Literature

While there has been extensive literature examining the efficiency features of the contemporary banking sector, particularly the U.S. and European banking markets, the work on Islamic banking is still in its infancy. Typically, studies on Islamic bank efficiency have focused on theoretical issues and the empirical work has relied mainly on the analysis of descriptive statistics rather than rigorous statistical estimation (El-Gamal and Inanoglu, 2004). However, this is gradually changing as a number of recent studies have sought to apply various frontier techniques to estimate the efficiency of Islamic banks.

Hassan and Hussein (2003) examined the efficiency of the Sudanese banking system during the period of 1992 and 2000. They employed a variety of parametric (cost and profit efficiencies) and non-parametric DEA techniques to a panel of 17 Sudanese banks. They found that the average cost and profit efficiencies under the parametric were 55% and 50% respectively, while it was 23% under the non-parametric approach. During the period of study, they found that the Sudanese banking system have exhibited 37% allocative efficiency and 60% technical efficiency, suggesting that the overall cost inefficiency of the Sudanese Islamic banks were mainly due to technical (managerially related) rather than allocative (regulatory).

Yudistira (2004), for example, with a global sample of 18 Islamic banks, found Islamic banks to be more efficient than conventional banks. In contrast, Hassan (2006) in a larger study of 43 Islamic banks found them somewhat less cost efficient than conventional banks. Mokhtar et al., (2006), similarly, in a study of Malaysian Islamic banks found that while Islamic banks had grown faster, their overall efficiency was lower than the conventional banks.

Saleh and Zeitun (2007) analyzed the performance and efficiency of Jordan Islamic Banks for 1998 to 2003 period. The contribution of the paper is the measures show the ability and the efficiency of both Islamic banks to increase their income and reduce expenses. Viverita et al. (2007), of their study of Islamic bank in Asia, Africa and Middle East found the average Middle East bank size was some US $2 billion with Asia Islamic banks averaging US $900 million and African banks just US $151 million. The other finding is the age of each bank was correlated against the various efficiency results. It could be expected that newer banks may have had a chance to implement newer technologies. In this case, technical efficiency results were not correlated with the bank’s age.

Hussein (2003) provides an analysis of the cost efficiency features of Islamic banks in Sudan between 1990 and 2000. Using the stochastic cost frontier approach, he estimates cost efficiency for a sample of 17 banks over the period. The interesting contribution of this paper is that specific definitions of Islamic financial products are used as outputs. In addition, the analysis is also novel as Sudan has a banking system based entirely on Islamic banking principles. The results show large variations in the cost efficiency of Sudanese banks with the foreign owned banks being the most efficient. State owned banks are the most cost inefficient.

Samad (1999) was among the first to investigate the efficiency of the Malaysian Islamic banking sector. In his paper, he investigates the relative performance of the full-fledged Malaysian Islamic bank compared to its conventional bank peers. During the period of 1992 to 1996 he found that the managerial efficiency of the conventional banks was higher than that of the full-fledged Islamic bank. On the other hand, the measures of productive efficiency revealed mixed results. He suggests that the average utilization rate of the Islamic bank is lower than that of the conventional banks. Similarly, he found that profits earned by the full-fledged Islamic bank either through the use of deposit or loan able funds, or used funds are also lower than the conventional banks, reflecting the weaker efficiency position of the full-fledged Islamic bank. In contrast, the productivity test by loan recovery criterion indicate that the efficiency position of the full-fledged Islamic bank seems to be higher and bad debts as a percentage of equity, loans, and deposits also show a clear superiority over the conventional bank peers.

More recently, (Sufian et al. 2008) examined the efficiency of the Malaysian Islamic banking sector during the period 2001-2006 by using the non-parametric Data Envelopment Analysis (DEA) method. The empirical findings suggest that during the period of study, pure technical inefficiency outweighs scale inefficiency in the Islamic banking sector implying that the Islamic banks have been managerially inefficient in exploiting their resources to the fullest extent. The empirical findings seem to suggest that the MENA Islamic banks have exhibited higher technical efficiency compared to their Asian Islamic banks counterparts. During the period of study he fined that pure technical inefficiency has greater influence in determining the total technical inefficiency of the MENA and the Asian Islamic banking sectors.

