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The Determinants of World Islamic Banks’ Efficiency: Does Country Income Level have an Impact? Mohamad Akbar Noor Mohamad Noor Nor Hayati Bt Ahmad Abstract The paper investigates the efficiency of 78 Islamic banks sectors in 25 countries during the period of 1997-2009. The efficiency estimates of individual banks are evaluated using the non-parametric Data Envelopment Analysis (DEA) approach. The empirical findings seem to suggest that the World Islamic banks have exhibited high pure technical efficiency. The studies find positive relationship between bank efficiency and loans intensity, size, capitalization, and profitability. A multivariate analysis based on the Tobit model reinforces these findings. The finding for LOW income country is negative and statistically significant at 1% level. While for MEDIUM positive and not significant while lastly HIGH were negative and not significant. This indicated that Islamic banks at LOW and HIGH income country were inefficient compare to MEDIUM income country. JEL Classification: G21; G28 Keywords: Islamic Banks, Banks Efficiency, Data Envelopment Analysis (DEA), Multivariate analysis. 1.0 INTRODUCTION Islamic banks today exist in all parts of the world, and are looked upon as a viable alternative system, which has many things to offer. While it was initially developed to fulfill the needs of Muslims, Islamic banking has now gained universal acceptance. Islamic banking is recognized as one of the fastest growing sector in economy. Since the inception of the first Islamic bank in Egypt in 1963, Islamic banking has rapidly grown all over the world. The number of Islamic financial institutions worldwide has risen to over 300 today in more than 75 countries with Bahrain and Malaysia the Corresponding author: College of Business, Universiti Utara Malaysia. Mailing address: SAPURA Technology Berhad, Level 9, Sapura@Mines, No. 7 Jalan Tasik, The Mines Resort City, 43300 Seri Kembangan, Selangor, Malaysia. e-mail: [email protected] Tel: 603- 8949 7789, Fax: 603-8949 8007. Professor of Banking and Risk Management, College of Business, Universiti Utara Malaysia. Mailing address: Professor Office, College of Business, Universiti Utara Malaysia, 06010 Sintok, Kedah, e-mail: [email protected], Tel: 604- 9286404 ext 6403, Fax: 604-9285762.

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The Determinants of World Islamic Banks’ Efficiency:Does Country Income Level have an Impact?

Mohamad Akbar Noor Mohamad Noor

Nor Hayati Bt Ahmad

Abstract

The paper investigates the efficiency of 78 Islamic banks sectors in 25 countries during theperiod of 1997-2009. The efficiency estimates of individual banks are evaluated using thenon-parametric Data Envelopment Analysis (DEA) approach. The empirical findings seem tosuggest that the World Islamic banks have exhibited high pure technical efficiency. Thestudies find positive relationship between bank efficiency and loans intensity, size,capitalization, and profitability. A multivariate analysis based on the Tobit model reinforcesthese findings. The finding for LOW income country is negative and statistically significant at1% level. While for MEDIUM positive and not significant while lastly HIGH were negativeand not significant. This indicated that Islamic banks at LOW and HIGH income countrywere inefficient compare to MEDIUM income country.

JEL Classification: G21; G28

Keywords: Islamic Banks, Banks Efficiency, Data Envelopment Analysis (DEA),Multivariate analysis.

1.0 INTRODUCTION

Islamic banks today exist in all parts of the world, and are looked upon as a viablealternative system, which has many things to offer. While it was initially developedto fulfill the needs of Muslims, Islamic banking has now gained universal acceptance.Islamic banking is recognized as one of the fastest growing sector in economy. Sincethe inception of the first Islamic bank in Egypt in 1963, Islamic banking has rapidlygrown all over the world. The number of Islamic financial institutions worldwide hasrisen to over 300 today in more than 75 countries with Bahrain and Malaysia the

Corresponding author: College of Business, Universiti Utara Malaysia. Mailing address:SAPURA Technology Berhad, Level 9, Sapura@Mines, No. 7 Jalan Tasik, The MinesResort City, 43300 Seri Kembangan, Selangor, Malaysia. e-mail:[email protected] Tel: 603- 8949 7789, Fax: 603-8949 8007. Professor of Banking and Risk Management, College of Business, Universiti Utara

Malaysia. Mailing address: Professor Office, College of Business, Universiti UtaraMalaysia, 06010 Sintok, Kedah, e-mail: [email protected], Tel: 604- 9286404 ext 6403,Fax: 604-9285762.

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201210

biggest hubs, but are also appearing in Europe and the United States. The Islamicbanking total assets worldwide are estimated to have exceed $250 billion and aregrowing at an estimated pace of 15 percent a year (Qorchi, 2005). Zaher and Hassan(2001) suggested that Islamic banks are set to control some 40-50 percent of Muslimsavings by 2009/10.

The Islamic resurgence in the late 1960's and 1970's, further intensified by the 1975oil price boom, which introduced a huge amount of capital inflows to Islamiccountries has initiated the call for a financial system that allows Muslim to transact ina system that is in line with their religious beliefs. Before the re-emergence of theIslamic financial system, Muslims throughout the world has only conventionalfinancial system to fulfill their financial needs. (Sufian et al. 2008)

Islamic financial products are aimed at investors who want to comply with theIslamic laws (sharia’h) that govern a Muslim's daily life. Sharia’h law forbids thegiving or receiving of riba’1 (because earning profit from an exchange of money formoney is considered immoral); mandate that all financial transactions be based onreal economic activity; and prohibit investment in sectors such as tobacco, alcohol,gambling, and armaments. Despite that, Islamic financial institutions are providing anincreasingly broad range of financial services, such as fund mobilization, assetallocation, payment and exchange settlement services, and risk transformation andmitigation. Despite the growing interest and the rapid growth of the Islamic bankingand finance industry, analysis of Islamic banking at a cross-country level is still at itsinfancy. This could partly be due to the unavailability of data, as most of the Islamicfinancial institutions particularly in the Asian region are not publicly traded.

The aim of this paper is to fill a demanding gap in the literature by providing thelatest empirical evidence on the performance of Islamic banks in the World duringthe period 1997 to 2009. The efficiency estimate of each Islamic bank is computed byusing the non-parametric Data Envelopment Analysis (DEA) method. The methodallows us to distinguish between three different types of efficiency measures, namelytechnical, pure technical, and scale. Unlike the previous analysis of Islamic bankefficiency, we have constructed and analyzed the results derived from dynamicpanels, which is critical in a dynamic business environment as a bank may be themost efficient in one year but may not be in the following year (s). A dynamic panel

1 Riba’ the English translation of which is usury is prohibited in Islam and is acknowledgedby all Muslims. The prohibition of riba’ is clearly mentioned in the Quran, the Islam's holybook and the traditions of Prophet Muhammad (sunnah). The Quran states: "Believers! Donot consume riba’, doubling and redoubling…" (3.130); "God has made buying and sellinglawful and riba’ unlawful… (2:274).

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 11

analysis will also highlight any significant changes taking place in the Islamicbanking sector during the period of study. The paper also explores the proximatesources of (in) efficiency under the multivariate framework and relates the findings tobank specific characteristics.

Since the countries of coverage are span across 25 countries, we also will study theefficiency result base on the Islamic bank country of origin. The countries arediversified in term of the economy activity; we divided the classification by using2003 Gross National Income (GNI) published by World Bank. According to 2003GNI per capita, calculated using the World Bank Atlas method2. The income groupsare: low income, $765 or less; middle income, $766 - $9,385 and high income,$9,386 or more.

Base on 2003 GNI report, some high income countries may also be developingcountries. Our samples in the paper will include this particular country and study thedifferences of country background into the efficiency of it Islamic Banks. The GulfCooperation Council (GCC), for example, are classified as developing high incomecountries. This paper unfolds as follows. Section 2 provides an overview of therelated studies in the literature, followed by a section that outlines the method usedand choice of input and output variables for the efficiency model. Section 4 reportsthe empirical findings. Section 5 concludes and offers avenues for future research.

2.0 REVIEW OF THE LITERATURE

While there have been extensive literatures examining the efficiency features of thecontemporary banking sector, particularly the U.S. and European banking markets,the work on Islamic banking is still in its infancy. Typically, studies on Islamic bankefficiency have focused on theoretical issues and the empirical work has reliedmainly on the analysis of descriptive statistics rather than rigorous statisticalestimation (El-Gamal and Inanoglu, 2004). However, this is gradually changing as anumber of recent studies have sought to apply various frontier techniques to estimatethe efficiency of Islamic banks.

