Technical efficiency determinants within a Dual Banking System: a ...

Technical efficiency determinants within a Dual Banking System: a DEA-bootstrap approach Hamdani Hanen, Ali Emrouznejad and Mohamed Nejib Ouertani

International Journal of Applied Decision Sciences, 7 (4): 382 - 404.

Hanen, H., A. Emrouznejad, M. N. Ouertani (2014), Technical efficiency determinants within a dual banking system: a DEA-bootstrap approach, International Journal of Applied Decision Sciences, 7 (4): 382 - 404.

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Technical efficiency determinants within a Dual Banking System: a DEA-bootstrap approach Hamdani Hanen1, Ali Emrouznejad2*, Mohamed Nejib Ouertani1 1 2

Faculty of Economics and Management, Sfax, Tunisia Aston Business School, Aston University Birmingham B4 7ET UK

Abstract: The purpose of this study is to provide a comparative analysis of the efficiency of the Islamic banking sector in Gulf Cooperation Council (GCC) countries. To this end, we employ a semi-parametric two-stage methodology, where we derive technical efficiency scores via a Data Envelopment Analysis in the first stage. Then scores obtained are regressed on a series of determinants of bank efficiency using a double bootstrapping procedure. Our findings indicate that during the eight years of study, conventional banks largely outperform Islamic banks with an average technical efficiency score of 81% compared to 95.57%. However, it’s clear that since 2008 conventional banks efficiency was in a downward trend while the efficiency of their Islamic counterparts were in an upward trend since 2009. This indicates that Islamic banks have succeeded to maintain a level of effectiveness during the dark period of the subprime crisis after certainly, coming under their secondary effects during 2008-2009. An investigation of the determinants of bank’s efficiency show that bank size have a significant positive impact on, only Islamic bank’s efficiency, while z-score is related negatively to efficiency of both departments showing that a higher (lower) distance from insolvency reduces (increases) banks’ efficiency. In other words, a stable and reliable system is crucial to foster the efficiency of GCC banks. Finally, for the whole sample, the analysis demonstrates the strong link of macroeconomic indicators with efficiency for GCC banks. But, surprisingly, there is no significant relationship in the case of Islamic banks. Keywords: Technical efficiency, Islamic banks, DEA-bootstrap model, GCC, Economic freedom.

*

Corresponding author : Ali Emrouznejad, Aston Business School, Aston University Birmingham

B4 7ET UK 1

1. Introduction Recently, several observers have noted the particularity of strong resilience, which was demonstrated by the Islamic financial system, to the financial crisis. Indeed, in their report, Jouini and Pastré (2009) assumed that ‘The Islamic banking solution to the crisis’ is that Islamic finance could be a system that would avoid another subprime crisis. The same idea was taken up by Causse (2010) who admits that the subprime crisis would not happen in the Islamic financial system, In fact, there are two major differences between Islamic and conventional banking systems. First, conventional banking practices are concerned with the elimination of risk whereas Islamic banks bear the risk when involved in any transaction. That is in Islamic banking transactions, the provider of financial capital and the entrepreneur share business risks in return for shares of the profits. Second, when conventional banks are involved in transactions with consumer, they do not only take the liability, but also get the benefit from consumer in form of interest whereas Islamic banks bear all the liability when involved in transaction with consumers (Hanif, 2011). Getting out any benefit without bearing its liability is declared Haram† in Islam. Still, Bader et al (2008) and Masruki et al (2013) argued that the long history and experience of conventional banks along with their huge capital accompanied a developed technologies make their more advantageous compared to the Islamic ones. In this regard, it’s interesting to deal with the current performance of Islamic banks and compare it to the conventional counterparts. What do the different characteristics of Islamic and conventional banks imply for their relative efficiencies? The objective of this empirical work is not only to evaluate, analyze and compare the technical efficiencies of conventional and Islamic banks operating in GCC countries, but also to investigate the impact of bank-specific and macroeconomic indicators on technical efficiency of each compartment and whether their influence is the same for both of them. To achieve these aims, we use DEA-bootstrap procedure of Simar and Wilson (2007) that results in several disadvantages of the traditional Data Envelopment Analysis (DEA) method (Mghyereh and Awartani, 2012). First, DEA is purely deterministic as it does not †

Haram is a term used to describe anything that is illegal or prohibited.

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include stochastic error term in the optimized linear program. Moreover, as we measure efficiency of each bank relative to the best practice observations in the sample, the estimator is biased by construction since any deviation from the frontier is attributed to inefficiency regardless of environmental factors that are beyond the control of the firm. Consequently, we expect that the technical efficiency scores will be overestimated. Finally, the major drawback is that the scores do not allow statistical properties, which we cannot determine intervals or even make standard statistical inference. That method is previously used in few studies concerning diverse sectors. For instance, Kthiri et al (2011) used it for employment office, Lee (2011) for academic sector, De Nicola et al (2013) and Djema, H.; M. Djerdjouri (2012) for hospital and health care efficiency and Assaf et al

(2011) and Maghyereh and Awartani (2012) for conventional banking sector. Indeed, our quest complements those studies and differs from them in many ways. First, we apply the DEA-bootstrap approach to undertake a comparative analysis between Islamic and conventional Gulf banks. Second, we attempt to explore sources of technical inefficiency by regressing the efficiency scores against a set of internal and external determinants. Thus, to our own knowledge, this is the first research that adopts the DEA-bootstrap approach to explain and compare the inefficiencies of conventional and Islamic banks. Finally, we examine and compare the impact of economic freedom‡ on the technical efficiencies of Islamic and conventional banking sectors. The study is structured into five sections. Section 2 presents our theoretical basis. In the section 3 we will present our research methodology followed by the data and definition of variables. Empirical results in section 5. Conclusion and policy implications are discussed in the last section.

2. Literature review The efficiency of the banking sector is a subject that has received a lot of attention in recent years. The empirical literature related to the analysis of this concept has been massively developed over the last years using both parametric and non-parametric approach. Most of these studies focused especially on developed countries. For instance, Delis and Papanikolaou (2009) dealt with European banks, Pasiouras et al (2008) were concerned with Greek banks,

‡

Economic freedom indexes are widely used in the literature (i.e. Gurgul and Lach, 2011; Rode and Coll, 2012) which mainly focus on economic freedom-economic growth relationship. Sufian and Abdul-Majid (2011) used these indexes indicators of the performance of MENA Islamic banking sector.

