International Journal of Business and Society, Vol. 16 No. 1, 2015, 19 - 38

MALAYSIAN ISLAMIC BANKS’ EFFICIENCY: AN INTRA-BANK COMPARATIVE ANALYSIS OF ISLAMIC WINDOWS AND FULL-FLEDGED SUBSIDIARIES

Oladipupo Luqman Salami University Technology Petronas

Adewale Abideen Adeyemi♣ International Islamic University Malaysia

ABSTRACT

This paper aims to fill an apparent dearth of empirical studies that compare the efficiency of Islamic banks in Malaysia during their operation as Islamic windows and later transformation to full-fledged Islamic banks. Data obtained from the annual financial reports of the sampled banks is analyzed using the Data Envelopment Analysis (DEA) via DEAP 2.1 software to assess both the technical and scale efficiency of the banks under sample. Results obtained indicate that the banks have improved over the years in terms of both scale and technical efficiency although the former takes prominence. In general, the banks were found to be more efficient as Islamic windows compared to being full-fledged subsidiaries. This augurs well for the current disposition where, as per the Islamic Financial Service Act 2013, Islamic banks in Malaysia may now operate as full-fledged banks from their hitherto Islamic banking window status.

Keywords: Scale Efficiency; Technical Efficiency; Islamic Window; Full-Fledged Banks.

1. INTRODUCTION

The banking industry is arguably the most regulated in any part of the world. This may be due to a plethora of reasons including the nature of their product – money, as well as their dual but conflicting obligations of liquidity and profitability to their depositors and shareholders respectively. As such, the need for banks to be efficient cannot be discounted. As rightly noted by Sufian (2007b), factors like globalisation, deregulation, financial innovation, etc and their consequential implication for financial stability advertises the need for banks to place performance upon themselves to sort through the often-impassioned arguments. Therefore, it is by design rather than happenstance that recently, numerous policies and academic attention has been placed on banks’ efficiency (Hasan, Koetter, & Wedow, 2009). However, in contrast

♣ Corresponding author: Department of Finance, Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia. Email: [email protected] 20 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries to the enormous researches on banking efficiency especially from the conventional perspective, a dearth of empirical studies from the Islamic banking perspective leaves a lacuna in the extant banking efficiency literature. This is rather ironical especially viewed against the backdrop of the monumental growth witnessed in the Islamic banking industry since the early 2000 (Laldin, 2008).

The Islamic banking system (IBS) is gradually becoming a global phenomenon and it is incontrovertible that Malaysia is one of the major hubs of the burgeoning industry. In Malaysia for instance, Islamic banking operates in two folds and under two different acts vis. the Islamic Financial Services Act 2013 and the Financial Services

Act 2014. As such, there are the full-fledged Islamic banks and conventional banks’ Islamic window both of which have grown tremendously over the past years (Mokhtar, Abdullah, & Al-Habshi, 2006). In a report by the Bank Negara Malaysia (Central Bank of Malaysia), Islamic banking sector has increased in total assets to RM 434.6 million 2011. This amounted to 22.4% of the total banking assets in the country as at the end of 2011 (Porter, 2012).

Arguably, the gradual development witnessed in the Islamic banking industry is reflected in its ability to withstand financial crisis. According to Derbel, Bouraoui & Dammak (2011), the effect of the 2007/2008 global financial crisis on the Islamic banks is relatively mild compared to the conventional banks. This arouses interest on whether these banks are more efficient compared to their conventional counterparts. In this regard, numerous studies have compared both types of banks in terms of some efficiency benchmarks. For instance, studies like Bader, Mohamad, Ariff & Hassan (2008) and Rafiuddin & Alam (2012) found that the Islamic banks are more profit efficient while the conventional banks are more cost efficient. This may not be unexpected given that either bank’s operational philosophy differs.1 However, an apparent lacuna is noted in lack of efficiency studies based on intra-banking system in which case, a full-fledged Islamic bank’s efficiency is yet to be compared to its performance during operation as an Islamic window. This present study, therefore, aims to fill this gap in the context of the Malaysian banking industry.