Hussein (2003) provides an analysis of the cost efficiency features of Islamic banksin Sudan between 1990 and 2000. Using the stochastic cost frontier approach, heestimates cost efficiency for a sample of 17 banks over the period. The interestingcontribution of this paper is that specific definitions of Islamic financial products are

2 Atlas conversion factor, Calculating gross national income (GNI—formerly referred to asGNP) and GNI per capita in U.S. dollars for certain operational purposes, the World Bankuses the Atlas conversion factor. The purpose of the Atlas conversion factor is to reduce theimpact of exchange rate fluctuations in the cross-country comparison of national incomes.

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201212

used as outputs. In addition, the analysis is also novel as Sudan has a banking systembased entirely on Islamic banking principles. The results show large variations in thecost efficiency of Sudanese banks with the foreign owned banks being the mostefficient. State owned banks are the most cost inefficient. The analysis is extended toexamine the determinants of bank efficiency. Here, he finds that smaller banks aremore efficient that their larger counterparts. In addition, banks that have higherproportion of musharakah and mudharabah finance relative to total assets also haveefficiency advantages. Overall, the substantial variability in efficiency estimates isput down to various factors, not least the highly volatile economic environment underwhich Sudanese banks have had to operate over the last decade or so.

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

El-Gamal and Inanoglu (2004) used the stochastic frontier approach to estimate thecost efficiency of Turkish banks over the period 1990-2000. The study compared thecost efficiencies of 49 conventional banks with four Islamic special finance houses(SFHs). The Islamic firms comprised around 3% of the Turkish banking market.Overall, they found that the Islamic financial institutions to be the most efficient andthis was explained by their emphasis on Islamic asset-based financing which led tolower non-performing loans ratios. It is worth mentioning that the SFH achieved highlevels of efficiency despite being subjected to branching and other self-imposedconstraints such as the inability to hold government bonds.

Hassan (2005) examined the relative cost, profit, X-efficiency, and productivity ofthe world Islamic Banking industry. Employing a panel of banks during 1993-2001,he used both the parametric (Stochastic Frontier Approach) and non-parametric (DataEnvelopment Analysis) techniques as tools to examine the efficiency of the samplebanks. He calculated five DEA efficiency measures namely cost, allocative,technical, pure technical, and scale and further correlated the scores with theconventional accounting measures of bank performance. He found that the Islamicbanks are more profit efficient, with an average profit efficiency score of 84 per centunder the profit efficiency frontier compared to 74 per cent under the stochastic cost

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 13

frontier. He also found that the main source of inefficiency is allocative rather thantechnical. Similarly, his results suggest that the overall inefficiency was outputrelated. The results suggest that on average the Islamic banking industry is relativelyless efficient compared to their conventional counterparts in other parts of the world.The results also show that all five efficiency measures are highly correlated withROA and ROE, suggesting that these efficiency measures can be used concurrentlywith the conventional accounting ratios in determining Islamic bank performance.

Sufian (2006) examined the efficiency of the Malaysian Islamic banking sectorduring the period 2001-2004 by using the non-parametric Data EnvelopmentAnalysis (DEA) method. He found that scale efficiency outweighs pure technicalefficiency in the Malaysian Islamic banking sector, implying that Malaysian Islamicbanks have been operating at non-optimal of operations. He suggests that thedomestic Islamic Banking Scheme banks have exhibited a higher technical efficiencycompared to their foreign Islamic Banking Scheme bank peers. He suggests thatduring the period of study the foreign Islamic Banking Scheme banks’ inefficiencywere mainly due to scale rather than pure technical.

Recently, (Sufian et al. 2008) examined the efficiency of the Malaysian Islamicbanking sector during the period 2001-2006 by using DEA method. The empiricalfindings suggest that during the period of study, pure technical inefficiencyoutweighs scale inefficiency in the Islamic banking sector implying that the Islamicbanks have been managerially inefficient in exploiting their resources to the fullestextent. The empirical findings seem to suggest that the MENA Islamic banks haveexhibited higher technical efficiency compared to their Asian Islamic bankscounterparts. During the period of study he fined that pure technical inefficiency hasgreater influence in determining the total technical inefficiency of the MENA and theAsian Islamic banking sectors.

More recently Ahmad et al. (2010) investigates the efficiency of the Islamic bankingsectors in four Asian countries during the period of 2001–2006. The efficiencyestimates of individual banks are evaluated using the non-parametric dataenvelopment analysis (DEA) method. The results suggest that the Asian Islamicbanks have exhibited mean technical efficiency highest of 86.5% at 2004 duringstudy period suggesting mean input waste of 13.5%. This implies that the Islamicbanks in the Asian countries could have produced the same amount of outputs byonly using 86.5% of the amount of inputs they employed.

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201214

3. METHODOLOGY

A non-parametric Data Envelopment Analysis (DEA) is employed with variablereturn to scale assumption to measure input-oriented technical efficiency of theMENA and Asian Islamic banking sectors. DEA involves constructing a non-parametric production frontier based on the actual input-output observations in thesample relative to which efficiency of each firm in the sample is measured (Coelli,1996). Let us give a short description of the Data Envelopment Analysis3. Assumethat there is data on K inputs and M outputs for each N bank. For ith bank these arerepresented 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 wecalculate a ratio of all inputs, such as (u’yi/v’xi) where u is an M x 1 vector of outputweights and v is a K x 1 vector of input weights. To select optimal weights wespecify the following mathematical programming problem:

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

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

min (μ’yi),μ,φφ’xi = 1μ’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 reflecttransformations. Using the duality in linear programming, an equivalent envelopmentform of this problem can be derived:

min ,θ, λ

0 Yyi

0 Xxi

0 (3)where is a scalar representing the value of the efficiency score for the ith bankwhich will range between 0 and 1. is a vector of N x 1 constants. The linear

3 Good reference books on efficiency measures are Coelli et al. (1998), Thanassoulis (2001),Cooper et al. (2000), and Avkiran (2002).

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 15

programming has to be solved N times, once for each bank in the sample. In order tocalculate efficiency under the assumption of variable returns to scale, the convexityconstraint ( 1'1 N ) will be added to ensure that an inefficient bank is onlycompared against banks of similar size, and therefore provides the basis formeasuring economies of scale within the DEA concept. The convexity constraintdetermines how closely the production frontier envelops the observed input-outputcombinations and is not imposed in the constant returns to scale case. The variablereturns to scale technique therefore forms a convex hull which envelops the datamore tightly than the constant returns to scale, and thus provides efficiency scoresthat are greater than or equal to those obtained from the constant returns to scalemodel.

3.1 Multivariate Tobit Regression Analysis

It is also of considerable interest to explain the determinants of the technicalefficiency scores derived from the DEA model. As defined in equations 1 to 3, theDEA score falls between the interval 0 and 1 ( 10 * h ) making the dependentvariable a limited dependent variable. A commonly held view in previous studies isthat the use of the Tobit model can handle the characteristics of the distribution of(in) efficiency measures and thus provide results that can provide important policyguidelines to improve performance. Accordingly, DEA efficiency scores obtained inthe first stage is used as a dependent variable in the second stage one side censoredTobit model in order to allow for the restricted [0, 1] range of efficiency values.

Coelli et al. (1998) suggested several ways in which environmental variables can beaccommodated in a DEA analysis. The term “environmental variables” is usuallyused to describe factors, which could influence the efficiency of a bank. In this case,such factors are not traditional inputs and are assumed to be outside the control of themanager. Hence, the two-stage method used in this study involves the solution ofDEA problem in the first stage analysis, which comprises mainly the traditionaloutputs and inputs. In the second stage, the efficiency scores obtained from the firststage analysis are regressed against a set of bank characteristics.

The standard Tobit model can be defined as follows for bank i :

iii xy '* (4)

*ii yy if 0* iy and

0iy , otherwise

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201216

where ),0(~ 2 Ni , ix and are vectors of explanatory variables and unknown

parameters respectively, while *iy is a latent variable and iy is the DEA efficiency

score4.

3.2 Definition and the Choice of Variables

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

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

Under the intermediation approach, financial institutions are viewed asintermediating funds between savers and investors. In our case, Islamic banksproduce intermediation services through the collection of deposits and otherliabilities 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 asoutput variables.