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Drake et al (2006) concentrated on Hong Kong banks, and Berger et al (2010) investigated Chinese banks. Fewer studies have looked at banks’ efficiency in Islamic countries. The efficiency of Turkish banks was evaluated by El Gamal and Inanoglu (2004) who employed the stochastic frontier approach (SFA). The study compared the cost efficiency of 49 conventional banks with four Islamic special finance houses (SFHs) over the period 1990-2000. Results show that Islamic financial institutions were the most efficient ones. The authors suggest that this could be explained by their emphasis on Islamic asset based financing which led to low nonperforming loan ratios. In addition, employing the non-parametric DEA technique to a sample of five Islamic banks and five conventional banks in Pakistan, Shahid et al, (2010) investigate the comparison between the efficiency of two groups of banks over the period 2005-2009. This study is different from previous studies in that it utilizes DEA to analyze technical efficiency (TE), cost efficiency (CE) and allocative efficiency (AE) of Islamic banks. The empirical findings show that conventional banks perform better than the Islamic banks in term of technical efficiency but there is no difference in terms of cost and allocative efficiency. Furthermore, the t-statistics shows that there is no significant difference in mean efficiencies scores of conventional and Islamic banks, except in 2008. Similarly, the efficiency of Pakistani banks was investigated by Qureshi and Shaikh (2012).They use financial ratio analysis and DEA to compare Islamic banks, conventional banks with Islamic banking division and conventional banks over the period 2003-2008. The empirical results show that Islamic banks are more cost efficient and less revenue efficient than other two counterparts. Further, Islamic banks should be encouraged to reach the efficient frontier in the banking industry by reducing their wastes. Malaysian banking efficiency was examined by Zainal and Ismail (2012).Principally, technical efficiency, pure technical efficiency and scale efficiency of local vs. foreign Islamic banks were analyzed over the period 2006-2010 using the DEA. Results show that the average technical efficiency (TE), pure technical efficiency (PTE) and scale efficiency (SE) were 0.79, 0.90 and 0.88 respectively. Furthermore, local Islamic banks scored higher TE an SE compared to foreign Islamic banks. But, foreign Islamic banks scored the higher PTE. Moreover, a number of studies conducted a cross-country analysis. For example, Bader et al (2008) provided a comparative analysis of the cost, revenue and profit efficiencies of 43 Islamic and 37 conventional banks in 21 countries over the period 1990-2005 using Data Envelopment Analysis. The paper concludes that there are no significant differences between 4

the overall efficiency results of conventional versus Islamic banks. Moreover, global results are favorable with the new banking system. In addition, the results in this paper indicate that on average banks are more efficient in using their resources compared to their ability to generate revenues and profits. The same results are found with Bader et al (2009) which used the same approach to estimate cost, revenue and profit efficiency of 40 conventional and Islamic banks in 11 Organization of Islamic Conference (OIC) countries over the period 1990-2005. This paper also examines the effect of size and age on overall efficiency of the sampled banks. Thus, results suggest that these two factors did not significantly affect the efficiency scores in both banking groups. More recently, Said (2012) explore the efficiency of 47 Islamic banks operating in Middle Eastern and non Middle Eastern countries during the financial crisis period (2006-2009). Using the DEA and report, the author found that the size of bank affects the efficiency during the crisis period. In fact, the efficiency of large Islamic banks increased during 2006-2008 and declined in 2009. Also, the findings showed that Islamic banks’ efficiency in Middle Eastern and non-Middle Eastern countries have increased during the crisis period. There is a large amount of literature that examines the role of different factors in determining banks’ efficiency. For instance, Srairi (2009b) investigates the cost and profit efficiency of GCC banking system by comparing Islamic banks versus their conventional counterparts. He used Stochastic Frontier Approach (SFA) to estimate efficiency of 71 commercial banks over the period 1999-2007. The study also explores the bank-specific variables that may explain the sources of inefficiency. The results indicate that conventional banks are more efficient than Islamic banks in GCC countries in terms of both cost and profit efficiency. In addition, the findings suggest a positive correlation between cost and profit efficiency with bank capitalization and profitability, and a negative correlation with operation cost. The determinants of world Islamic banks efficiency and profitability are evaluated by Ahmad and Mohamad-Noor (2011) over the period 1992-2009. Similarly, the results show that banks’ efficiency is positively related to bank size, bank capitalization and GDP growth. But, it is negatively related to loan intensity. In addition, Sufian et al (2011) examined the determinants of the revenue efficiency of the Malaysian Islamic banks over the period 20062010. The empirical findings show that revenue efficiency is positively and significantly related to market power and liquidity. Ali-Shah et al (2012) compared the efficiency of Pakistani banks during 2001-2008 under loan base approach and income base approach. Using DEA and Tobit regression, the results show that, under the first approach, total mark5

up expenses, total liability and ownership affect technical efficiency. Also, total liability, total profit, and ownership have an impact on efficiency under the second approach. Another strand of the efficiency research has linked the efficiency concept to different bank risks. For instance, Alam (2012a) has evaluated the risk-efficiency relationship of 165 commercial banks and 70 Islamic banks from 11 emerging markets over 2000-2010.The results suggest that banks’ inefficiency and risk are positively related for conventional banks and inversely related for Islamic banks. Alam (2012a) argue that inefficient Islamic banks still maintain lower risk level due to cost constraints weakness which restricts that ability of inefficient Islamic banks to take on more risks. The same author conducted a study on risk and bank efficiency of top 14 Islamic banking countries (Alam, 2012b).Using the Seemingly Unrelated Regression model, empirical findings suggest bank inefficiency is inversely related to risk for Islamic banks. More recently, Said (2013) has explored the correlation between efficiency and risk in the context of MENA region. After three stage analysis, results suggest that credit and operational risks are negatively correlated to efficiency. However, there was no significant correlation with liquidity risk. In General, studies exploring factors affecting Islamic banking efficiency utilize simple regression analysis. However, concerning the present study, DEA-bootstrap method will be applied for both concern of determining bank-efficiency scores along with bank-efficiency factors.