The remaining of this paper is divided as follows. Immediately following this brief introduction is the literature review. This covers efficiency issues relating to Islamic banks, and the input and output variables used in the efficiency analysis of the study. Thereafter, the research methodology is discussed followed by the results of the analysis. The paper ends with a summary of major findings and the conclusion.

1 According to Rafiuddin & Alam (2012), while the Islamic banks operate on a non-interest basis and have limited options on what type financial instruments they can trade in; their conventional counterparts operate an interest- based system and have more rooms to manoeuvre as per the types of instruments they can deal in. Oladipupo Luqman Salami and Abideen Adeyemi Adewale 21

2. LITERATURE REVIEW

2.1. Malaysian Islamic Banks Efficiency Studies

As noted above a lot of studies have been conducted on bank efficiency and performance in the developed world. However, empirical studies on bank performance and efficiency in developing countries, especially emerging economies like Malaysia is relatively scarce. This is even more so in the context of Islamic banks. Most studies on Islamic banking efficiency compared Islamic banks with conventional banks. Samad (2009) was among the first researchers that conducted research on Malaysian Islamic banks efficiency. The study measured Islamic banks performance, in terms of profitability in comparison to their conventional counterparts using financial ratios as analysis tool. It compared efficiency of full-fledged Malaysian Islamic bank against conventional bank counterpart from 1992 to 1996. Samad (2009) established that conventional banks are more efficient in terms of managerial efficiency than the fully fledged Islamic bank. The study further examined productive efficiency and revealed that Islamic banks’ average utilization rate is lower compared to conventional banks.

While still using financial ratios analysis, Samad (2004) also examined Malaysian Islamic banks efficiency performance over a longer period covering 1984 to 1997. Thestudy concluded that Islamic banks are more efficient compared to conventional banks in terms of liquidity and risk vulnerability. However, the study mentioned that Islamic banks are still under-performing due to lack of management skills which affected their growth during the period covered. Samad (2004) further adds that Islamic bank is less profitable compared to conventional banks, which prove that Islamic bank is less efficient. However, the productivity test by loan recovery measure reveals that Islamic banks are highly efficient and that bad debt as a percentage of shareholders’ fund, loans and deposits also indicates high superiority over the conventional banks.

Sufian (2006) investigated Malaysian Islamic banking efficiency. The scope of thestudy spans three years from 2001 to 2004. The study used non-parametric Data Envelopment Analysis (DEA) methodology approach as analysis tool to compare Malaysian Islamic banks scale efficiency and technical efficiency. The study finds that Malaysian Islamic banks scale efficiency outweighs pure technical efficiency. The finding suggests that Malaysian Islamic banks are under-performing. It was further established that domestic Islamic banks are more technically efficient compared to foreign Islamic banks in Malaysia. The study revealed that inefficiency of foreign Islamic banks in Malaysia was as a result of more focus on scale rather than pure technical efficiency during the period.

Sufian (2007a) expanded the scope of the study by including some risk factors into his analysis. The study revealed that Islamic banks scale inefficiency was as result of pure technical inefficiency witnessed in the Malaysian Islamic banking industry during the scope of study. It was further established that foreign Islamic banks have higher technical efficiency compared to domestic Islamic banks in Malaysia. Sufian (2007a) further proved that Malaysian Islamic banks in year 2002 declined in efficiency. However, the banks have slightly recovered in 2003 and 2004 consecutively. Based on the DEA method adopted in the study, it was established 22 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries that Malaysian domestic Islamic banks are more efficient compared to foreign Islamic banks in Malaysia, though with slight difference. The study proved that the cause of inefficiency in the Malaysian Islamic banks in general was as a result of wrong scale of operations.

In an empirical study conducted by Mokhtar, Abdullah & Alhabshi (2008) on Malaysian Islamic banks for the period of 1997 – 2003, the findings of the study show that the efficiency of Islamic bank industry in the country has increased during the study period. However, that of conventional banks remained stagnant over the same time period. As such, the study established that conventional banks are still more efficient compared to the Islamic banks.