4 The likelihood function )(L is maximised to solve based on 145 observations

(banks) of iy and ix is22 ))](2/(1[

0 02/12 )2(

1)1( ii

i i

xy

y yeFL

where,

dteF tx

ii 2//

2/1

2

)2(1

The first product is over theobservations for which the banks are 100 percent efficient (y = 0) and the second product is

over the observations for which banks are inefficient (y >0). iF is the distribution functionof the standard normal evaluated at /'

ix .

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 17

Following among others, Hassan and Hussein (2003), Hassan (2005), and Sufian(2006), a variation of the intermediation approach or asset approach originallydeveloped by Sealey and Lindley (1977) will be adopted in the definition of inputsand outputs used in this study. Furthermore, as at most times bank branches areengaged in the processing of customer documents and bank funding, the productionapproach might be more suitable for branch efficiency studies (Berger andHumphrey, 1997).

Due to entry and exit factor, the efficiency frontier is constructed by using anunbalanced sample of 78 Islamic banks operating in the World during the period1997-2009 (see Appendix 1) yielding 464 bank year observations. We are able tocollect data on three outputs and three inputs variables. Data for the empiricalanalysis is sourced from individual bank’s annual balance sheet and incomestatements and BankScope database by IBCA. The BankScope database converts thedata to common international standards to facilitate comparisons and all financialinformation is reported both in local currency and in US dollar. We use US dollardata which makes the comparison across country consistent. The Islamic banks aremodeled as multi-product firms producing three outputs namely, Total Loans (y1),which include loans to customers and other banks, Income (y2), which includeincome derived from investment of depositors’ funds and other income from Islamicbanking operations, and Other Earning Asset (y3), which include investmentsecurities held for trading, investment securities available for sale (AFS), andinvestment securities held to maturity, by engaging three inputs namely, TotalDeposits (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.

Table 1: Summary Statistics of the Variables Employed in the DEA Model (inmillion of USD)

Inputs Mean Min Max Std. Dev.

1997 Loan/ Advances (y1) 484.49 1.24 2,678.17 716.06

Income (y2) 43.28 0.07 306.97 73.59

Other Earning Asset (y3) 116.19 0.5 492.91 152.81

1998 Loan/ Advances (y1) 429.98 2.16 1,802.82 582.29

Income (y2) 35.29 0.31 158.02 47.05

Other Earning Asset (y3) 91.77 0.1 406.27 110.87

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201218

1999 Loan/ Advances (y1) 494.33 3.929795 2,199.43 658.39

Income (y2) 28.13 0.351027 134.97 35.73

Other Earning Asset (y3) 64.07 0.351027 713.5 149.1

2000 Loan/ Advances (y1) 641.02 103.7725 2,910.76 790.13

Income (y2) 2,910.76 100.6223 298.12 45.53

Other Earning Asset (y3) 1023.32 100.0184 1,023.32 248.95

2001 Loan/ Advances (y1) 384.37 100 2,550.76 535.77

Income (y2) 180.71 100.4844 1,012.27 172.78

Other Earning Asset (y3) 193.35 180.7074 806 172.73

2002 Loan/ Advances (y1) 607.01 100 6,219.41 1,244.66

Income (y2) 187.59 100 1,000.80 177.88

Other Earning Asset (y3) 249.32 100 1,041.76 233.85

2003 Loan/ Advances (y1) 504.2 100 5,327.74 953.15

Income (y2) 218.93 100.02 1,202.67 245.76

Other Earning Asset (y3) 248.23 100 924 241.14

2004 Loan/ Advances (y1) 536.19 100 6,004.76 1,160.13

Income (y2) 487.03 100.5146 10,416.44 1,598.04

Other Earning Asset (y3) 349.91 100 2,948.77 542.21

2005 Loan/ Advances (y1) 499.6 100 7,812.78 1,128.10

Income (y2) 276.4 100 2,230.19 409.95

Other Earning Asset (y3) 323.66 100 2,792.07 466.53

2006 Loan/ Advances (y1) 1,343.53 100 36,187.49 4,920.73

Income (y2) 51,267.32 100 2,112,488.00 293,026.56

Other Earning Asset (y3) 804.62 100 20,379.23 2,755.69

2007 Loan/ Advances (y1) 1519.03 0.4 18,671.14 3,505.80

Income (y2) 255.62 0.055398 4,841.30 784.24

Other Earning Asset (y3) 2,328.30 0.222547 58,728.30 9,021.58

2008 Loan/ Advances (y1) 1,760.39 0 14,348.50 2,756.63

Income (y2) 145.6 3.04765 1,136.19 214.21

Other Earning Asset (y3) 870.87 36.22882 6,458.56 1,257.60

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 19

2009 Loan/ Advances (y1) 2,440.08 537.3 7,179.08 2,792.14

Income (y2) 194.33 4.3 470.22 207.72

Other Earning Asset (y3) 1,446.82 136.2178 2,563.10 1,076.33

Inputs Mean Min Max Std. Dev.

1997 Deposits (x1) 596.4 1.83 4,085.50 1,017.64

Labour (x2) 17.67 0 224.78 50.67

Fixed Asset (x3) 36.46 0.2 570.33 129.47

1998 Deposits (x1) 551.98 3.1 4,411.31 1,019.97

Labour (x2) 15.74 0.14 196.71 42.23

Fixed Asset (x3) 27.45 0.25 426.44 91.7

1999 Deposits (x1) 202.46 0.23 3899.42 826.23

Labour (x2) 12.11 0.08 143.93 30.46

Fixed Asset (x3) 21.06 0.256849 341.2 71.77

2000 Deposits (x1) 757.52 100 4,571.53 1,081.94

Labour (x2) 113.81 100.15 277.81 36.32

Fixed Asset (x3) 123.64 100.28 417.05 65.49

2001 Deposits (x1) 1,211.27 110.2863 10,180.00 2,032.33

Labour (x2) 114.93 100 258.4 30.46

Fixed Asset (x3) 121.06 100 595.51 84.97

2002 Deposits (x1) 1,158.85 100.6635 11,645.60 2,254.99

Labour (x2) 116.83 100 265.87 34.05

Fixed Asset (x3) 128 100 657.87 96.1

2003 Deposits (x1) 1,684.02 100.2 14,534.00 3,300.34

Labour (x2) 120.49 100 266.93 38.56

Fixed Asset (x3) 122.45 100 664.86 87.12

2004 Deposits (x1) 5,721.14 101.13 169,950.96 26,147.79

Labour (x2) 184.34 100 2,775.08 411.3

Fixed Asset (x3) 110.04 100 289.95 29.84

2005 Deposits (x1) 1,959.94 100 19,153.49 3,917.47

Labour (x2) 137.24 100 802.36 106.85

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201220

Fixed Asset (x3) 109.69 100 209.48 21.62

2006 Deposits (x1) 521,095.84 100 22,000,100.00 2,955,759.22

Labour (x2) 2,243.73 100 102,030.89 13,507.71

Fixed Asset (x3) 120.59 100 369.8 53.27

2007 Deposits (x1) 4,640.32 0 115,155.10 17,768.07

Labour (x2) 41.77 0.01 484.13 93.84

Fixed Asset (x3) 92.32 0 2,534.00 382.91

2008 Deposits (x1) 2,039.78 0 18,100.05 3,380.45

Labour (x2) 31.15 0.47964 240.46 49.7

Fixed Asset (x3) 43.13 0.059955 371.93 77.6

2009 Deposits (x1) 3,457.00 232.3 7,151.25 3,340.16

Labour (x2) 30.78 2.269824 75.1 36.06

Fixed Asset (x3) 14.81 0.1 40.32 20.08

Source: Banks Annual Reports & Bankscope database compiled by IBCA.

This section discusses the technical efficiency change (TE) of the World Islamicbanking sectors, measured by the DEA method and its decomposition into puretechnical efficiency (PTE) and scale efficiency (SE) components. In the event of theexistence of scale inefficiency, we will attempt to provide evidence on the nature ofthe returns to scale of each Islamic bank. The Islamic banks’ efficiency is examinedfor each year under investigation.

As suggested by Bauer et al. (1998), DeYoung and Hasan (1998), and Isik andHassan (2002), constructing an annual frontier specific to each year is more flexibleand thus more appropriate than estimating a single multiyear frontier for the banks inthe sample. Following the earlier studies, for the purpose of the study, we prefer toestimate separate annual efficiency frontier for each year. In other words, there weresix separate frontiers constructed for the study. Isik and Hassan (2002) contended thatthe principal advantage of having panel data is the ability to observe each bank morethan once over a period of time. The issue is also critical in a continuously changingbusiness environment because the technology of a bank that is most efficient in oneperiod may not be the most efficient in another. Furthermore, by doing so, wealleviate, at least to an extent, the problems related to the lack of random error inDEA by allowing an efficient bank in one period to be inefficient in another,

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 21

assuming that the errors owing to luck or data problems are not consistent over time(Isik and Hassan, 2002).

