pendent entities. In this method, banks are viewed as financial intermediates that collect purchased funds and use labor and capital to transform these funds into loans and other earning assets. In the alternative production approach, banks are assumed to produce deposits, loans and investments services, using labor, physical capital and financial capital as inputs. Bank branch efficiency studies frequently use this method. Berger and Humphrey (1997) argue that the intermediation approach is superior because the majority of banks’ expenses are interest related.

In the cost and profit models, we consider two outputs⁸: net total loans (total customer loans) and other earning assets which include in the IBCA terminology investment securities, inter-bank funds and other investments. The input prices are: the price of capital, measured by the ratio of non-interest expenses (operating cost net of personnel expenses) to total fixed asset, the price of funds, computed by dividing interest expenses⁹ to total deposits, and the price of labor. Due to the lack of information about the number of employees,¹⁰ we follow Altunbas et al. (2000), and use a proxy measure of labor price by using the ratio of personnel expenses divided by total assets. For the dependent variable, total cost is defined as interest and non- interest costs in cost efficiency function. In the case of profit function, total profit is measured by net profit before

final data set contains 594 observations over the period 1999–2007 (see Table 1). All data on the bank’s balance

sheets and income statements are obtained mainly from bankscope database of BVD-IBCA (June 2008) which provides homogenous classification of banks and information. In the case of missing information, we use annual reports provided by individual banks via their websites.

The sources of macroeconomic data and the structure of banking industry for the GCC countries are the central banks annual reports of the respective countries and the

international financial statistics (IFS).

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8 For Islamic banks, loans = Islamic operations = Murabaha receiv- able ? Mudaraba investments ? Musharaka investments ? loans without interest (Qard hasan) ? loans with service charge (Ju- ala) ? other short operations (e.g., investment in Ijara assets:

leasing); other earning assets = equity investments ? investment in associates ? investment securities (Islamic bond: Sukuk). For details of Islamic financing contracts see (e.g., Archer et al. 1998;

Zahar and Hassan 2001; Rosly 2005).

9 In case of Islamic banks, interest expenses represent profits distributed to depositors.

10 Bankscope database does not provide information on the number of employees for each bank.

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Table 1 Number of sample Country Number of banks Number of observations banks by country and type by country

Total Islamic Conventional

Bahrain 14 7 7 119

Kuwait 11 5 6 84

Oman 5 0 5 45

Qatar 8 2 6 68

Saudi Arabia 11 2 9 93

U.A.E 22 7 15 185

Total 71 23 48 594

tax earned by the bank, to avoid the bias of differences in tax regimes between GCC countries.

4.2.2 Country-level variables

To identify the common frontier, we include several country-level variables in the estimation of the cost and profit functions. Based on previous studies (Fries and Taci 2005;

Carvallo and Kasman 2005; Perera et al. 2007), these variables are categorized in two groups and include macroeconomic variables and a measure of the structure of the banking industry. The first group comprises five variables:

per capita GDP, Degree of monetization, density of demand, annual average of inflation and density of population. The

Table 2 Variables’description

Variables Definition

Dependant variable

TC: total cost Interest expenses ? personnel expenses ? other administration expenses ? other operating expenses

p : total profit Total income—total cost Input prices outputs and

Y1: price of labor Personnel expenses divided by total assets Y2: price of fund Interest expenses (interest paid) divided by

total deposits

Y3: price of physical Other administration expenses ? other capital operating expenses divided by fixed assets

P1: net total loans Total customer loans

definitions of these indicators and others (outputs, input prices, and bank-specific variables) are presented in Table 2.

Per capita GDP is used as the proxy for overall eco-

P2: other earning

assets Inter-bank funds ?investments securities (treasury bills ? government bonds ? other securities) ? other investments

nomic development. It also has an impact of the demand Country-specific variables

and supply for deposits and loans. This indicator is expected to be negatively associated with total costs and positively related to total profits. The ratio of money supply (M2) to the gross domestic product (GDP) measures the degree of monetization in the economy. The density of demand is measured as the total deposits of the banking sector divided by area in square kilometers. Banks that operate in an economic environment with a lower density of demand may have higher expenses to collect deposits and offer loans. The rate of inflation affects interest rate. Therefore, the higher these variables, the lower bank efficiency will be in activities such as risk management and credit screening. In a recent study on profit efficiency in the banking industry of four new European Union Member States, Koutsomanoli-Filippaki et al. (2008) show that