Rashwan (2010) examined the efficiency and profitability of both Islamic and conventional banks. The study focused on the pre and post 2008 financial turmoil by assessing if there are any major differences in the performance of the two systems. The study adopted MANOVA methodology to analyse the financial secondary data for the sample banks in the region. The study found there are statistically significant differences between the performances of the two banking systems in 2007 and 2009, while no statistically significant differences were obtained in 2008. The study revealed that Islamic banks outperform conventional banks in 2007 and conventional banks perform better than Islamic banks in 2009.

Abdul-Majid, Saal & Battisti (2011) examined the efficiency of Malaysian commercial banks during the period of 1996 to 2002, and focuses on examining the impact of Islamic banking on its performance. The study revealed that Islamic banking is lesser in the input efficiency vis-à-vis cost efficiency. However, the full-fledged Islamic banks have higher productivity efficiency but this is not the same with conventional banks with Islamic window.

Apart from the studies reviewed above, nothing has been published that examined the efficiency of Islamic banks to assess their efficiency between when they were Islamic banking windows and when they transformed into full-fledged Islamic banks that are now full subsidiary of their parent banking group in Malaysia. In light of this knowledge gap, this study seeks to provide an empirical evidence to fill this apparent research gap vis-à-vis efficiency in the Malaysian Islamic banking industry.

2.2. Approaches to Measure Bank Efficiency

Generally, to establish the approach to assess the efficiency of banks, previous studies viewed banks from two main perspectives. The approaches are the intermediation and the production approach (Akhtar, 2010; Mohamad, Hassan, & Bader, 2008; Sufian, 2007b; Sufian & Haron, 2009).

The production approach on the one hand explains banking activities as production of services. This approach defines banks as manufacturer of loan to borrowers and deposit operators to depositors using capital and labour. This approach adopted traditional production factors, land, labour and capital as inputs used to generate desired outputs (Bader, et al., 2008). However, the production approach is said to be predominantly suitable for banks that involves in transaction of channelling the bulky deposits and money obtained from other financial organizations into Oladipupo Luqman Salami and Abideen Adeyemi Adewale loans and investments (Favero & Papi, 1995). Moreover, interest expenses is not inclusive in the summation of total costs under the production approach, thus only operating costs are considered and output is determined by the number of accounts serviced rather than monetary values (Hassan, Mohamad, & Bader, 2009).

The intermediation approach defines banks to be seen as a mediator of monetary transactions. As was recommended by (Sealey & Lindley, 1977) this approach presents a bank as an intermediary that takes deposits from customers using labour and capital. These deposits are considered as inputs to the banks and are lent out to other customers that want to borrow money in form of loans and advances which are considered as output to the banks. Hence, this approach assumes banks to be a mediator between depositors and loan takers (Hassan, et al., 2009). Intermediation approach is arguably, the most globally accepted approach used to measure bank efficiency (Kwan, 2003). Berger & Humphrey (1997) opined that thisis because it includes interest expenses (interest paid to depositors). The interest expenses, often amounted to the half of the total costs of the banking operating expenses (Hassan, et al., 2009).

Though, some studies mentioned that production approach is more suitable to measuring bank’s branches efficiency, reason been that at branch level customer documents are managed for the banks as a whole (Kwan, 2003). The intermediation approach is adopted by this study based on the following grounds: First, this study is assessing the whole banks efficiency and not branches. Secondly, the intermediation approach is widely adopted (Kwan 2003). Finally, the Islamic finance structure principle is based on profit sharing and asset-based financing where the parties involved in the transaction bear the losses or profits based on agreed ratio. These principles show importance of intermediary activities. Also other studies such as Hassan & Hussein (2003), Hasan (2005) and Sufian (2006) just to mention but a few have also used this approach to measure Islamic banking efficiency.