Methodology

A non-parametric Data Envelopment Analysis (DEA) is employed with variable return to scale assumption to measure input-oriented technical efficiency of MENA Islamic banking sectors. DEA involves constructing a non-parametric production frontier based on the actual input-output observations in the sample relative to which efficiency of each firm in the sample is measured (Coelli, 1996). Let us give a short description of the Data Envelopment Analysis . Assume that there is data on K inputs and M outputs for each N bank. For ith bank these are represented by the vectors xi and yi respectively. Let us call the K x N input matrix – X and the M x N output matrix – Y. To measure the efficiency for each bank we calculate a ratio of all inputs, such as (u’yi/v’xi) where u is an M x 1 vector of output weights and v is a K x 1 vector of input weights. To select optimal weights we specify the following mathematical programming problem:

            min (u’yi /v’xi),
 u,v

u’yi /v’xi ≤1,     j = 1, 2,…, N,
u,v ≥ 0                                                                                                                     (1)

The above formulation has a problem of infinite solutions and therefore we impose the constraint v’xi = 1, which leads to:

min (μ’yi),
  μ,φ

φ’xi = 1
μ’yi – φ’xj ≤0    j = 1, 2,…, N,
μ,φ ≥ 0                                                                                                                    (2)

where we change notation from u and v to μ and φ, respectively, in order to reflect transformations. Using the duality in linear programming, an equivalent envelopment form of this problem can be derived:

min ,
 θ, λ


                                                                                                                     (3)

where  is a scalar representing the value of the efficiency score for the ith decision-making unit which will range between 0 and 1. is a vector of N x 1 constants. The linear programming has to be solved N times, once for each decision-making unit in the sample. In order to calculate efficiency under the assumption of variable returns to scale, the convexity constraint () will be added to ensure that an inefficient firm is only compared against firms of similar size, and therefore provides the basis for measuring economies of scale within the DEA concept. The convexity constraint determines how closely the production frontier envelops the observed input-output combinations and is not imposed in the constant returns to scale case. The variable returns to scale technique therefore forms a convex hull which envelops the data more tightly than the constant returns to scale, and thus provides efficiency scores that are greater than or equal to those obtained from the constant returns to scale model.

            Data Sample, Inputs-Outputs Definition, and the Choice of Variables

It is commonly acknowledged that the choice of variables in efficiency studies significantly affects the results. The problem is compounded by the fact that variable selection is often constrained by the paucity of data on relevant variables. The cost and output measurements in banking are especially difficult because many of the financial services are jointly produced and prices are typically assigned to a bundle of financial services. Two approaches dominate the banking theory literature: the production and intermediation approaches (Sealey and Lindley, 1977).

Under the production approach, pioneered by Benston (1965), the banks are primarily viewed as providers of services to customers. The input set under this approach includes physical variables (e.g. labour, material) or their associated costs, since only physical inputs are needed to perform transactions, process financial documents, or provide counseling and advisory services to customers. The output under this approach represents the services provided to customers and is best measured by the number and type of transactions, documents processed or specialized services provided over a given time period. This approach has primarily been employed in studying the efficiency of bank branches.

Under the intermediation approach, financial institutions are viewed as intermediating funds between savers and investors. In our case, Islamic banks produce intermediation services through the collection of deposits and other liabilities and in turn these funds are invested in productive sectors of the economy, yielding returns uncontaminated by usury (riba’). This approach regard deposits, labour and physical capital as inputs, while loans and investments are treated as output variables.

Following among others, Hassan (2005), and Sufian (2006), a variation of the intermediation approach or asset approach originally developed by Sealey and Lindley (1977) will be adopted in the definition of inputs and outputs used in this study. Furthermore, as at most times bank branches are engaged in the processing of customer documents and bank funding, the production approach might be more suitable for branch efficiency studies (Berger and Humphrey, 1997).

Due to entry and exit factor, the efficiency frontier is constructed by using an unbalanced sample of 44 Islamic banks operating in the World during the period 2003-2009 (see Appendix 1) yielding 167 bank year observations. We are able to collect data on three outputs and three inputs variables. Data for the empirical analysis is sourced from individual bank’s annual balance sheet and income statements and BankScope database by IBCA. The BankScope database converts the data to common international standards to facilitate comparisons and all financial information is reported both in local currency and in US dollar. We use US dollar data which makes the comparison across country consistent. The Islamic banks are modeled as multi-product firms producing three outputs namely, Total Loans (y1), which include loans to customers and other banks, Income (y2), which include income derived from investment of depositors’ funds and other income from Islamic banking operations, and Other Earning Asset (y3), which include investment securities held for trading, investment securities available for sale (AFS), and investment securities held to maturity, by engaging three inputs namely, Total Deposits (x1), which include deposits from customers and other banks, Labor cost (X2) and Total Assets (x3). All variables are measured in millions of US Dollars (US$) and are deflated against the respective countries inflation rates. For 2009, the data is same since only single bank available, this lead to no data for standard deviation.