CGDP: per capita GDP

DMON: degree of monetization DDEM: density

of demand INFR: annual average rate of inflation DPOP: density

of population CONC: concentration

market

INTR: intermediation ratio

ACAP: average capital ratio

Ratio of GDP to total population

Broad money supply (M2) divided by GDP Total deposits of the banking sector to area (CPIt-CPIt-1)/CPTt-1

Total inhabitant divided by area

Assets of three largest banks to total assets of the sector

Total loans of the banking sector divided by total deposits

Total equity of the banking sector to total assets

banks in high inflation countries usually incur lower profits. Finally, banking efficiency may be affected also by the

Determinants of efficiency

Log (Ass): size Natural logarithm of total assets

ratio of inhabitants per square kilometer. Banks operating EQAS: capital

adequacy Equity to total assets

in areas of low population numbers may incur higher

banking costs. The second group includes market structure variables

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ROAA: profitability Net profit to average total assets LOAS: credit risk Loans to total assets COIN: operation cost Cost to income

that may affect banking technology and service quality. We

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selected three indicators: concentration ratio, intermediation ratio and average capital ratio. Concentration ratio is calculated as the assets of the three largest banks divided by the total assets of the sector. If higher concentration reflects market power for some banks, total cost is increased through slack and inefficiency. However, if concentration is the result of superior management and market selection of such banks, market concentration would be associated with lower costs because markets remain contestable (Dietsch and Lozano-Vivas 2000; Fries and Taci 2005; Lensink et al. 2008). The intermediation ratio is measured by total loans to total deposits. This variable is included in the cost and profit functions to capture differences among the banking sectors in terms of their capacity to convert deposits into loans. According to Carvallo and Kasman (2005), we expect an inverse relationship between this ratio and bank costs and a positive association with profits. As a proxy for the difference in the regulatory conditions among countries, we use the average capital ratio. It is measured by equity over total assets and a negative association with total costs is expected because less equity implies higher risk taken at greater leverage.

4.3 Determinants of efficiency

Once the cost and profit efficiency scores are calculated, we examine internal factors that may explain the differences in efficiency across banks. For this objective, we follow previous studies (Weill 2004; Ariff and Can 2008;

Pasiouras 2008), and we include in Eq. (2) five bank-specific variables: size, capital adequacy, profitability, operation cost and credit risk.

The natural logarithm of total assets is used as the proxy for bank size. An overview of research shows ambiguous results. According to Perera et al. (2007), but also Berger et al. (1993) and Miller and Noulas (1996), larger banks are more cost efficient than smaller banks, because large size allows wider penetration of markets and increase in revenue at a relatively less cost. However, some recent studies (Girardone et al. 2004; Dacanay 2007) report a significant negative relationship between bank size and efficiency. Capital adequacy is measured as equity divided by total assets. For many (e.g., Casu and Girardone 2004;

Pasiouras 2008), this variable is positively related to efficiency. Banks with higher ratio of equity to total assets have lower cost and profit inefficiency. A third variable, return on average assets, is included as a proxy for profitability. This ratio should be positively correlated with efficiency. Gen- erally, highly profitable banks are less cost and profit inefficient. The credit risk or loan quality is generally defined in the most banking efficiency studies (Mester 1996; Fries and Taci 2005; Das and Ghosh 2006) by the ratio of non-performing loans to total loans.

However, lack

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of data on non-performing loans especially in Islamic banks prevents us from utilising this ratio. Thus, this data limitation constrains us to proxy credit risk by another ratio: loans to total assets which has been utilized in some recent studies (Isik and Hassan 2002; Havrylchyk 2006;

Pasiouras 2008) as a measure of risk and of bank’s loans intensity. Banks which provide more loans are expected to be more efficient in profit as they take more risks (Maudos et al. 2002). However, in the case of Chinese banks, Ariff and Can (2008) find an inverse relationship between this variable and efficiency. They argue that banks which have a higher ratio of loan to total assets incur higher credit risk, and thus higher loan-loss provision, and are less efficient. Moreover, these banks provide a large proportion of loans to some inefficient state owned firms. The final variable includes the operation cost indicator. It is measured as cost to income, and is expected to be negatively related to efficiency.

5 Empirical results

The discussion of the results on the cost and profit efficiency of banks in GCC countries is organized into four parts. First, we describe the variables used in this paper by country and type of bank. Next, we analyze the parameters of cost and profit frontier obtained by the stochastic frontier approach. Third, we discuss and compare the cost and profit efficiency scores of banks by year, country and type of banks. Finally, we investigate the determinants of efficiency.