3. Methodology In the current paper, the DEA non-parametric technique, which is developed by Charnes et al. (1978), is adopted to estimate the level of technical efficiency of GCC banking sector (see also Emrouznejad et al, 2008; Emrouznejad and De Writte, 2010). It is worth noting that DEA is a mathematical linear programming approach that helps measure the technical efficiency of different entities: productive and non-productive, public and private, profit and non-profit seeking firms. According to Banker et al. (1984), this technique is advocated to compare the ratio between outputs produced by a decision making unit (DMU) and inputs spent by a DMU for the production purpose: ‫ ܡ܋ܖ܍ܑ܋ܑ܎܎܍ܔ܉܋ܑܖܐ܋܍܂‬ൌ

σ ‫ܜܝܘܜܝܗ܌܍ܜܐ܏ܑ܍ܟ‬ σ ‫ܜܝܘܖܑ܌܍ܜܐ܏ܑ܍ܟ‬

Therefore, a set of variations of this technique has been constructed with reference to the following specificities: 6

i.If variables under control of a decision-maker are inputs (input oriented model) or outputs (output oriented model) ii. If there is a constant or varying returns on scale iii. If there are any limitations regarding weights of inputs and outputs, etc. With reliance on the economic activity of the Gulf-banking sector (maximizing their loans, their other earning assets and their operating income); we are likely to adopt the output oriented model where the linear programming problem is displayed as follows: ࡹࢇ࢞‫׎‬ (1) ௧ ௧ ‫ݏ‬Ǥ ‫ݐ‬Ǥ ෍ ߣ௝ ‫ݔ‬௜௝ ൑  ‫ݔ‬௜௝ ‫݅׊‬ బ ௝ ௧ ௧ ෍ ߣ௝ ‫ݕ‬௥௝ ൒ ‫ݕ׎‬௥௝ ‫ݎ׊‬ బ ௝

෍ ߣ௝ ൌ ͳ ௝

ߣ௝ ൒ Ͳ‫݆׊‬ where‫ݔ‬௜௝ is the amount of inputs i used by the jth bank, ‫ݕ‬௥௝ is the quantity of outputs r produced by the jth bank, ‫ ׎‬൒ Ͳrepresents the efficiency score for jth bank and ߣ௝ ൒ Ͳrepresents the variable weights that can be obtained after solving the linear program described later. The inequalities in the output and input constraint in (1) guarantee the strong disposability of inputs and outputs. On this basis, we maintain that reductions/ increases in all outputs/ inputs are appropriate for a given input/ output mix. Hence, the program should be solved n times, once for each DMU, thus leading to an optimal efficiency score for each bank (for the computing of DEA, see Emrouznejad, 2005). In the second stage of our approach, we attempt to explain the variations in efficiency scores across the Gulf-banking sector. So, we aim at identifying those exogenous factors that may explain part of the variation in efficiency scores, and then we propose an efficiency score that integrates the effects of these factors.

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It should be noted that regressing the efficiency scores (ES) on the exogenous variables is known as the usual approach used in the analysis of the effects of the operating environment on efficiency: ‫ܵܧ‬௜ ൌ ܺ௜ ߚ ൅ ߝ௜ ,

݅ ൌ ͳǡ ǥ ǥ Ǥ ǡ ݊

where ܺ௜ : the vector of exogenous variables associated with bank , ߝ௜ : a normally distributed error term with zero mean and variance ߪఌଶ . It should be noted that the DEA efficiency estimates are biased serially correlated with invalidates conventional inferences from two-stage approaches (Simar and Wilson, 2007). These authors advocated a double bootstrap procedure enabling consistent inference within models that might explain efficiency scores while producing standard errors and confidence intervals simultaneously. The rationale behind the adoption of bootstrapping is to yield a true sampling distribution by mimicking the data generating process. The procedure applied in this study is based on Simar and Wilson’s (2007) assumption. ߜ௜ ൌ ߰ሺܼ௜ ǡ ߚሻ ൅ ߝ௜ ൒ ͳ where:ߝ௜ is a left truncated normal random variable. As Simar and Wilson (2007) proposed, we applied the following steps to the above model in an attempt to approximate the asymptotic distribution and to bias-correct estimates of efficiency scores, within a two-stage DEA. 1. Using the original data, we compute ߜ௜ (DEA first stage) 2. Using the method of maximum of likelihood in truncated regression of ߜ௜ on ܼ௜ using the ݉ ൏ ݊observations where ߜመ ൐ ͳ, we estimate ߚመ of ߚ and ߪො ଶ of ߪ ଶ 3. Looping over the next three steps a to b L times to obtain a set of bootstrap estimates ௅ ‫ ܣ‬ൌ ൛൫ߚመ ‫ ڄ‬ǡ ߪොఌ‫ ڄ‬൯ൟ௕ୀଵ

ଶ

a. For each ݅ ൌ ͳǡήήήήήήήǡ ݉, draw ߝ௜ from the ܰሺͲǡ ߪොఌଶ ሻ distribution with left 6 truncation at (ͳ െ ‫ݖ‬௜ ߚመ ሻ b. Again for each ݅ ൌ ͳǡήήήήήήǡ ݉, computeߜ௜‫ כ‬ൌ ܼ௜ ߚመ ൅ ߝ௜ c. Use the maximum likelihood to estimate truncated regression of ߜ௜‫ כ‬on ܼ௜

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௅ 4. Using the bootstrap values in ‫ ܣ‬ൌ ൛൫ߚመ ‫ ڄ‬ǡ ߪොఌ‫ ڄ‬൯ൟ௕ୀଵ and the original estimates ߚመ and ߪො ଶ

to construct estimated confidence intervals for each element of ߚ and ߪ ଶ as described below is the amount quantity of output produced by the bank represents the efficiency score for represent the variable weights that can be obtained after solving our linear program described later, the vector of exogenous variables associated with bank a normally distributed error term with zero mean and variance.