4.1 Efficiency of the World Islamic Banking Sectors

Table 2 presents the mean efficiency scores of the Islamic banks of the world from1997 to 2009 via dedicated panel respectively. It is clear that the World Islamicbanks’ efficiency was on a declining trend from the years 1997 to 1999, increased inyear 2000, before declining again during the years 2001 and 2002. For the year 2003and 2004 it increase before decline for 3 years in 2005 to 2007 and finally increase inthe last two year on 2008 and 2009. The results seem to suggest that the WorldIslamic banks have exhibited mean technical efficiency of 78.0%, suggesting meaninput waste of 22.0%. This implies that the World Islamic banks could have producedthe same amount of outputs by only using 78.0% of the amount of inputs itemployed.

From Table 4 it is also clear that country income status whether the Islamic Bankoperated affected the efficiency level does not specifically at high income countryonly. Table 4 summarizes the highest and lowest efficiency score of Islamic Banksample. We take 3 specific sample of year 2008, 2003 and 1998. Year 2008 with 3bank share the score of the highest efficiency score is Faisal Islamic Bank of Egyptthat fall under middle income country, Asia Islamic Bank of Singapore that beencategorize high income country and Tadhamon International Islamic Bank of Yemenfrom Low income country. Year 2003 the most efficient is 2 banks Kuwait FinanceHouse of Kuwait and Al Rajhi Bank from Saudi, both from high income country.Year 1998 the most efficient is Faisal Islamic Bank of Egypt from middle incomecountry. It shows that the most efficient bank from the sample study is notspecifically to certain income country group only but shared among threeclassification groups. It applies also to the lowest efficient bank for this 3 year,started at 2008 with Bank Islam Brunei Darussalam from high income countryfollowed by Faisal Islamic Bank of Egypt on 2003 from middle income country andAl Shamal Islamic Bank of Sudan from low income country on 1998. From 1995 to2009 by referring to table 4 all the highest efficient score is 100% except of year1999 with 99% score. While the lowest is varies from the region of 17% to 87%.

During the period of study, we encounter two financial crisis happened, that is AsiaFinancial Crisis (AFC) on 1997 and Global Financial Crisis (GFC) on 2008. Firstlythe study will look on AFC since part of our data is from Asian Islamic banks andthis will provide us whether there is an impact on Islamic bank efficiency during thisperiod. The result at table 2 indicated that during AFC, the trend is declining from

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201222

88% in 1996 to 81% in 1997. It continuously decline to 80% in 1998 and again 76%in 1999. The result clearly shown that World Islamic Bank efficiency level isdeclining during AFC before it increase back to 84% in the year of 2000. It providesus an observation that World Islamic Bank is impacted by AFC during the period.

While for GFC, the result at table 2 indicated that, the trend is increasing from 50%in 2007 to 65% in 2008 and continuously increases to 95% in 2009. The result fromtable 2 clearly shown that World Islamic Bank efficiency level is increasing duringGFC period, contradict with decrease during AFC. There is possibility Islamic Bankhas learned a lot during AFC so that any crisis after it, the impact will be minimal.There is also possibility on migration of confidence from conventional bankingsystem to Islamic banking model during GFC that cause the result that favor Islamicbank.

Table 2: Summary Statistics of Efficiency Scores 1997 – 2009The table presents mean, minimum, maximum, and standard deviation of the World Islamicbanks technical efficiency (TE), and its mutually exhaustive pure technical efficiency(PTE) and scale efficiency (SE) components derived from the DEA. Panel A, B, C, D, E,until R shows the mean, minimum, maximum and standard deviation of TE, PTE, and SEof the Islamic banks for the years from 1997 to 2009 respectively. Panel S presents theWorld Islamic banks mean, minimum, maximum, and standard deviation of TE, PTE, andSE scores for all years. The TE, PTE, and SE scores are bounded between a minimum of 0and a maximum of 1.

Banks Mean Minimum Maximum Std. Dev

Panel F: All Countries 1997

Technical Efficiency 0.81 0.20 1.00 0.26

Pure Technical Efficiency 0.93 0.56 1.00 0.13

Scale Efficiency 0.88 0.20 1.00 0.25

Panel G: All Countries 1998

Technical Efficiency 0.80 0.13 1.00 0.27

Pure Technical Efficiency 0.91 0.33 1.00 0.18

Scale Efficiency 0.88 0.13 1.00 0.23

Panel H: All Countries 1999

Technical Efficiency 0.76 0.09 1.00 0.26

Pure Technical Efficiency 0.82 0.17 1.00 0.25

Scale Efficiency 0.92 0.52 1.00 0.12

Panel I: All Countries 2000

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 23

Technical Efficiency 0.84 0.45 1.00 0.12

Pure Technical Efficiency 0.97 0.45 1.00 0.11

Scale Efficiency 0.87 0.77 1.00 0.09

Panel J: All Countries 2001

Technical Efficiency 0.83 0.63 1.00 0.14

Pure Technical Efficiency 0.98 0.78 1.00 0.04

Scale Efficiency 0.84 0.64 1.00 0.14

Panel K: All Countries 2002

Technical Efficiency 0.61 0.32 1.00 0.26

Pure Technical Efficiency 0.97 0.56 1.00 0.08

Scale Efficiency 0.63 0.33 1.00 0.26

Panel L: All Countries 2003

Technical Efficiency 0.70 0.31 1.00 0.25

Pure Technical Efficiency 0.94 0.56 1.00 0.09

Scale Efficiency 0.75 0.44 1.00 0.24

Panel M: All Countries 2004

Technical Efficiency 0.74 0.37 1.00 0.20

Pure Technical Efficiency 0.97 0.58 1.00 0.10

Scale Efficiency 0.77 0.53 1.00 0.20

Panel N: All Countries 2005

Technical Efficiency 0.56 0.33 1.00 0.23

Pure Technical Efficiency 0.99 0.89 1.00 0.02

Scale Efficiency 0.57 0.33 1.00 0.23

Panel O: All Countries 2006

Technical Efficiency 0.54 0.15 1.00 0.26

Pure Technical Efficiency 0.96 0.00 1.00 0.13

Scale Efficiency 0.55 0.17 1.00 0.26

Panel P: All Countries 2007

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Technical Efficiency 0.50 0.01 1.00 0.35

Pure Technical Efficiency 0.68 0.01 1.00 0.36

Scale Efficiency 0.76 0.09 1.00 0.28

Panel Q: All Countries 2008

Technical Efficiency 0.65 0.02 1.00 0.33

Pure Technical Efficiency 0.76 0.02 1.00 0.31

Scale Efficiency 0.84 0.28 1.00 0.22

Panel R: All Countries 2009

Technical Efficiency 0.95 0.76 1.00 0.11

Pure Technical Efficiency 1.00 1.00 1.00 0.00

Scale Efficiency 0.95 0.76 1.00 0.11

Panel S: All Countries All Years

Technical Efficiency 0.78 0.43 1.00 0.19

Pure Technical Efficiency 0.93 0.58 1.00 0.11

Scale Efficiency 0.84 0.52 1.00 0.16

Note: Detailed results are available from the authors upon request

4.2 Composition of the Efficiency Frontier

While the results above highlight the sources of technical inefficiency of the Islamicbanks, we next turn to discuss the sources of the scale inefficiency of the Islamicbanks. As have been mentioned earlier, a bank can operate at CRS or VRS whereCRS signifies that an increase in inputs results in a proportionate increase in outputsand VRS means a rise in inputs results in a disproportionate rise in outputs. Further, abank operating at VRS can be at increasing returns to scale (IRS) or decreasingreturns to scale (DRS). Hence, IRS means that an increase in inputs results in ahigher increase in outputs, while DRS indicate that an increase in inputs results inlesser output increases.

To identify the nature of returns to scale, first the CRS scores (obtained with the CCRmodel) is compared with VRS (using BCC model) scores. For a given bank, if theVRS score equals to its CRS score, the bank is said to be operating at constant returnsto scale (CRS). On the other hand, if the scores are not equal, a further step is needed

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 25

to establish whether the bank is operating at IRS or DRS. To do this, the DEA modelis used under the non-increasing returns to scale assumptions (NIRS). If the scoreunder 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 bankis said to be operating at IRS (Coelli et al., 1998).