Results

In this section, we will discuss the technical efficiency change (TE) of the MENA Islamic banking sectors, measured by the DEA method and its decomposition into pure technical efficiency (PTE) and scale efficiency (SE) components. In the event of the existence of scale inefficiency, we will attempt to provide evidence on the nature of the returns to scale of each Islamic bank. The Islamic banks’ efficiency is examined for each year under investigation.

As suggested by Bauer et al. (1998), DeYoung and Hasan (1998), and Isik and Hassan (2002), constructing an annual frontier specific to each year is more flexible and thus more appropriate than estimating a single multiyear frontier for the banks in the sample. Following the earlier studies, for the purpose of the study, we prefer to estimate separate annual efficiency frontier for each year. In other words, there were six separate frontiers constructed for the study. Isik and Hassan (2002) contended that the principal advantage of having panel data is the ability to observe each bank more than once over a period of time. The issue is also critical in a continuously changing business environment because the technology of a bank that is most efficient in one period may not be the most efficient in another. Furthermore, by doing so, we alleviate, at least to an extent, the problems related to the lack of random error in DEA by allowing an efficient bank in one period to be inefficient in another, assuming that the errors owing to luck or data problems are not consistent over time (Isik and Hassan, 2002).

Efficiency of the MENA Islamic Banking Sectors

Table 2 presents the results of the MENA Islamic banks. It is clear that the MENA Islamic banks’ efficiency was on a declining trend from the years 2005 to 2008, increased in year 2004 and 2009. The results seem to suggest that the MENA Islamic banks have exhibited mean technical efficiency of 31.19%, suggesting mean input waste of 68.81%. This implies that the Islamic banks in the MENA countries could have produced the same amount of outputs by only using 31.19% of the amount of inputs it employed. From Table 3 it is also clear that pure technical inefficiency outweighs scale inefficiency in determining the total technical efficiency of the MENA Islamic banks during the period of study.

During the period of study, we find that banks from Saudi Arabia were the most efficient from the MENA region, exhibiting a mean efficiency score of 85.58, followed by banks from Kuwait and Qatar with a mean efficiency score of 84.84% and 78.18% respectively. The result is similar with Sufian et al 2008 finding which study during the period 2001-2006 with smaller sample size. He found that the most efficient start with Iran followed by Bahrain and Qatar. Our finding and this is consistent specifically for Qatar maintaining the top three efficient Islamic banks in MENA region.

Although that Kuwait and Qatar were categorized as high income country, it seem that it does not necessary that higher income country will led to efficiency level. Saudi Arabia that fall under middle income country was the most efficient during the study period. Base on the result, it stated majority of the efficiency bank over the years was dominating from high income country, namely Kuwait and Qatar in MENA banking sectors.

On the other hand, the results seem to suggest that the Gambia banking sector were the least efficient, recording a mean efficiency of 41.3%, followed by Palestine, and Sudan banks with a mean efficiency levels of 45.73% and 48.97% respectively. The result specifically for Sudan consistent with Sufian et al that found the last 3 non efficient were Kuwait, Yemen and Sudan. It is interesting to note that comparing with Sufian et al 2008 finding, Kuwait Islamic bank have climb to become top three efficient from the lowest level.

Composition of the Efficiency Frontier

While the results above highlight the sources of technical inefficiency of the Islamic banks, we next turn to discuss the sources of the scale inefficiency of the Islamic banks. As have been mentioned earlier, a bank can operate at CRS or VRS where CRS signifies that an increase in inputs results in a proportionate increase in outputs and VRS means a rise in inputs results in a disproportionate rise in outputs. Further, a bank operating at VRS can be at increasing returns to scale (IRS) or decreasing returns to scale (DRS). Hence, IRS means that an increase in inputs results in a higher increase in outputs, while DRS indicate that an increase in inputs results in lesser output increases.

To identify the nature of returns to scale, first the CRS scores (obtained with the CCR model) is compared with VRS (using BCC model) scores. For a given bank, if the VRS score equals to its CRS score, the bank is said to be operating at constant returns to scale (CRS). On the other hand, if the scores are not equal, a further step is needed to establish whether the bank is operating at IRS or DRS. To do this, the DEA model is used under the non-increasing returns to scale assumptions (NIRS). If the score under VRS equals the NIRS score, then the bank is said to be operating at DRS. Alternatively, if the score under VRS is different from the NIRS score, than the bank is said to be operating at IRS (Coelli et al., 1998).