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Data and definition of variables 4.1. Data This study used a sample of 35 banks from five GCC countries comprising 24

conventional banks and 11 Islamic banks (see table 1). Data was obtained from Bankscope Database. Since all countries have different currencies, all the annual financial values are converted in US dollar using appropriate average exchange rates for each year. [Table 1] 4.2. Inputs and outputs specification Two main approaches are extensively used to select inputs and outputs in efficiency studies (Saely and Lindeley, 1977). In our study, we will use the intermediation approach as it is widely used in the literature examining Islamic banks’ efficiency (Yudistira, 2004; Viverita et al, 2007; Srairi, 2009b; Assaf et al, 2011). Thereby, GCC banks was modelled as multioutputs that product three outputs namely Gross loans (Y1),Operating income (Y2) and other earning assets (Y3) by using three inputs namely Deposits short term funding (X1), Personal expenses (X2) and Total assets (X3). Table (2) reports means and standard deviations of each input and output for the entire sample as well as for conventional and Islamic banks. It is apparent that there is a significant difference in the inputs and outputs of the two types of banks. In fact, the average size of an Islamic bank (at least in terms of deposits and assets) is around half the size of a conventional bank. This indicates that conventional banks are significantly larger than Islamic banks. [Table 2] 4.3.

Determinants of bank efficiency

In order to investigate sources of the differences between Islamic and conventional banks, we propose regression analysis to examine the effect of a number of variables on their 9

efficiencies. In fact, differences between the two sets of banks’ efficiencies may be due to internal and external factors related to the banking sector. The internal factors originated from bank accounts (balance sheet and/or profit and loss accounts), while the external factors are variables that are not related to bank management but reflect the economic and legal environment that affects the operation and performance of financial institutions (Delis and Papanikolaou, 2009). A number of explanatory variables are proposed here for both categories along with the economic freedom measures (see Table 3). 4.3.1. Bank-specific variables The bank specific variables included in the regression are LNTA (log of total assets), ROA (Return in Asset ratio), CTI (cost to income ratio), NLTA (Net loans divided by total assets) and z-score. The LNTA variable is incorporated as a proxy of size to capture for the possible advantages associated with size (economics of scale) (Sufian and Habibullah, 2009; Tan and Floros, 2012). Empirical studies presented mixed results.Ahmad and Mohamad Noor (2011), Sufianand Abdul Majid (2011), Assaf et al (2011) and Zeitun(2012)found that the larger the bank, the more efficient the bank will be. However, El Moussawi and Obeid (2010) report a negative relationship between productive efficiency and size in the context of GCC Islamic banks suggesting that economics of scale have stimulating a positive effect on the productive performance of small banks and a negative impact on large banks’ performance. The ROA is used to provide information about the profitability of such set of banks.

The variable

represents the profit earned per dollar of assets and most importantly, reflects the management ability to utilize the bank’s financial real investment resources to generate profits (Hassan and Bashir, 2003). ROA is expected to be positively related to bank efficiency. For the cost efficiency, the cost to income ratio (CTI) is used. According to Rajhi (2013), this is a measure indicating how well banks manage their total costs (such as overhead expenses) relative to their income, higher values indicate more inefficiency. Thus, the coefficient of the CTI variable is expected to be negatively related to efficiency. This result is already found by Srairi (2009b) and Ariff and Can (2008). A forth variable, net loans to total asset, is included as a measure of liquidity risk. In this vein, Mukherjee et al (2001) argue that loans are the most risky and least liquid of assets. Consequently, Srairi (2009a) reveal that, since loans are the principal source of bank’s income, higher lending could be transformed into higher efficiency. Moreover, Srairi (2009b) used this ratio as a proxy to credit risk or loans intensity

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following Pasiouras (2008) and Havrylchyk (2006) and he argue that credit risk is positively related to profit efficiency and cost inefficiency suggesting that bank with higher loans to assets ratio take more risk and are more profit efficient but less cost efficient because the expenses associated with loans are quite substantial. Originally developed by Boyd and Runkle (1993), we used z-score as a proxy of bank stability. This indicator is a popular measure of bank stability (Beck et al, 2006; Demirguc-Kunt and Detragiache,2009; Laeven and Levine,2009; Cihak et al,2009; Cihak and Hesse,2010; Sufian and Abdul-Majid, 2011)and it was also raised in the study of Berger et al. (2008)as an indication of the bank insolvency risk. We define z-score referring to Yeyati and Micco (2007) as follows: ࢆ࢏࢚ ൌ

ࣆࡾࡻ࡭࢏࢚ା ࡱࡽ࢏࢚ Τ࡭࢏࢚ ࣌ࡾࡻ࡭࢏࢚

where ୧୲ is a proxy variable for the probability of insolvency of the bank i at time t, ୧୲ is the ratio of return on assets of bank i at time t, ୧୲ Τ୧୲ is the amount of equity to assets ratio of bank i at time t, and Ɋୖ୓୅౟౪ is the rate of return on assets of bank i at time t, and ɐୖ୓୅౟౪ is the estimated standard deviation of the rate of return on assets as a proxy of return volatility. According to Cihak et al (2009), Z score measures how many standard deviations a bank is away from exhausting its capital base. Thus, a higher z score implies a lower probability of insolvency. Table (2) also contains some descriptive statistics of these variables. It is clear that the average value of total assets varies among the two types of banks. Conventional banks (15.423 US$ Millions) are approximately twice the Islamic banks (8.346 US$ Millions). In addition, conventional banks are more cost efficient (33.17% compared to 42.99%) more stable (z-score of conventional banks is widely higher Islamic banks) than their Islamic counterparts. For other indicators (profitability, liquidity) there are negligible differences between both types of banks. 4.3.2.