During the period of study, high income country Islamic banks seem to havedominated the highest three efficiency frontier, leading by Bahrain, followed by UAEand number three by Qatar. There is eight Islamic banks have failed to appear at leastonce on the frontier. All of the eight Islamic banks were fall under low and middleincome country.

In general, table 3 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 theearlier studies by among others McAllister and McManus (1993) and Noulas et al.(1990). To recap, McAllister and McManus (1993) have suggested that while thesmall banks have generally exhibited IRS, the large banks on the other hand tend toexhibit DRS and at best CRS. As it appears, the low and middle income countrywhere the Islamic banks operated, experienced increasing returns to scale (IRS) intheir operations during the period of the study. One implication is that for the low andmiddle income country Islamic banks, a proportionate increase in inputs would resultin more than a proportional increase in outputs. Hence, the Islamic banks at low andmiddle income country which have been operating at IRS could achieve significantcost 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 furtherconsolidation 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 costadvantages. Thus, the banks that experience IRS should either eliminate their scaleinefficiency or be ready to become a prime target for acquiring banks, which cancreate value from underperforming banks by streamlining their operations andeliminating their redundancies and inefficiencies (Evanoff and Israelvich, 1991). Onthe other hand, the results seem to suggest that the Islamic bank operated at highincome country incline to be more efficient compare to Islamic bank operated at lowand middle income country.

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Table 3: Composition of Production Frontiers (Partial from 2003 instead of 1997 dueto space constraints)

SL Financial Institutions Country ofOrigin 2003 2004 2005 2006 2007 2008 2009 Count

Bank1 ABC Islamic Bank Bahrain IRS IRS CRS IRS 12 Al Amin Bank Bahrain IRS IRS IRS 03 Al Baraka Islamic Bank Bahrain CRS IRS IRS IRS DRS DRS 14 Arab Banking Corporation Bahrain IRS CRS IRS IRS 15 Arcapita Bank B.S.C. Bahrain IRS CRS CRS CRS CRS IRS CRS 96 Arab Islamic Bank Bahrain 37 Bahrain Islamic Bank Bahrain IRS IRS IRS IRS DRS 08 Gulf Finance House Bahrain IRS IRS IRS IRS 09 Al Salam Bank Bahrain IRS 0

10 Shamil Bank Bahrain CRS CRS CRS IRS DRS CRS 1211 Taib Bank Bahrain IRS IRS IRS IRS 012 Ithmaar Bank Bahrain IRS IRS 013 Al Arafah Islami Bank Bangladesh IRS IRS IRS IRS DRS IRS 214 Shah Jalal Islami Bank Bangladesh IRS IRS IRS IRS CRS CRS 215 ICB Islamic Bank Limited Bangladesh IRS IRS DRS IRS 216 Islamic Bank Bangladesh Bangladesh CRS CRS IRS IRS 5

17Islamic Development Bankof Brunei Bhd Brunei CRS IRS CRS DRS DRS 7

18Bank Islam BruneiDarussalam Berhad Brunei CRS CRS IRS IRS CRS IRS 5

19 Faisal Islamic Bank Egypt IRS CRS IRS CRS 7

20Arab Gambian IslamicBank Gambia IRS IRS IRS 0

21 Bank Muamalat Indonesia Indonesia IRS IRS 022 Bank Mellat Iran IRS CRS DRS 123 Bank Refah Iran IRS 224 Al Bilad Islamic Bank Iraq IRS IRS 025 Jordan Islamic Bank Jordan CRS CRS IRS 326 Arab Islamic Bank Jordan IRS DRS 0

27Islamic International ArabBank Jordan IRS IRS IRS IRS IRS 2

28 Jordan Dubai Islamic Bank Jordan IRS IRS IRS CRS 529 Kuwait Finance House Kuwait CRS DRS CRS CRS 430 Affin Islamic Bank Berhad Malaysia IRS DRS CRS CRS 231 Alliance Islamic Bank Malaysia IRS CRS DRS 1

32Bank Islam MalaysiaBerhad Malaysia IRS DRS 0

33Bank Islam Malaysia (L)Berhad Malaysia IRS 0

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 27

34Bank Muamalat MalaysiaBerhad Malaysia IRS IRS CRS 1

35CIMB Islamic BankBerhad Malaysia IRS IRS CRS IRS 1

36EONCAP Islamic BankBerhad Malaysia IRS CRS 1

37Kuwait Finance HouseMalaysia Malaysia IRS DRS CRS 1

38 Hong Leong Islamic Bank Malaysia DRS IRS CRS 139 Maybank Islamic Berhad Malaysia CRS 140 RHB Islamic Bank Bhd Malaysia IRS CRS 1

41BAMIS-Banque Al WavaMauritanienne Islamique Mauritania IRS IRS CRS DRS 1

42AlBaraka Islamic BankB.S.C. Pakistan IRS IRS IRS IRS IRS 0

43 Meezan Bank Pakistan IRS IRS IRS CRS 1

44Standard CharteredModharaba Pakistan IRS IRS IRS 0

45 Bank Islami Pakistan Pakistan IRS IRS DRS 046 Dawood Islamic Bank Pakistan IRS DRS IRS 047 Dubai Islamic Bank Pakistan IRS IRS 0

48Emirates Global IslamicBank Pakistan IRS IRS 0

49 Arab Islamic Bank Palestine IRS IRS IRS 050 Al Rajhi Banking Saudi Arabia CRS CRS IRS 351 Bank AlJazira Saudi Arabia CRS CRS 252 EG Saudi Finance Bank Saudi Arabia IRS IRS IRS CRS 153 The Islamic Bank of Asia Singapore CRS 1

54Syria International IslamicBank Syria DRS DRS 0

55 Islamic Bank of Thailand Thailand IRS IRS IRS IRS DRS IRS 056 Al Baraka Turk Turkey IRS 057 Kuwait Finance House Turkey IRS 058 Ihlas Finan Turkey IRS DRS 059 Abu Dhabi Islamic Bank UAE CRS IRS CRS IRS DRS 460 Dubai Islamic Bank UAE CRS CRS CRS IRS DRS 961 Mashreq Bank UAE CRS IRS IRS IRS 162 Emirates Islamic Bank UAE IRS IRS IRS CRS DRS 163 Sharjah Islamic Bank UAE CRS IRS IRS CRS DRS 264 Noor Islamic Bank UAE CRS 1

65European IslamicInvestment Bank Plc

UnitedKingdom IRS IRS CRS CRS 2

66Islamic Bank of BritainPLC

UnitedKingdom IRS IRS DRS IRS 0

67 Qatar Islamic Bank Qatar IRS CRS IRS CRS IRS IRS 468 Qatar International Islamic Qatar CRS CRS CRS CRS DRS CRS 11

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Bank

69 Islamic Bank of Yemen Yemen IRS IRS IRS 0

70Tadhamon InternationalIslamic Bank Yemen IRS IRS IRS IRS DRS CRS 3

71 Saba Islamic Bank Yemen IRS IRS IRS IRS DRS CRS 172 Al Baraka South Africa South Africa IRS IRS IRS IRS 073 Al Baraka Sudan Sudan CRS CRS CRS IRS 474 Al Shamal Islamic Bank Sudan CRS DRS IRS 375 Faisal Islamic Bank Sudan IRS IRS IRS DRS IRS 0

76Islamic Co-operativeDevelopment Bank Sudan IRS IRS IRS IRS DRS IRS 0

77 Sudanese Islamic Bank Sudan IRS IRS IRS IRS DRS 078 Tadamon Islamic Bank Sudan IRS IRS IRS DRS IRS 0

Count Year 16 12 8 8 11 14Total Countries 25

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 sampleperiod (2001-2006) compared to the other banks in the sample.

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

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

Table 4: 1997-2009 Summary Table Efficiency (cover for period of 1997 to 2009)

Year Highest Country Level Lowest Country Level

2009 1.000 Highest. Bahrain, Arcapita Bank:high. 0.25 Lowest. Malaysia, Affin Bank:

middle.

2008 1.000

Highest. Egypt, Faisal IslamicBank: Middle. Singapore, AsiaIslamic Bank: High. Yemen,Tadhamon International IslamicBank: Low

0.054Lowest. Brunei,Bank IslamBrunei Darussalam Berhad:High

2007 1.000

Highest. Iraq, Al Bilad IslamicBank: Middle. Kuwait, KuwaitFinance House: High. SaudiArabia, EG Saudi Finance Bank:Middle.