5.1 Summary descriptive statistics of the data

Table 3 displays some descriptive statistics by country for the variables used in the study. Comparing the average values across countries, we can then observe some differences regarding total cost and profit values, outputs, input prices and other bank-specific (panels A and B). The average cost to asset ratio is nearly similar in GCC countries; it ranges from 3.74% in Saudi Arabia to 5.24% in Kuwait. The same report is observed for the average profit efficiency measured by the ratio of profit before tax to total assets of banks. This variable varies from 2% in Oman to 3.17% in Bahrain. Regarding the levels of output, differences in average value are significant, especially in the ratio of net total loans to total assets which fluctuates from 40.77% in Bahrain to 69.72% in Oman. The difference is also greater when we see the ratio of other earning assets to total assets, which ranges between 12.82% in Oman to 33.21% in Bahrain.

However, the average prices of labor and funds seem to be show closer similarity between GCC countries.

Indeed, the price of labor (Y1) measured by the

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Table 3 Descriptive statistics of dataset by country (average values)

Variables UAE Saudi Arabia Bahrain Kuwait Qatar Oman

Panel A: cost and profit value, outputs and input prices

All variables are in percentage, except where indicated

ratio of personnel expenses to total assets, which has the lowest dispersion, fluctuates from 0.92% in Qatar to 1.35%

in Bahrain. Likewise, and to a lesser degree, the ratio of interest expenses to total deposits (Y2) varies from 2.42%

in Saudi Arabia to 4.53% in Bahrain; the highest interest, hence, was paid by banks in Bahrain, Qatar and Kuwait.

Turning to the variables that may affect the efficiency of a bank (panel B), we observe that there is a greater difference in the average size of banks measured by total assets. Saudi Arabia banks are the largest among GCC countries followed by Bahrain and Kuwait. We also find significant variations between countries regarding capital adequacy and the operation cost indicator. The ratio of equity to assets is much higher in all GCC countries; it ranges from 12.39% in Saudi Arabia to 28.36% in Bahrain.

When comparing average value for cost to income ratio, this mean is comparable in UAE, Saudi Arabia and Qatar, while the Bahrain (51.83%) has the highest value of this ratio.

Finally, concerning the country-level factors (panel C), there are large differences in all macroeconomic variables across GCC countries. In particular, the per capita GDP ($

10,074 in Oman, $34,908 in Qatar), the deposit per square kilometer ($0.04 per km² in Oman, $13.95 per km² in Bahrain), the degree of monetization (33.91% in Oman, 74.62% in Bahrain), and the rate of inflation (0.57% in

Saudi Arabia, 5.18% in Qatar) vary greatly across countries, especially between Bahrain and Oman. Regarding the market structure variables, Bahrain and Oman have higher concentration ratios (87.21 and 80.73%, respectively) compared with Saudi Arabia (50.41%) and UAE (42.52%).

We can also see a variation in intermediation ratio between countries. Average ratio of total loans to total deposits ranges from 58.53% in Bahrain to 111.66% in Oman. The large difference observed across GCC countries in most variables provides argument for the inclusion of country- level factors in cost and profit efficiency functions.

Table 4 provides summary statistics of cost and profit values, products, factor prices, and other bank-characteristics. It reports simple means for the overall sample and for conventional and Islamic banks. We can then observe minor differences for the most average values between both types of banks. In terms of profit efficiency measured by the ratio of profit before tax to total assets, Islamic banks have higher profit value (3.5%) compared with conventional banks (2.8%). We find a large difference between banks if we calculate the ROAA (2.39% for conventional banks and 4.42% for Islamic banks). How- ever, the average cost efficiency measured by the ratio of total costs to total assets is nearly similar for the two cat- egories of banks (4.89 and 4.28%, respectively for Islamic and conventional banks).