Country-specific/ macro-economic variables In order to separate the effect of internal variables on bank efficiency, we also control

the specific country and the macro-economic variables. We use the inflation rate which represents the changes in the general price level or inflationary conditions in the economy (Khrawish, 2011). In the same vein, Perry (1992) suggests that the effect of inflation on bank performance depends on whether the inflation is anticipated or unanticipated. In the

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anticipated case, the interest rates are adjusted accordingly resulting in fast increase of revenues rather than costs, which subsequently has positive impact on bank profitability (i.e. Delis and Papanikolaou,2009; Sufian and Habibullah,2009; El-Mossawi and Obeid,2010; Sufian and Abdul-Majid,2011;Alper and Anbar, 2011).On the other hand, in the unanticipated case, banks may adjust their interest rate resulting in a faster increase of banks’ costs rather than banks’ revenues, which consequently has negative effects on bank profitability (i.e. Zeitun; 2012).Banking efficiency may be also affected by the capacity of government to effectively formulate and implement sound policies, the respect of citizens and the state for the institutions that govern economic and social interactions among them§. In this context, we control for the effect of the institutional environment by using the governance indicator compiled by Kaufman et al (2010)**. We also examine the impact of the development of the financial sector using two indicators: banking sector development and banking concentration. Tan and Floros (2012) noticed that greater financial development can help to improve the efficiency of the banking sector. This result was based on the study of Demirguc-Kunt and Huizinga (2000) that supported those banks in countries with more competitive banking sectors, where bank assets constitute a large portion of GDP, generally have smaller margins and are less profitable. They also argued that countries with underdeveloped financial system tend to be less efficient and adopt less than competitive pricing behaviour. In our study, financial development is proxied by the ratio of total assets over GDP per capita. Finally, the impact of the banking sector concentration on bank efficiency is captured by the Herfindhahl-Hirschman index. Tan and Floros (2012) argued that the more concentrated the industry is, the greater the monopolistic power of the firm will be and this, in turn, improves profit margin. Thus, industry concentration is positively related to bank performance. This result is also supported by Srairi (2009) and Staikouras et al (2008).However, Ben Naceur and Goaied (2008) reported a negative coefficient between concentration and bank profitability in Tunisia. Also, Garcia-Herrero et al. (2009) and Tan and Floros (2012) found a negative relationship between concentration and efficiency in the context of Chinese banking industry.

§

http://info.worldbank.org/governance/wgi/index.asp The Worldwide Governance Indicators (WGI) project reports six governance indicators: Voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and control of corruption. **

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4.3.3. Economic freedom According to Rode and Coll (2012) economic freedom is supposed to measure to what degree an economy is driven by market principles. We employ economic freedom indexes used by Sufian and Abdul-Majid (2011) in the context of MENA Islamic banking sector. Theses authors defined the economic freedom as a conceptual measure of the private ownership and market allocation of resources. In the regression model, OVER_FEE is used to capture the effects of overall economic freedom on the Islamic and conventional bank’s efficiency. Then, BUSI_FREE, MON_FREE, FIN_FREE and CORR_FREE are introduced as proxies of business freedom, monetary freedom, financial freedom and freedom from corruption, respectively. All these indices are defined in table (3) according to Gurgul and Lach (2011), they have 0 to 100 scale where a score of 100 signifies an economic environment or set of policies that is most conductive to economic freedom (Sufian and Abdul-Majid, 2011).We expect that these measures will improve our understanding on the relationship between economic freedom and economic efficiency. On the other hand, we wonder whether countries that exhibit greater degrees of economic freedom perform better than those that do not. [Table 3] [Table 4] 5.

Empirical results/ findings In this section, we will estimate technical efficiency for 35 commercial banks operating

in GCC countries using output oriented DEA method. Then, we divide our sample into two groups, conventional banks and Islamic banks, to analyze the relationship between technical efficiency and type of banks. Finally, we attempt to identify internal and external factors that explain differences in technical efficiency scores between conventional and Islamic banks. 5.1. Estimation of the technical efficiency of GCC banks Figure (1)below indicates that, during the study period, the GCC banks’ technical efficiency scores are roughly floating between 92% and 95% with an average value of 93.85%. Indeed, exhibiting this score, these banks could have expanded 6.15% of the outputs without altering the used inputs amounts. An investigation of each individual year indicates that the two first study years exhibited the less efficiency scores. This is particularly caused by the turbulent economic 13

environment throughout the pre-2003 period. Economic conditions in the 1990s which was characterized by declining oil income accompanied by a decline in production (60%) in 1981(Kostiner, 2010). Since 2005, banks benefited from the enhancement of economic environment: the increase in oil prices, the expansion of oil production, the expansionary fiscal policies and the low interest rates. So they improve their efficiency levels during 20052006. However, the GCC banks’ TE scores witnessed a drastic decreasing in 2007 caused by the subprime crisis. In fact, during the global financial crisis on 2007, most banks’ TE declined because of the occurrence and the spillover of such crisis. Finally, an improvement in the TE scores is marked in 2008 followed by a declining trend over 2009-2010. [Figure 1] At this level, we turn to examine the differences characterizing both bank sets. As depicted in figure 2 above, technical efficiency has been discovered to be significantly higher, on average, for the conventional banks as compared to the Islamic banks. Indeed, conventional banks outperform all the time their Islamic counterparts except the last study years. In fact, conventional banks’ scores were on a steady trend during the first four study-years (2003-2006) being nearly 97% efficient. This implies that the same amounts of inputs employed still have generated 3% more than the produced outputs. In 2007, conventional banks’ TE fell from 97% to 94.6%, with a slight rise to 95.3% in 2008 and then worse back to 94.4% and 92.7% during 2009 and 2010, respectively. As regard the Islamic banks’ scores, they are floating between nearly 78% and 84% during the study period. They have been on an upward trend up to 2006 before an important decrease in 2007. During 20072010, Islamic banks’ TE shows a relative stability around 82%.This level of efficiency indicates18% input waste. In other words, Islamic banks would have expanded 18% of outputs employing the same inputs used. During the global financial crisis on 2008, most banks’ TE declined and Both Islamic and Conventional banks were subject to the effects of that crisis. Nevertheless, Islamic banks’ TE did not fall as deeply as the conventional banks did because the TE of the former was bounced back during 2009-2010, yet, the TE of the latter has kept on in their declining trend after an improvement during 2008 and 2009. [Figure 2]

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Our findings pertinent to the technical efficiency concerning the GCC sited Islamic banks appear to be different at mixed temporal intervals in comparison with the results achieved by similar conducted pertaining to different parts of the world. For instance, our Olson and Zoubi (2008) have revealed that the Islamic banks based in the GCC countries have turned out to be less efficient than the conventional ones. They have also pointed out that the Islamic banks’ inefficiency might well be due to the fact that their customers are predisposed to acquire the Islamic products regardless of cost. A similar result has been discovered by Rosly and Abu-Bakar (2003) as well as Yudistira (2004). Conversely, however, many studies found have found that no significant difference has been proven to prevail with regard the overall-efficiency results of the conventional and Islamic banks (Samad, 2004, Kader et al, 2007, Bader et al., 2008-2009, Johnes et al, 2009). 5.2.