0.071 Lowest. Iran, Bank Tejarat:Middle

2006 1.000

Highest. Iran, Bank Mellat:Middle. Mauritania, BAMIS-Banque Al Wava MauritanienneIslamique: Low

0.149 Lowest. Iraq, Al Bilad IslamicBank: Middle

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 29

2005 1.000 Highest. Kuwait, Kuwait FinanceHouse: High 0.302 Lowest. Thailand, Islamic Bank

of Thailand: Middle.

2004 1.000Highest. Egypt, Faisal IslamicBank: Middle. Qatar, QatarIslamic Bank: High

0.525 Lowest. Thailand, Islamic Bankof Thailand: Middle.

2003 1.000Highest. Kuwait, Kuwait FinanceHouse: High. Saudi Arabia, AlRajhi Bank: High

0.307 Lowest. Egypt, Faisal IslamicBank: Middle.

2002 1.000

Highest. Egypt, Faisal IslamicBank: Middle. Iran, Bank Refah:Middle. Qatar, Qatar IslamicBank: High. Pakistan, MeezanBank: Low.

0.254 Lowest. Saudi Arabia, Al RajhiBank: High

2001 1.000

Highest. Egypt, Faisal IslamicBank: Middle. Iran, Bank Refah:Middle. Kuwait, Kuwait FinanceHouse: High. Qatar, Qatar IslamicBank: High.

0.639

Lowest. BAMIS-Banque AlWava Mauritanienne Islamique:Low. Pakistan, MeezanBank:Low.

2000 1.000Highest. Egypt, Faisal IslamicBank: Middle. UAE, Abu DhabiIslamic Bank : High.

0.693 Lowest. Sudan, Al ShamalIslamic Bank: Low.

1999 0.990 Highest. Egypt, Faisal IslamicBank: Middle. 0.172 Lowest. Sudan, Al Shamal

Islamic Bank: Low.

1998 1.000 Highest. Egypt, Faisal IslamicBank: Middle. 0.205 Lowest. Sudan, Al Shamal

Islamic Bank: Low.

1997 1.000

Highest. Egypt, Faisal IslamicBank: Middle. UAE, DubaiIslamic Bank: High. Yemen,Tadhamon International IslamicBank: Low.

0.222 Lowest. Gambia, Arab GambianIslamic Bank: Low

4.3 The Determinants of the Islamic Banks’ Efficiency

Regression results focusing on the relationship between bank efficiency and theexplanatory variables are presented in Table 5. The equations are based on 402observations during the 1997-2009 periods. Since our data is based on dynamic pane,some year we might have the data and some year not available. Due to that we havein total 402 observations during the study period for all banks. As pointed bySaxonhouse (1976), heteroscedasticity can emerge when estimated parameters areused as dependent variables in the second stage analysis. Thus, following amongothers Hauner (2005) and Pasiouras (2007), QML (Huber/White) standard errors andcovariates are calculated. Several general comments regarding the test results arewarranted. The model performs reasonably well in at least two respects. For one,

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201230

results for most variables remain stable across the various regressions tested.Secondly, the findings suggest that all explanatory variables have the expected signsand in most cases are statistically different from zero.

The return on equity (ROE) has a positive relation to Bank Efficiency, whichindicating that the Islamic bank has more profit, and its productivity growth will behigh. The finding implies that the higher the return on equity, the higher the bankproductivity growth will be. The result consistent with Sufian and Majid (2007)showed that bank profitability has a significantly positive impact on bank efficiency.The ROE result is consistently low for 10 model study and none of them werestatistically significant even at 10% level.

For operating expense to total assets (ΟΕ/ΤΑ) the coefficient is expected to benegative, since the higher the ratio of operating expense to total assets the lessefficiently the Islamic Banks is utilizing its inputs to generate a given level of output.But the result exhibit positive relationship at 0.0001 nearly to negative with bankefficiency levels and is statistically significant.

In line with the findings of Akhigbe and McNulty (2005), EQUITY/TA seem toexhibit positive relationship with bank efficiency levels and is statistically significant.The findings seem to suggest that the more efficient banks, ceteris paribus, uses lessleverage (more equity) compared to their peers.

The proxy of loans intensity, LOANS/TA, reveals negative relationship (statisticallysignificant at the 1% level) with bank efficiency levels. The findings seem to suggestthat banks with higher loans-to-asset ratio tend to exhibit lower efficiency levels. Thefinding is contradict with earlier findings by Sufian and Noor (2009) while IsikandHassan (2003) argue that the positive relationship between loan activity and bankefficiency may be attributed to the ability of the relatively efficient bank to manageoperations more productively which enables them to have lower production costs andconsequently to offer more reasonable loan terms. This allows them to gain largermarket shares in the loan market segment.

The findings indicate that Natural Logarithm of Total Asset (LNTA), as a proxy ofbank’s size, shows positive and significant coefficients at 1% level, suggesting thatthe larger the bank, the more efficient the bank will be, purely because of theeconomies of scale arguments. Thus, assuming that the Islamic banks’ average costcurve is U-shaped, the recent growth policies of the small and medium banks seem tobe consistent with cost minimization.

The proxy for market power, Natural Logarithm of Deposit (LNDEPO) revealsnegative relationship with bank efficiency and is statistically not significant;

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 31

suggesting that the more efficient banks are associated with the banks with lowermarket share, thus negatively correlated diminishing the market leadership argument.The results imply that Islamic banks with lower market share can be efficient becausemaintaining lesser branch network and relative small business is advantage at certainpoint. Furthermore the relationship with customer is more since low customer basecompare with higher market share Islamic bank.

Following Havrylchyk (2006), Sathye (2001), and Isik and Hassan (2003), the ratioof non-performing loans to total loans (NPL/TL) is incorporated as an independentvariable in the regression analysis as a proxy of risk. As expected, NPL/TL exhibitspositive relationship in all years with bank efficiency and statistical confidence formodel 1, 2, 3 and 9 indicating increase in efficiency. The finding is contradict withearlier findings by among others, Kwan and Eisenbeis (1995), Resti (1997), and Barret al. (2002) who found negative relationship between problem loans and bankefficiency. The results imply that the Islamic banks can less focus on credit riskmanagement, which has been proven to be problematic in the recent past forinefficiency.

Another factor, which could explain Islamic banks efficiency, is the relativelyvolatile rates of national income growth recorded during the period of analysis. Wemeasured the national income growth by using Gross Domestic Product (GDP) whichbeen log and code as LNGDP. The result shown positive relationship between bankefficiency and GDP and all the efficiency measure were statistically significant to TEat the 1% level. The result explains that demand for financial services tends to growas economies expand and societies become wealthier. Bank businesses will growthwith every GDP percentage growth since both variables positively correlated andclosely dependent in every economics where Islamic banks operated. Base onobservation, during good economy cycle, all finance infrastructures like stockmarket; demand for financial product by businesses like financing, deposit etc willincrease. This directly will increase the Islamic banks efficiency.

Another independent variable in the regression was inflation rate for each of countrywhere Islamic banks operated. Specifically, we include inflation as one of theindependent variables in the host Islamic banks economy to analyze the impact on thebank efficiency. Inflation means a persistent, substantial rise in the general level ofprices related to an increase in the volume of money circulation in the market. Thedata for inflation come from the World Development Indicators published by WorldBanks. The item was code as INFLATION and result of regression shown negativerelationship with bank efficiency though not statistically significant.

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201232

We also have market capitalization as other independent variables in the regressionequation. This market capitalization represents the size of overall stock market wherethe Islamic bank operated, the figure been take for end of period. The proxy formarket capitalization of country where Islamic bank originated had been code asMARKET. The result shown negative relationship with bank efficiency and isstatistically not significant.

As a robustness check, several binary dummy variables had been introduced torepresent several items. It started with 1998 Asian Financial Crisis (AFC), eachdummy variable which takes a value of 1 for Islamic banks in 1998 and 0 otherwisein the regression model 2, 4 and 6 of Table 5. AFC entered positively in theregression model implying that the World Islamic banks bank efficiency has animpact during AFC. For model 2, it significant at 1% level while model 6 itsignificant at 5% level. It suggesting that during AFC, Islamic banking overallbusiness is impacted due to close relationship with country economic growth andGDP as discuss before. The finding is consistent with our earlier findings in theIslamic banks relationship with GDP.