During the period of study, there is four MENA Islamic banks seem to have leaded the efficiency frontier, while eighteen MENA Islamic banks have failed to appear at least once on the frontier. It is also clear from the results that four MENA Islamic banks namely, Arcapita Bank B.S.C of Bahrain, Qatar International Islamic Bank, Al Baraka Sudan and Shamil Bank, Bahrain were the MENA leaders by appearing the most times on the efficiency frontier.

In general, the table indicates that while the small banks tend to operate at CRS or IRS, the large banks tend to operate at CRS or DRS, the findings which are similar to the earlier studies by among others McAllister and McManus (1993) and Noulas et al. (1990). To recap, McAllister and McManus (1993) have suggested that while the small banks have generally exhibited IRS, the large banks on the other hand tend to exhibit DRS and at best CRS. As it appears, the small Islamic banks have experienced increasing returns to scale (IRS) in their operations during the period of the study. One implication is that for the small Islamic banks, a proportionate increase in inputs would result in more than a proportional increase in outputs. Hence, the small Islamic banks which have been operating at IRS could achieve significant cost savings and efficiency gains by increasing its scale of operations. In other words, substantial gains can be obtained from altering the scale via internal growth or further consolidation in the sector. In fact, in a perfectly competitive and contestable market, the efficient banks should absorb the scale inefficient banks, in order to exploit cost advantages. Thus, the banks that experience IRS should either eliminate their scale inefficiency or be ready to become a prime target for acquiring banks, which can create value from underperforming banks by streamlining their operations and eliminating their redundancies and inefficiencies (Evanoff and Israelvich, 1991). On the other hand, the results seem to suggest that further increase in size would only result in a smaller increase of outputs for every proportionate increase in inputs of the large banks, resulting from the fact that the large banks have been operating at declining returns to scale (DRS) during the period. Hence, decision-makers ought to be more cautious in promoting mergers among the large banks as a means to enjoying efficiency gains.

Conclusions and directions for future research

In this paper, we examine the performance of the MENA Islamic banks that consist of 15 Middle East and North Africa (MENA) countries namely Bahrain, Egypt, Gambia, Iran, Iraq, Jordan, Kuwait, Mauritania, Palestine, Saudi Arabia, Syria, United Arab Emirates, Qatar, Sudan and Yemen during the period of 2003-2009. The efficiency estimates of individual banks are evaluated using the non-parametric Data Envelopment Analysis (DEA) approach.

The empirical findings suggest that during the period of study, pure technical inefficiency outweighs scale inefficiency in the Islamic banking sector implying that the Islamic banks have been managerially inefficient in exploiting their resources to the fullest extent. The empirical findings seem to suggest that the MENA Islamic banks have exhibited high technical efficiency. During the period of study we find that pure technical inefficiency has greater influence in determining the total technical inefficiency of the MENA Islamic banking sectors. Furthermore, base on the result, it stated majority of the efficiency bank over the years was dominating from high income country, namely Kuwait and Qatar in MENA banking sectors. But it does not necessary that higher income country will lead to efficiency level. Saudi Arabia that fall under middle income country was the most efficient during the study period.

Due to its limitations, the paper could be extended in a variety of ways. Firstly, the scope of this study could be further extended to investigate changes in cost, allocative, and technical efficiencies over time. Secondly, it is suggested that further analysis into the investigation of the Islamic banking sector efficiency to consider risk exposure factors. Finally, future research into the efficiency of the Islamic banking sector efficiency could also consider the production function along with the intermediation function.

Despite these limitations, the findings of this study are expected to contribute significantly to the existing knowledge on the operating performance of the Islamic banking industry in the MENA countries. Nevertheless, the study have also provide further insight to bank specific management as well as the policymakers with regard to attaining optimal utilization of capacities, improvement in managerial expertise, efficient allocation of scarce resources and most productive scale of operation of the banks in the industry. This may also facilitate directions for sustainable competitiveness of Islamic banking operations in the future.

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Note: Detailed results are available from the authors upon request

Note: CRS – (Constant Returns to Scale); DRS – (Decreasing Returns to Scale); IRS – (Increasing Returns to Scale).

The banks corresponds to the shaded regions have not been efficient in any year in the sample period (2001-2006) compared to the other banks in the sample.

‘Count Year’ denotes the number of banks appearing on the efficiency frontier during the year.

‘Count Bank’ denotes the number of times a bank has appeared on the efficiency frontier during the period of study.

Economies are classified by GNI per capita in 2003, calculated using the World Bank Atlas method. The groups are low income, $765 or less; middle income, $766–9,385; and high income, $9,386 or more.