Determinants of GCC banks’ technical efficiency

In the second stage of this analysis, we regress the efficiency scores on a number of variables including bank-characteristics, macroeconomic indicators and economic freedom measures following the methodology described previously. Results are reported in tables 6-7 and 8 (see appendix). It is worth noting that the regression models are estimated by including the each economic freedom indicator at a time rather estimating all economic freedom variables concurrently. Regarding bank-specific variables, the results indicate that the coefficient of size has a positive and statically significant effect on technical efficiency for all banks models. This finding which is supported by several studies (Ahmad andMohamed-Noor,2011;Sufian andAbdulmajid,2011; Assaf et al,2011 and Zeitun,2012) implies that efficiency improvement is higher for large banks than for smaller ones. The proxy of profitability (ROAA) reveals a significant and positive relation with the conventional banks’ and the full sample technical efficiencies. This finding suggests that the more profitable the banks, the more efficient it will be. In other worlds, GCC banking sector will have better technical efficiency levels if they develop and improve their management resources. The cost to income ratio is negatively related to efficiency of the Islamic banking model. As expected, the indicator of loan quality is associated negatively with technical efficiency in the conventional and the full sample models. Nevertheless, we found a positive (but not significant) relationship in the case of Islamic banks. This result is consistent with Srairi (2009) who explained this positive relationship by the nature of Islamic products which 15

present less bad loans and therefore need less reserve for these loans compared to their conventional peers. This finding contradicts Ahmad and Mohamad-Noor’s (2011) when exploring world Islamic banks efficiency suggesting that banks with higher loans to assets ratio exhibit lower efficiency. Furthermore, the results highlight that the impact of z-score is negative and significant except for the conventional banking model. That indicates that an important efficiency level requires a minimum risk taking. This result is inconsistent with Sufian and Abdul Majid’s (2011). It is worth noting that the economic conditions determinants along with the economic freedom indexes appear to be non-significant neither in the Islamic nor on the full sample models. However, in the conventional banking model, concentration exhibits a negative and significant sign which indicate that it is inversely related to the technical efficiency. Still, among the economic freedom indexes, only the corruption freedom index was significant at 1% level with a positive sign, unexpectedly. 6.

Conclusion This paper has sought to look at the factors that can influence technical efficiency of

GCC banking sector by comparing conventional and Islamic banks. Our sample contained 11 Islamic banks and 24 conventional banks from five Gulf countries. We employed the two stage DEA analysis; we have computed technical efficiency scores (TE) for all banks, conventional banks and Islamic banks; Then, we linked the scores obtained by a number of explanatory variables in order to examine the factors that may influence the efficiency of both models. Our reached finding highlights that during the study period, GCC banks were nearly 94% efficient, implying 6% input waste. Regarding each single model, we found that conventional banks model exhibited higher TE scores over the study period compared to the Islamic model. It is evident from the regression model that size reported a positive and significant effect on all banks models’ efficiency. We also found that return on assets ratio as a proxy of bank profitability is positively related to technical efficiency with a high level of significance in the conventional banking model. Moreover, net loans to total asset ratio remain negatively and significantly correlated to conventional bank efficiency, while there is no significant relationship with the Islamic banking model. 16

Despite this privilege recorded for Islamic banks, it is also worth noting that the Islamic framework turns to be under the obligation to improve some operational areas if it is to compete more efficiently with the conventional system. In other words, Islamic finance, having the same purpose as conventional finance, needs to evolve some of its structures to adapt more adequately market needs and is under the requirement to tolerate some kind of convergence with the traditional financial practices.

17

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23

Appendices Table 1: Banks in sample by country and type Country

Islamic banks

Conventional banks

Total

UAE

4

8

12

Kuwait

1

3

4

Saudi-Arabia

1

5

6

Bahreïn

3

4

7

Qatar

2

4

6

Total

11

24

35

Table 2: Summary Statistics of the variables applied to the DEA model (USD’000) Islamic banking model Obs

Mean

Std. dev

Min

Max

Gross Loans

88

6722419

7966222

85700

32981561

Operating Income

88

558584

729290.2

17683.59

3109684

Other Earning Assets

88

2837281

3182328

9800

13602887

Deposits and Short term funding

88

8176905

9271719

144600

39594236

Personal expenses

88

105256.5

116946.2

6877.658

462560.6

Total Assets

88

10533698

11968409

257500

492560.6

Conventional banking model Gross Loans

192

9895980

8891262

7800

38256626

Operating Income

192

630389.3

511484.4

28516.46

2031069

Other Earning Assets

192

5596358

4895885

289748

24613444

Deposits and Short term funding

192

13210380

11280485

550387.9

49399676

Personal expenses

192

117648.8

92472.5

6589.515

406017.6

Total Assets

192

16652219

14131740

699065.3

61369829

Full Sample Gross Loans

280

8898575

8722779

7800

38256626

Operating Income

280

607821.9

588272.6

17683.59

3109684

Other Earning Assets

280

4729219

4605840

9800

24613444

Deposits and Short term funding

280

11628431

10926998

144600

49399676

Personal expenses

280

113754.1

100756.7

6589.515

462560.6

Total Assets

280

14729256

13765184

257500

61369829

24

Table 3: Variables description Variable Bank size Profitability (ROAA) Cost efficiency Liquidity risk Z-SCORE

Inflation Concentration Financial development

Overall freedom index (OVER_FREE)

Business freedom index (BUSI_FREE) Monetary freedom index (MONE_FREE) Financial freedom index (FIN_FREE) Freedom from corruption (CORR_FREE)