Then 2008 Global Financial Crisis (GFC), which takes a value of 1 for Islamic banksin 2008 and 0 otherwise in the regression model 3, 5 and 7 of Table 5. For model 3and 5, both have negative relationship and not statistically significant while model 7the result is also negative but significant. These contradict to another dummy forAFC that positively with bank efficiency. It may indicate that World Islamic bankshave relatively well prepared after the AFC, furthermore GFC is more concentratedat western countries compare to AFC that concentrated in Asia. Although the Islamicbanks data also consist of Islamic banks from UK, due to bigger number of otherregion and countries, it may have impact very minimal in the overall result. The otherreasons we may find is Islamic banks are does not have an impact during GFC is theexposure as western bank ie. In subprime crisis that mean financial crisis that arosein the mortgage market after a sharp increase in mortgage foreclosures, mainlysubprime, collapsed numerous mortgage lenders and hedge funds. The other thing isthe practices of excessive consumption supported by excessive lending by the banksand other reason as well.

The Middle East and North Africa countries (MENA) variables which take value of 1for Islamic banks originated from MENA and 0 otherwise in the regression models 4and 5 of Table 5. MENA entered positively in the regression model implying that theMENA Islamic banks are relatively more efficient compared to Islamic banks atother countries. The finding is consistent with Sufian and Noor (2009).

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 33

Then followed by Asian countries (ASIA), each dummy variable will takes a value of1 for Islamic banks origin from ASIA and 0 otherwise in the regression model 6 and7 of Table 5. Both have negative relationship and statistically significant. Thisindicated that Islamic banks from Asia were inefficient compared to other countries.

Since sample study is across 25 countries, we have tested dummy for analyzing theIslamic banks originated base on three GNI country income classification that isLOW, MEDIUM and HIGH in the regression model 8, 9 and 10 of Table 5representing low, medium and high income countries respectively. Each model likeLOW will take value of 1 for Islamic banks originated from World Bank GNIclassification as low income country and 0 otherwise. Same procedure applies forMEDIUM and HIGH income countries.

The finding for LOW is negative relationship with statistically significant. While forMEDIUM positive relationship and not significant and lastly for HIGH negativerelationship and not significant. This indicated that Islamic banks at low and highincome country were inefficient compare to middle income country. It shouldrelatively positive relationship since the higher income per capita of the country; itwill lead to more deposit in the Islamic banks, more product offering that can leadmore profit to Islamic banks. This is interesting because middle income countryresult is positive relationship although not statistically significant while low incomecountry is negative relationship and significant at 1% level. There is possibility thatthe HIGH income country, the tendency of the people to use Islamic bank financialproduct is lower compare to MEDIUM since the exposure to other investmentproduct. The most favourite investment is real estate property. By having these, thesubscription to Islamic banks financial product is lower therefore support the result ofnegative relationship with bank efficiency.

If efficiency analysis is done in a static setting, they may fail to capture developmentsin the regulatory environment and in the marketplace, which may have changed theunderlying production technology and the associated production functions. Becausethe world Islamic banking industry has been under continuous structural change, thework analyzes the efficiency of the World Islamic banks at different points of time.Furthermore, different banking forms could demonstrate different reactions toenvironmental changes. Hence, the change in the financial landscape and structure,etc. may vary across banking groups (Saunders et al. 1990; Button and Weyman-Jones, 1992; Berger et al. 1995). As a final check, dummy variables for severalevents to represent changes over time and environmental changes i.e. AFC, GFC,

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201234

MENA, ASIA, GNI LOW, GNI MEDIUM and GNI HIGH are used to take intoaccount for changes in the Islamic banking sector environments during the period5.

The results above seem to suggest that most of the bank trait variables continued toremain robust in the directions and significance level. Favourable economicconditions during the period of study may have fuelled higher demand for Islamicbanking products and services, reduces default loan probabilities, and thus resultingin a greater output.

Table 5: Results of Tobit Regression Analysis

φjt = α + β1ROE + β2OE/TA + β3EQUITY/TA

+ β4LNTA + β5LOANS/TA + β6LNDEPO+ β7NPL/TL

+ β8LNGDP+ β9 INFLATION + β10MARKET+

β11ΣDUMMY (AFC, GFC, MENA, ASIA, LOW, MEDIUM, HIGH)+ εj

The dependent variable is individual bank’s technical efficiency score derived from theDEA; ROE is proxy measure of bank’s profitability calculated as net income after taxdivided by total shareholders equity;.; OE/TA is a measure of bank operating expensesagainst total asset; EQUITY/ TA is a measure of bank leverage intensity measured bybanks’ total shareholders equity divided by total assets; LNTA is the size of the bank’s totalasset measured as the natural logarithm of total bank assets; LOANS/TA is a measure ofbank’s loans intensity calculated as the ratio of total loans to bank total assets; LNDEPO isa measure of bank’s market share calculated as a natural logarithm of total bank deposits;NPL/TL is a measure of banks risk calculated as non performing loans divided by totalloans; LNGDP is country gross domestic product of the country’s measured as the naturallogarithm of gross domestic product; INFLATION is country inflation rates; MARKET isoverall stock market capitalization size of the country’s bank operated; DUMMY is adummy variables which takes a value of 1 for AFC, 0 otherwise; same apply for GFC,MENA, ASIA, LOW, MEDIUM, and HIGH where each variable takes a value of 1, 0otherwise.

Note: Values in parentheses are z-statistics.***, **, and * indicates significance at 1, 5, and 10% levels respectively.

5 AFC and GFC are dummies for specific event for the financial crisis while GNI LOW,MEDIUM AND HIGH were use as indicator variables for country income levels where theIslamic Banks originated. All of this was included in the regression equation base ondifferent regression model.

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 35

ExplanatoryVariables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Model 10

Constant0.4566*** 0.4504*** 0.4480*** 0.4148*** 0.4142*** 0.5286*** 0.5255*** 0.5317*** 0.4316*** 0.4595***

(5.9909) (5.9434) (5.8780) (6.0004) (5.9956) (6.8174) (6.6184) (6.8614) (5.2527) (6.1917)

BankCharacteristic

ROE2.0500 3.3400 9.0200 0.0002 0.0001 0.0001 9.3600 -2.6900 -3.0700 5.4800

(0.1646) (0.2663) (0.0072) (1.1799) (0.9667) (0.9385) (0.7341) (-2.319) (-0.2365) (0.4132)

OE/TA0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 0.0001*** 8.9300 0.0001** 0.0001**

(2.7580) (2.9388) (2.7100) (2.8168) (2.6523) (3.3556) (3.1468) (1.5275) (2.0568) (2.8846)

EQUITY/TA4.15*** 4.2500*** 4.0800*** 4.3700*** 4.2300*** 4.52*** 4.38*** 3.8400*** 3.7600*** 4.44***

(5.0034) (5.1513) (5.1166) (5.5592) (5.5481) (6.1730) (6.0486) (3.6923) (3.9390) (5.2553)

LNTA0.061** 0.0643** 0.0664** 0.0473* 0.0489* 0.0525* 0.0538* 0.0503* 0.0593* 0.0576*

(1.9489) (2.0260) (2.1031) (1.6617) (1.7298) (1.7988) (1.8507) (1.6485) (1.8378) (1.9361)

LOANS/TA-4.36** -4.26*** -4.4400*** -4.7200*** -4.8600*** -4.7200*** -4.8500*** -4.7500*** -4.1600*** -4.7900***

(-6.6619) (6.4826) (7.1390) (7.6167) (-8.195) (-7.3499) (-7.8500) (-7.527) (-6.0146) (-6.3964)

LNDEPO-0.0124 -0.013 -0.0141 -0.0058 -0.0064 -0.0087 -0.0092 -0.0118 -0.0143 -0.0099

(-0.6847) (-0.7212) (-0.7683) (-0.3755) (-0.4190) (-0.5306) (-0.5682) (-0.68) (-0.7402) (-0.6038)

NPL/TL0.0216*** 0.0197** 0.0219*** 0.0089 0.0109 0.0139 0.0158 0.0164 0.0264*** 0.0135

(2.4982) (2.2123) (2.5712) (0.9438) (1.2027) (1.5381) (1.8136) (1.8584) (2.7920) (1.1839)

EconomicCondition

LNGDP4.01*** 4.04*** 4.8100*** 5.2100*** 5.7900*** 5.1400*** 5.6600*** 3.8400*** 3.6100*** 4.3800***

(5.5591) (5.5516) (4.5493) (5.7628) (4.7366) (5.6030) (4.7225) (5.2333) (4.4956) (5.5520)