Total costs to total assets 3.97 3.74 5.23 5.24 4.02 5.06

Total profit to total assets 2.51 2.45 3.17 3.11 2.49 1.99

Net total loans to total assets 63.00 50.83 40.77 44.84 52.27 69.72

Other earning assets to total assets 13.92 29.51 33.21 33.18 22.11 12.82

Price of labor 1.03 0.94 1.35 0.97 0.92 1.29

Price of fund 2.82 2.42 4.53 4.13 4.26 3.22

Panel B: bank-specific variables

Total assets (US$ millions) 4,246 16,171 9,371 6,850 2,774 2,361

Equity to total assets 19.06 12.39 28.36 20.96 21.97 12.80

ROAA 2.87 2.59 3.61 3.30 2.81 1.88

Cost to income 39.81 39.51 51.83 42.21 37.87 45.91

Panel C: country-specific variables

Per capita GDP (US$) 24,041 11,193 15,009 20,229 34,908 10,074

Degree of monetization 64.59 41.11 74.62 70.68 43.83 33.91

Density of demand (US$/km²) 87.29 70.15 1395.73 215.01 136.82 4.15

Inflation rate 5.05 0.57 1.04 2.26 5.18 1.00

Density of population (hab/km²) 48.73 9.86 1,001.05 137.58 65.89 9.32

Concentration ratio 42.52 50.41 87.21 60.88 78.08 80.73

Intermediation ratio 79.38 73.92 58.53 87.59 81.38 111.66

Average capital ratio 11.98 20.11 8.96 11.65 11.54 11.93

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Table 4 Descriptive statistics of dataset by type of banks (average values)

Variables Full sample Islamic banks Conventional banks

Mean SD Mean SD Mean SD

Panel A: cost and profit value, outputs and input prices

Total costs to total assets 4.45 2.09 4.89 3.09 4.28 1.52

Total profit to total assets 2.68 3.12 3.96 5.13 2.18 1.55

Net total loans to total assets 53.30 18.98 55.36 25.66 52.60 15.72

Other earning assets to total assets 23.80 16.78 25.04 22.28 23.27 14.05

Price of labor 1.08 0.72 1.43 1.11 0.94 0.42

Price of fund

Panel B: bank-specific variables

3.48 3.96 3.75 4.92 3.37 3.52

Total assets (US$ millions) 7,188 11,207 3,198 5,521 8,759 12,418

Equity to total assets 20.01 17.94 31.00 25.45 15.75 11.49

ROAA 2.95 3.82 4.42 6.12 2.39 2.15

Cost to income 42.75 25.91 49.40 35.78 40.17 20.37

All variables are in percentage, except where indicated

Turning to the levels of output, Table 4 shows slight differences in structure of activities between conventional and Islamic banks. Furthermore, these banks focus their activities on loan (53.3%) than on other earning-assets (23.8%). It is interesting to note that there is not much variation in the level of other earning assets between conventional and Islamic banks despite the large difference in the nature of activities in these banks. Conventional banks invest in government securities whereas Islamic banks invest in Islamic bonds.¹¹ Additionally, Islamic banks are more actively engaged in equity investment.¹² Table 4 also shows that the input prices are somewhat higher for Islamic banks, especially for the mean price of funds (1.43%). This means that borrowed funds are more expensive in Islamic banks than in conventional banks.

Regarding the other bank-specific variables, we observe that the average value of total assets varies greatly among the two groups of banks. Conventional banks ($ 8,759 million) are approximately three times bigger than Islamic banks ($ 3,198 million). In terms of capital adequacy (equity to total assets), Islamic banks (31%) are better capitalized than conventional banks (15.75%). Finally, the mean ratio of cost to income is larger in Islamic banks (49.40%) than in conventional banks (40.12%).

11 Issuance of Islamic bonds is a major advancement in the field of Islamic finance. The difference between a conventional bond and Islamic bond (Sukuk) is that the latter is asset-backed and in accordance with Shariah principle. Islamic bonds exist in most GCC countries, especially in Bahrain, Qatar and UAE. Sukuks are also issued and bought outside the Islamic world.

12 There are several ways in which Islamic banks undertake direct investment: a number of Islamic banks in GCC countries (Bahrain, UAE, Qatar) have taken the initiative in establishing and managing subsidiary companies; other banks (Saudi Arabia) have participated in the equity capital of other companies.

These differences in GCC banking between conventional and Islamic banks may have some influence on the cost and profit efficiency levels.

5.2 Estimation of the cost and profit efficiency frontiers Table 5 reports the stochastic translog cost and profit frontier parameter estimates from the maximum-likelihood model. Overall, the estimation results show good fit and the signs of most of the variables conform to the theory.

First, out of the 28 regressors, the profit and cost estimates report,

21 and 19 regressors as statistically significant, respectively. Second, and most importantly, the value of the log- likelihood functions of the profit and cost estimates is high (-530.46 and -1,584.57, respectively) and statistically significant at the 1% level. Third, the sigma-squared is significant at 1% level for both cost and profit functions and indicates highly significant parameter estimates. In addition, the parameter c is also significant for the profit and cost function (0.997, 0991) and clearly means that a large part of the residual consists of bank-specific inefficiency.