Description

Source

Bank-specific variables Natural logarithm of total assets of the bank Return on average total assets of the bank Cost to income Measure of liquidity, calculated as net loans/ total assets. The ratio includes what percentage of the assets of the bank is tied up in loans. Measures the number of standard deviations a return realization has to fall in order to deplete equity, under the assumption of normality of banks’ returns. Country-specific variables The inflation rate Herfindahl-Hirschmann index Bank assets/GDP Economic freedom The summary index of economic freedom based on average of 10 economic measurements: business freedom, trade freedom, fiscal freedom, government size, monetary freedom, investment freedom, financial freedom, property rights, labour freedom and freedom from corruption. The business freedom index reflects the individual’s right to establish and run an enterprise without interference from the state. The most common barriers to the free conduct of entrepreneurial activity are redundant and burdensome regulations. Every economy needs a steady and reliable currency as a medium of exchange and store of value. Without monetary freedom, it is extremely difficult to create long-term value. A measure of banking efficiency as well as a measure of independence from government control and inference in the financial sector. Corruption can simply infect all parts of an economy. Political corruption manifests itself most commonly in the form of graft, bribery, nepotism or embezzlement. Openness in regulatory processes and procedures can promote equitable treatment and improve regulatory efficiency.

25

BankScope BankScope BankScope BankScope Authors calculations based on BankScope database. The world bank UNCTAD Authors calculation Heritage Foundation

Heritage Foundation

Heritage Foundation

Heritage Foundation

Heritage Foundation

Table 4: Average values of country-specific variables INFR GDP_GROWTH

UAE 8.09 4.93

KUWAIT 4.18 6.36

0.45

Concentration OVER_FREE BUS_FREE MONE_FREE FIN_FREE

3.53 4.08

BAHRAIN 2.51 6.19

0.67

0.73

0.39

0.54

65.65

66.15

62.67

73.58

64.77

60.11

69.19

68.9

81.55

61.8

75.45

77.79

81.21

80.65

76.2

40

50

40

83.75

50

52.5

39.37

59.75

61.5

64.12 CORR_FREE Note: All variables are in percentage

SA

QATAR 6.4 14.35

Table 5: Efficiency score results Full Sample UAE KUWAIT SA BAHRAIN QATAR Mean

2003

2004

2005

2006

2007

2008

2009

2010

0.92

0.915

0.932

0.933

0.91

0.942

0.939

0.933

0.963

0.951

0.961

0.944

0.92

0.949

0.953

0.924

0.947

0.947

0.974

0.975

0.939

0.948

0.961

0.961

0.928

0.919

0.928

0.959

0.942

0.94

0.907

0.952

0.914

0.909

0.936

0.952

0.951

0.966

0.962

0.929

0.930

0.924

0.943

0.950

0.929

0.948

0.943

0.939

Conventional banking model UAE KUWAIT SA BAHRAIN QATAR

0.964

0.944

0.971

0.975

0.949

0.977

0.971

0.96

0.983

0.967

0.963

0.953

0.915

0.978

0.987

0.931

0.956

0.952

0.975

0.976

0.936

0.948

0.958

0.957

0.982

0.984

0.98

0.984

0.978

0.966

0.973

0.981

0.983

0.97

0.974

0.958

0.957

0.977

0.984

0.935

Mean

0.971

0.959

0.973

0.971

0.948

0.969

0.973

0.955

Islamic banking model UAE KUWAIT SA BAHRAIN QATAR

0.943

0.949

0.917

0.929

0.908

0.929

0.933

0.941

1

0.992

1

0.982

1

0.988

0.989

1

0.988

0.998

1

1

0.981

0.995

1

1

0.907

0.887

0.908

0.964

0.924

0.934

0.931

0.935

0.883

0.891

0.963

1

0.968

1

0.988

1

Mean

0.932

0.930

0.938

0.963

0.939

0.955

0.954

0.961

26

Table 6a: DEA-bootstrap method regression analysis

Islamic banking model (1)

(2)

CONSTANT

0.821*** (7.00)

0.81*** (5.38)

Size

0.013* (1.85) 0.001 (0.47) -0.001*** (-2.73) 0.0003 (0.45) -0.002** (-2.31)

0.014 (1.44) 0.001 (0.43) -0.001** (-2.27) 0.0003 (0.45) -0.002** (-2.18)

ROA CTI NL/TA Z-score

INFL GDPG CONC

OVER-FREE BUSI-FREE

-0.001 (-0.88) 0.001 (0.46) -0.007 (-0.09)

(3)

(4)

0.604** 0.734*** (2.42) (5.02) Bank characteristics 0.015 0.014 (1.63) (1.57) 0.001 0.001 (0.34) (0.28) -0.002** -0.001** (-2.37) (-2.31) 0.0004 0.0005 (0.71) (0.78) -0.002 -0.002** (-2.19) (-2.27) Economic conditions -0.0001 0.0001 (-0.13) (0.12) 0.0004 0.0003 (0.32) (0.27) 0.018 -0.006 (0.26) (-0.08) Economic freedom 0.002 (0.96) 0.001 (1.12)

(5)

(6)

(7)

0.927*** (5.4)

0.811*** (5.33)

0.859*** (4.78)

0.009 (0.97) 0.001 (0.47) -0.001* (-1.83) 0.0002 (0.41) -0.002* (-1.92)

0.013 (1.49) 0.001 (0.37) -0.001* (-1.94) 0.0003 (0.38) --0.002** (-2.00)

0.012 (1.32) 0.001 (0.39) -0.001** (-2.17) 0.0004 (0.57) -0.002** (-2.12)

-0.001 (-0.9) 0.0006 (0.48) -0.007 (-0.11)

_0.001 (-0.94) 0.001 (0.68) -0.023 (-0.27)

-0.001 (-1.25) 0.001 (0.53) 0.006 (0.39)

-0.001 (-1.07)

MON-FREE

-0.000 (-0.03)

FIN-FREE

-0.0004 (-0.51)