INFLATION-0.0073 -0.0069 -0.0062 -0.0032 -0.0027 -0.0058 -0.0053 -0.0058 -0.0075 -0.0063

(-1.4690) (-1.4191) (-1.2759) (-0.7341) (0.6326) (1.3914) (1.2718) (1.3612) (1.5502) (1.2950)

MARKET-1.3100 -1.3000 -9.8900 7.4100 9.4300 -3.0200 -2.7600 -3.4300 -8.0900 -2.9400

(-0.3297) (-0.3273) (-0.2442) (0.1800) (0.2252) (0.7426) (0.6649) (0.8331) (0.2017) (0.6806)

AFC0.1579*** 0.1382 0.1334**

(2.5429) (2.2749) (2.1909)

GFC-0.0885 -0.0696 -0.0638**

(1.0498) (0.7801) (0.7190)

MENA0.1426 0.1399

(2.8831) (2.8167)

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201236

ASIA-0.1142*** -0.1111***

(-2.6334) (-2.5142)

LOW-0.1260***

(-0.8331)

MEDIUM0.0556

(1.2758)

HIGH-0.0629

(1.1295)

R2 0.1404 0.1473 0.1479 0.1828 0.1826 0.1754 0.1746 0.1662 0.1465 0.1456

Adj. R2 0.0895 0.0920 0.0926 0.1251 0.1249 0.1171 0.1164 0.1121 0.0912 0.0902

Log likelihood -14.2794 -13.4475 -13.2934 -8.9971 -9.0390 -9.8911 -0.9927 -10.9576 -13.5304 -13.5392

No. ofObservations 198.0000 198.0000 198.0000 198.0000 198.0000 198.0000 198.0000 198.0000 198.0000 198.0000

5.0 CONCLUSIONS AND DIRECTIONS FOR FUTURE RESEARCH

In this paper, we examine the performance of the World Islamic banks that consist of25 countries during the period of 1997-2009 with 78 Islamic banks involved. Theefficiency estimates of individual banks are evaluated using the non-parametric DataEnvelopment Analysis (DEA) approach. We have further correlated bank efficiencyscores derived from the DEA against a set of bank specific variables.

The empirical findings suggest that during the period of study, pure technicalefficiency outweighs scale efficiency in the World Islamic banking sector implyingthat the Islamic banks have been managerially efficient in exploiting their resourcesto the fullest extent. The empirical findings seem to suggest that the World Islamicbanks have exhibited high pure technical efficiency. During the period of study wefind that pure technical inefficiency has greater influence in determining the totaltechnical inefficiency of the World Islamic banking sectors.

The findings suggest that technical efficiency is positively and significantlyassociated with loans intensity, size, and capitalization. The empirical results showthat technically more efficient banks are those that have higher market share and lownon-performing loans ratio. We also find positive correlation between bankprofitability and technical efficiency levels, indicating that the more efficient bankstend to be more profitable. The results also suggest that favourable economicconditions to exhibit positive relationship with bank efficiency.

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 37

The finding for LOW income country is negative relationship with statisticallysignificant. While for MEDIUM positive relationship and not significant and lastlyfor HIGH negative relationship and not significant. This indicated that Islamic banksat low and high income country were inefficient compare to middle income country.

Due to its limitations, the paper could be extended in a variety of ways. Firstly, thescope of this study could be further extended to investigate changes in cost,allocative, and technical efficiencies over time. Secondly, it is suggested that furtheranalysis into the investigation of the Islamic banking sector efficiency to considerrisk exposure factors. Thirdly, future research into the efficiency of the Islamicbanking sector efficiency could also consider the production function along with theintermediation function. Finally, investigation of changes in productivity over time asa result of technical change or technological progress or regress by employing theMalmquist Total Factor Productivity Index could yet be another extension to thepaper.

Despite these limitations, the findings of this study are expected to contributesignificantly to the existing knowledge on the operating performance of the WorldIslamic banking industry. Nevertheless, the study have also provide further insight tobank specific management as well as the policymakers with regard to attainingoptimal utilization of capacities, improvement in managerial expertise, efficientallocation of scarce resources and most productive scale of operation of the banks inthe industry. This may also facilitate directions for sustainable competitiveness ofWorld Islamic banking operations in the future.

Acknowledgements

I would like to thank the anonymous journal referee and Prof Dr. Kabir Hassan (theeditor), I am particularly grateful for the anonymous comments that helped me tobetter conceptualize, improvise and situate this version of the paper. Naturally theusual disclaimers apply.

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The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 41

APPENDIX 1

SL Financial Institutions Country of Origin Classification countries

1 ABC Islamic Bank Bahrain High income country

2 Al Amin Bank

3 Al Baraka Islamic Bank

4 Arab Banking Corporation

5 Arcapita Bank B.S.C.

6 Arab Islamic Bank

7 Bahrain Islamic Bank

8 Gulf Finance House

9 Al Salam Bank

10 Shamil Bank

11 Taib Bank

12 Ithmaar Bank

13 Al Arafah Islami Bank Bangladesh Low income country

14 Shah Jalal Islami Bank

15 ICB Islamic Bank Limited

16 Islamic Bank Bangladesh

17Islamic Development Bank of BruneiBhd

18 Bank Islam Brunei Darussalam Berhad Brunei High income country

19 Faisal Islamic Bank

20 Arab Gambian Islamic Bank Egypt Lower middle income

21 Bank Muamalat Indonesia Gambia Low income country

22 Bank Mellat Indonesia Lower middle income

23 Bank Refah Iran Lower middle income

24 Al Bilad Islamic Bank

25 Jordan Islamic Bank

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201242

26 Arab Islamic Bank Iraq Lower middle income

27 Islamic International Arab Bank Jordan Lower middle income

28 Jordan Dubai Islamic Bank

29 Kuwait Finance House

30 Affin Islamic Bank Berhad

31 Alliance Islamic Bank Kuwait High income country

32 Bank Islam Malaysia Berhad Malaysia Upper middle income

33 Bank Islam Malaysia (L) Berhad

34 Bank Muamalat Malaysia Berhad

35 CIMB Islamic Bank Berhad

36 EONCAP Islamic Bank Berhad

37 Kuwait Finance House Malaysia

38 Hong Leong Islamic Bank

39 Maybank Islamic Berhad

40 RHB Islamic Bank Bhd

41BAMIS-Banque Al WavaMauritanienne Islamique

Mauritania Low income country

42 AlBaraka Islamic Bank B.S.C. Pakistan Low income country

43 Meezan Bank

44 Standard Chartered Modharaba

45 Bank Islami Pakistan

46 Dawood Islamic Bank

47 Dubai Islamic Bank

48 Emirates Global Islamic Bank

49 Arab Islamic Bank Palestine Low income country

50 Al Rajhi Banking Saudi Arabia Upper middle income

51 Bank AlJazira

52 EG Saudi Finance Bank

The Determinants of World Islamic Banks’ Efficiency Does Country Income Level... 43

53 The Islamic Bank of Asia Singapore High income country

54 Syria International Islamic Bank Syria Lower middle income

55 Islamic Bank of Thailand Thailand Lower middle income

56 Al Baraka Turk Turkey Lower middle income

57 Kuwait Finance House

58 Ihlas Finan

59 Abu Dhabi Islamic Bank UAE High income country

60 Dubai Islamic Bank

61 Mashreq Bank

62 Emirates Islamic Bank

63 Sharjah Islamic Bank

64 Noor Islamic Bank

65 European Islamic Investment Bank Plc United Kingdom High income country

66 Islamic Bank of Britain PLC

67 Qatar Islamic Bank Qatar High income country

68 Qatar International Islamic Bank

69 Islamic Bank of Yemen Yemen Low income country

70 Tadhamon International Islamic Bank

71 Saba Islamic Bank

72 Al Baraka South Africa South Africa Lower middle income

73 Al Baraka Sudan Sudan Low income country

74 Al Shamal Islamic Bank

75 Faisal Islamic Bank

76Islamic Co-operative DevelopmentBank

77 Sudanese Islamic Bank

78 Tadamon Islamic Bank

Total Countries 25

Journal of Islamic Economics, Banking and Finance, Vol. 8 No. 2, Apr - Jun 201244

Total Banks 78

Income Groups

Low Income (55) - (Income Per Capita: $765 or less)

Middle Income (52) - (Income Per Capita: $766–9,385)

High Income (40) - (Income Per Capita: $9,386 or more)

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