Table 5 (panel A) shows a positive significant relationship between the coefficients of the two outputs (loan and other earning assets) and the two dependant variables. This means that higher outputs generate higher total costs and increase profits. Similar findings, especially for the cost function, are reported by several recent studies (e.g., Da- canay III 2007; Lensink et al. 2008; Staikouras et al. 2008).

The price coefficients of the cost function are all positive and significant, as expected, because higher prices of inputs lead to higher costs. The elasticity of the cost of labor (a2 = 1.011) is greater than the elasticity of the cost of fund (a1 = 0.325). This suggests that banks

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should control

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Table 5 Estimation results for

the cost and profit frontier Dependent variables (total costs, total profits before tax)

Cost efficiency Profit efficiency

Parameters Notation Coefficient t-Ratio Coefficient t-Ratio

Panel A: input prices, outputs and multiplicative term

a Significant at 1% level,

b significant at 5% level,

Sigma squared 42.21 33.26^a 25.37 17.1^a

Gamma 0.991 1,350.52^a 0.997 2,637.3^a

Log-likelihood function -15,84.57 -530.46

LR test of the one-sided error 61.12^a 1,942.86^a

c significant at 10% level

more personnel expenses than interest expenses when prices increase. Surprisingly, the price of labor in the profit function is positive (only at the 10% level), although it is expected to be negative like price of fund, since higher prices incur lower profits. The coefficient of the cross- output term (a12) is negative and statistically significant at 1% level. This finding confirms the presence of scope economies in GCC banking. The results also show that the time coefficient is insignificant for the cost function.

However, this coefficient for the profit function is positive

and significant at 1% level, implying that the profit of GCC banks have been increasing with time. This is likely to be the result of economic development in these countries resulting from the rise of oil prices over the last years.

Concerning the country-level variables, Table 5 (panel B) shows that the level of economic development measured by per capita GDP is significant and positively related to costs and profits. This suggests that banks in higher per capita income countries present higher levels of profit and are less cost efficient than banks in low income countries.

a0 Constant -4.153 -18.265^a 8.314 24.2^a

a1 ln (y1) 0.325 3.415^a 0.083 8.523^a

a2 ln (y2) 1.011 22.159^a -0.333 -1.915^c

b1 ln (p1) 0.559 7.313^a 0.849 15.866^a

b2 ln (p2) 0.195 1.951^c 0.124 2.635^b

a₁₁ ln (y1) ln (y1) 0.085 0.722 0.075 2.548^b

a12 ln (y1) ln (y2) -0.055 4.315^a -0.759 -4.958^a

a22 ln (y2) ln (y2) 0.501 16.373^a 0.685 3.578^a

b11 ln (p1) ln (p1) 0.587 16.312^a 0.435 3.056^a

b12 ln (p1) ln (p2) -0.159 -8.634^a 0.006 1.905^c

b22 ln (p2) ln (p2) 0.005 4.859^a 0.073 3.452^a

/11 ln (y1) ln (p1) -0.001 -1.712 0.035 0.395

/12 ln (y1) ln (p2) 0.139 6.601^a 0.025 1.205

/21 ln (y2) ln (p1) 0.017 2.125^b -0.019 -0.022

/₂₂ ln (y2) ln (p2) -0.196 -11.359^a 0.05 0.226

l1 T 0.016 0.654 0.411 8.195^a

l2 T 9T 0.187 1.273 0.072 0.978

k1 T 9 ln (y1) -0.016 0.229 0.372 4.054^a

k2 T 9 ln (y2) -0.419 1.100 0.205 5.662^a

w1 T 9 ln (p1) 0.031 0.143 0.024 2.917^a

w2 T 9 ln (p2) 0.186 1.246 0.303 8.193^a

Panel B: country level variables

q1 CGDP 0.222 5.013^a 0.027 3.332^a

q2 DMON -0.183 -0.986 0.039 1.967^c

q3 DDEM -0.122 -4.912^a 0.025 0.250

q4 INFR 0.126 0.538 0.035 1.44

q5 DPOP -0.116 -4.015^a 0.021 0.886

q6 CONC 0.063 2.594^b 0.017 2.279^b

q7 INTR -0.095 -2.409^b 0.028 3.853^a

q8 ACAP -0.032 –1.926^c 0.113 3.156^a

Panel C: diagnostics