CORR-FREE

Wald x2 25.12*** 30.81*** 48.71*** 30.17*** 30.17*** 28.09*** 29.52*** R-squared 0.291 0.298 0.308 0.305 0.305 0.298 0.301 Adj R-squared 0.249 0.226 0.228 0.225 0.225 0.217 0.220 Root MSE 0.060 0.061 0.061 0.061 0.061 0.062 0.061 No. of Observ 88 88 88 88 88 88 88 Note: t-statistics are between parentheses, *,** and *** indicate statistical significance at 1%, 5% and 10%

27

Table 6b: DEA-bootstrap method regression analysis

Conventional banking model (1)

(2)

CONSTANT

0.848*** (15.10)

0.854*** (14.22)

Size

0.007** (2.45) 0.004** (2.06) 0.0005 (1.63) -0.000*** (-2.89) -0.000 (-0.73)

ROA CTI NL/TA Z-score

INFL GDPG CONC

OVER-FREE BUSI-FREE

(3)

(4)

0.842*** 0.881*** (7.77) (14.2) Bank characteristics 0.009*** 0.009*** 0.009*** (2.71) (2.68) (2.79) 0.003* 0.004* 0.003* (1.88) (1.94) (1.77) 0.0004 0.0004 -0.000 (0.153) (1.32) (1.61) -0.000*** -0.0005** -0.000*** (-2.91) (-2.49) (-3.32) -0.0001 -0.002 -0.0001 (-1.25) (-1.18) (-0.84) Economic conditions -0.0001 -0.0002 -0.000 (-0.18) (0.22) (-0.22) 0.000 0.000 0.0001 (0.08) (0.08) (0.19) -0.044* -0.042 -0.045** (-1.92) (-1.39) (-2.09) Economic freedom 0.0001 (0.14) -0.0004 (-1.41)

(5)

(6)

(7)

0.789*** (11.08)

0.848*** (13.51)

0.763*** (9.36)

0.010*** (3.15) 0.003* (1.75) 0.004 (1.20) -0.0004** (-2.46) -0.0002 (-1.60)

0.008*** (2.65) 0.004* (1.92) 0.0004 (1.29) -0.000** (-1.97) --0.0002 (-1.24)

0.011*** (3.13) 0.004** (0.13) 0.0004 (1.51) -0.000*** (-3.03) -0.0001 (-0.67)

-0.003 (0.39) -0.000 (-0.06) -0.058** (-2.49)

-0.002 (0.21) 0.000 (0.09) -0.040 (-1.60)

-0.0004 (0.50) -0.0005 -(0.68) -0.007 (-0.24)

-0.0006 (1.27)

MON-FREE

-0.000 (-0.43)

FIN-FREE

0.0007* (1.80)

CORR-FREE Wald x2 R-squared Adj R-squared Root MSE No. of Observ

26.45*** 0.079 0.079 0.038 192

44.46*** 0.100 0.061 0.038 192

49.6*** 0.100 0.056 0.038 192

44.27*** 0.109 0.065 0.038 192

43.90*** 0.111 0.067 0.038 192

49.91*** 0.101 0.056 0.038 192

43.97*** 0.117 0.073 0.038 192

Note: t-statistics are between parentheses, *,** and *** indicate statistical significance at 1%, 5% and 10%

28

Table 6c: DEA-bootstrap method regression analysis

Full Sample CONSTANT

Size ROA CTI NL/TA

Z-score

(1)

(2)

0.627*** (10.57)

0.615*** (9.3)

0.0021*** (5.93) 0.006*** (6.00) -0.0002 (0.73) 0.0006**(2.38) -0.0004** (-1.98)

(5)

(6)

(7)

0.502*** 0.600*** (4.11) (8.65) Bank characteristics 0.022*** 0.023*** 0.023*** (5.51) (5.71) (5.60) 0.006*** 0.006*** 0.006*** (6.03) (5.95) (5.43) -0.0003 -0.0004 -0.0003 (-0.86) (-1.20) (-1.03) -0.0005** -0.0004 -0.0005 (-2.11) (-1.62) (-1.76)

0.568*** (7.34)

0.601*** (8.61)

0.601*** (7.53)

0.024*** (5.76) 0.006*** (6.01) -0.0003 (-0.96) -0.0005* (-1.88)

0.022*** (5.54) 0.006*** (5.53) -0.0003 (-1.01) -0.0004 (-1.40)

0.023*** (5.71) 0.006*** (5.36) -0.0002 (-0.80) 0.0005** (-2.22)

-0.0005** (-2.11)

-0.0005** (-2.39)

-0.0005** (-2.10)

-0.0004** (-2.08)

-0.0005 (-075) 0.0006 (0.88) -0.031 (-1.15)

-0.0006 (-0.78) 0.0006 (0.99) -0.010 (-0.38)

-0.0007 (-0.92) 0.0005 (0.73) -0.014 (-0.42)

-0.0007 (-0.98) 0.0006 (0.99) -0.02 (-0.81)

INFL GDPG CONC

OVER-FREE BUSI-FREE

(3)

(4)

-0.0005** -0.005** (-2.25) (-2.15) Economic conditions -0.0003 -0.0004 (-0.39) (-0.57) 0.0006 0.0006 (0.92) (0.89) -5.11e-07 -0.019 (-0.00) (-0.79) Economic freedom 0.001 (1.10) 0.0002 (0.61)

0.0004 (1.01)

MON-FREE

0.0002 (0.76)

FIN-FREE

0.0001 (0.28)

CORR-FREE Wald x2 R-squared Adj R-squared Root MSE No. of Observ

71.36*** 0.252 0.239 0.053 280

74.75*** 0.258 0.236 0.053 280

78.21*** 0.262 0.238 0.053 280

73.75*** 0.259 0.234 0.053 280

78.65*** 0.260 0.236 0.053 280

71.97*** 0.260 0.235 0.053 280

65.9*** 0.258 0.234 0.053 280

Note: t-statistics are between parentheses, *,** and *** indicate statistical significance at 1%, 5% and 10%

29

Figure 1: Evolution of the technical efficiency of GCC banks 0.96 0.95 0.94 0.93 0.92 0.91 2003

2004

2005

2006

2007

2008

2009

2010

ALL BANKS

Figure2: Islamic vs. conventional banks technical efficiency 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 0.9 2003

2004

2005

2006

2007

IB

CB

30

2008

2009

2010