3. METHODOLOGY

3.1. Data

This study assesses bank efficiency of domestic Islamic banks in Malaysia with focus on five domestic Islamic banks out of eight as listed by the Bank Negara Malaysia (Bank Negara, 2012). They are: Affin Islamic Bank, RHB Islamic bank, Public Islamic Bank, Hong Leong Islamic Bank, and May Bank Islamic. These five banks started as Islamic window and has since transformed into full-fledged Islamic banks as subsidiary of their parent conventional banks. As such, data were collected from these companies’ annual reports covering the period from 2002-2011. As Islamic window, the banks report their Islamic banking operations under the note to the account. However, as a subsidiary, these banks operate as fully fledge Islamic banks and have their separate annual report different from their parent companies. Nearly all the banks have started to operate as full Islamic bank by 2008 (May Bank and Public Bank). Some started as early as 2005 – RHB Bank, Affin Bank and Hong Leong Bank started in 2006.

In spite of enormous literatures on bank efficiency, there is no format or agreed principles among scholars on what can be used as inputs and outputs variables in assessing efficiency 24 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries of banks. One study’s input variables might be used by other as output variables and vice versa (Mokhtar, et al., 2008). It is generally acceptable that the selections of variables in bank efficiency research drastically affect the outcome of the study (Sufian & Noor, 2009). This issue intensified due to the fact that choosing variables are usually made difficult by the scarcity of data on relevant variables. Aside that, measuring costs as in banking in particular is difficult. This is because most of the financial activities are mutually produced and prices are usually allocated to group of financial services (Sufian & Noor, 2009).

3.2. Input and Output Variables

To make choice of inputs and outputs variables, this study adopted the inputs and outputs variables as used by (Sufian & Noor, 2009). This study collected data on two input variables and three output variables. The output variables are: total loans denoted by (Y1), which consist of loans to customers and other banks; income denoted by (Y2) which consist of income derived from investment of deposits and other proceeds from other Islamic banking activities; and the last is the investments denoted by (Y3) which consist of investment securities held for trading, investment securities available for sale, and investment securities held to mature. The input variables on the other hand are; total deposits denoted by (X1) which consist of deposit from individual customers, organizations and other banks; and capital denoted by (X2).

3.3. Data Envelopment Analysis

Data Envelopment Analysis, DEA is used as the tool of analysis. This study adopted Fare, (Färe, Grosskopf, & Roos, 1998) approach to DEA to measure the efficiency of the banks, comparing their performance when they were operating as Islamic window and their performance as full- fledged Islamic banks. The Fare, et al., (1998) approach to DEA, employed output-oriented Malmquist Index (MI).

The MI is built using the DEA and this research used DEA software program developed by Coelli (1996) called DEAP version 2.1. The MI was chosen because of its desirable features that seem to be suitable for this research. This method does not require the normal norms such as profit optimization or cost reduction. This appears to be suitable for this research, because measuring cost minimization may prove difficult to measure efficiency. This is so given that the actual costs attributable to the banks when they are Islamic window are difficult to establish. For example, it would be difficult to measure other administrative expenses such as rental expenses, electricity bill, water rate and so on that is attributable to Islamic window.

Due to it features and merits, a lot of literatures have used the Malmquist productivity indexes in DEA. A few of those studies are Fare, Grosskopf, Norris & Zhang (1994) which used this model to analyse the productivity comparison between countries, while Tauer & Lordkipanidze (1999) and Koo & Mao (1996) used this approach to examine economic sectors such as agricultural sector. (Alam & Sickles, 1997) used this approach to examine productivity efficiency of airlines. Also (Calabrese, Campisi, & Mancuso, 2002) used this approach to examine efficiency of telecommunications industry, while (Mlima & Hjalmarsson, 2002) adopted this approach to measure production efficiency in banking and (Avkiran, 2001) used this approach to examine efficiency in the universities. Oladipupo OladipupoLuqman Salami Luqman and Salami Abideen and Adeyemi Abideen Adewale Adeyemi Adewale 18 18 variables are:varia totalbles loansare:Oladipupo total denoted loans Luqman by denoted (Y1), Salami which by and(Y1), consist Abideen which of Adeyemi consistloans to Adewaleof customers loans to customersand other andbanks; other income banks;18 denoted income denoted by (Y2) which consist of income derived from investment of deposits and other proceeds from other Islamic variables are: total byloans (Y2) denoted which by consist (Y1), ofwhich income consist derived of loans from to investment customers andof deposits other banks; and otherincome proceeds denoted from other Islamic banking activities; and the last is the investments denoted by (Y3) which consist of investment securities held for by (Y2) which consistbanking of incomeactivities; derived and the from last investmentis the investments of deposits denoted and by other (Y3) proceeds which consist from ofother investment Islamic securities held for trading, investmenttrading, investment securities availablesecurities foravailable sale, and for investmentsale, and investment securities heldsecurities to mature. held to The mature. input Thevariables input onvariables on banking theactivities; other andhand the are; last totalis the depositsinvestments denoted denoted by by(X1) (Y3) which which consistconsist ofof investmentdeposit from securities individual held forcustomers, trading, investmentthe securities other handavailable are; fortotal sale, deposits and investment denoted securitiesby (X1) heldwhich to mature.consist Theof depositinput variables from individual on customers, organizations and other banks; and capital denoted by (X2). the other hand are;organizations total deposits and otherdenoted banks; by and(X1) capital which denoted consist by (X2).of deposit from individual customers, organizations and other banks; and capital denoted by (X2). 3.3. Data3.3. Envelopment Data Envelopment Analysis Analysis

3.3. Data Envelopment Analysis Data Envelopment Analysis, DEA is used as the tool of analysis. This study adopted Fare, (Färe, Grosskopf, & Data Envelopment Analysis, DEA is used as the tool of analysis. This study adopted Fare, (Färe, Grosskopf, & Roos, 1998)Roos, approach 1998) toapproach DEA to to measure DEA to the measure efficiency the efficiencyof the banks, of the comparing banks, comparing their performance their performance when they when they Data Envelopmentwere operating Analysis, as Islamic DEA windowis used as and the their tool performanceof analysis. Thisas full study-fledged adopted Islamic Fare, banks. (Färe, The Grosskopf, Fare, et &al., (1998) Roos, 1998) approachwere to operating DEA to measureas Islamic the window efficiency and oftheir the performancebanks, comparing as full their-fledged performance Islamic banks. when Thethey Fare, et al., (1998) approach to DEA, employed output-oriented Malmquist Index (MI). were operating as Islamicapproach window to DEA, and employed their performance output-oriented as full Malmquist-fledged Islamic Index (MI).banks. The Fare, et al., (1998) approach to DEA, employed output-oriented Malmquist Index (MI). The MI is Thebuilt MI using is built the DEAusing andthe thisDEA research and this used research DEA used software DEA program software developed program developedby Coelli (1996)by Coelli called (1996) called DEAP version 2.1. The MI was chosen because of its desirable features that seem to be suitable for this research. 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This is This is This methodso given does that not therequire actual the costs normal attributable norms such to the as banks profit whenoptimization they are orIslamic cost reduction. window are This difficult appears to toestablish. be For suitable for this resesoarch, given because that the measuring actual costs cost attributable minimization to the may banks prove when difficult they areto measure Islamic windowefficiency. are This difficult is to establish. For example, it would be difficult to measure other administrative expenses such as rental expenses, electricity bill, so given that the actualexample, costs itattributable would be difficultto the banks to measure when they oth areer administrative Islamic window expenses are difficult such toas establish.rental expenses, For electricity bill, water rate waterand so rate on thatand sois attributableon that is attributable to Islamic towindow. Islamic window. example, it would be difficult to measure other administrative expenses such as rental expenses, electricity bill, water rate and so on that is attributable to Islamic window. Due to it features and merits, a lot of literatures have used the Malmquist productivity indexes in DEA. A few of Due to it features and merits, a lot of literatures have used the Malmquist productivity indexes in DEA. A few of those studies are Fare, Grosskopf, Norris & Zhang (1994) which used this model to analyse the productivity Due to it features andthose merits, studies a lot are of Fare, literatures Grosskopf, have used Norris the &Malmquist Zhang (1994) productivity which indexesused this in modelDEA. Ato fewanalyse of the productivity comparisoncomparison between countries, between countries,while Tauer while & Lordkipanidze Tauer & Lordkipanidze (1999) and (1999 Koo) & and Mao Koo (1996) & Mao used (1996) this approachused this approach those studiesto examine are Fare, economic Grosskopf, sectors Norris such &as Zhangagricultural (1994) sector. which (Alam used &this Sickles, model 1997) to analyse used thisthe approachproductivity to examine 19 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic comparison betweento countries,examine economic while Tauer sectors & Lordkipanidze such as agricultural (1999) sandector. Koo (Alam & Mao & Sickles, (1996) used1997) this used approach this approach to examine productivity efficiency of airlines. Also (Calabrese, Campisi, & Mancuso, 2002) used this approach to examine Windows and Full-Fledged Subsidiaries to examine economicproductivity sectors such efficiency as agricultural of airlines. sector. Also (Alam (Calabrese, & Sickles, Campisi, 1997) & used Mancuso, this approach 2002) used to examine this approach to examine t+1 t+1 t+1 efficiency of telecommunications industry, while (Mlima & Hjalmarsson, 2002) adopted this approach to measure Do (x , y ) productivity efficiencyefficiency of airlines. of telecommunications Also (Calabrese, industry,Campisi, while& Mancuso, (Mlima 2002)& Hjalmarsson, used this approach2002) adopted to examine this approach to measure Change in Efficiency = (3) production efficiency in bankingOladipupo and (Avkiran,Luqman Salami 2001) and Abideen used Adeyemi this approach Adewale to examine efficiency in the25 universities. t t t efficiency of telecommunicationsproduction efficiency industry, in whilebanking (Mlima and (Avkiran, & Hjalmarsson, 2001) used 2002) this adopted approach this to approach examine to efficiency measure in the universities. Do (x , y )

productionThe efficiency DEAThe approach DEA in banking approach used and in usedthis (Avkiran, research in this 2001) research expresses used expresses this the approachoutput the-based output-based to examine MI productivity efficiencyMI productivity between in the universities.between time periods (t) and The DEA approach used in this research expresses the output-based MI productivity between time periods (t) and ⎡ t t+1 t+1 t t t ⎤ (t + 1) whichtime periods can be mathematical(t) and (t + 1) whichexpressed can intobe mathematical two equations expressed as express into by two Fare equations et al (1989 as expresscited in Wahab and ⎛ Do (x , y ) ⎞ ⎛ Do (x , y ) ⎞ The DEA approach(t used + 1) inwhich this researchcan be mathematical expresses the expressed output-based into twoMI productivityequations as between express bytime Fare periods et al (1989(t) and cited in Wahab and Technological Change = ⎜ ⎟ ⎜ ⎟ (4) Rahman,by 2012 Fare ). et al (1989 cited in Wahab and Rahman, 2012 ). ⎢⎜ t+1 t+1 t+1 ⎟ ⎜ t+1 t t ⎟⎥ (t + 1) which can beRahman, mathematical 2012 ). expressed into two equations as express by Fare et al (1989 cited in Wahab and ⎣⎝ Do (x , y )⎠ ⎝ Do (x , y )⎠⎦

Rahman, 2012 ). 1 (1) t t+1 t+1 t+1 t+1 t+1 2 1 t t+1 t+1 t+1 t+1 t+1 2 t t t+1 t+1 ⎛ Do (x ,⎛y ) Do (x , y )⎞ ⎞ Equation (3) examines the level at which the production procedure changes inputs into outputs (to meet up to the t t t+1 t+1 Do (x , y 1) Do (x , y ) M o (x , y , x , y )= ⎜ t t t t+1 t t ⎟ M o (x , yt , xt+1⎜ , yt+1 )=t+⎜1 t+1 t t+t1 t2 t+1 ⎟ t t ⎟ frontier) and equation (4) examines the development in technology. Improvement in the productivity yield in MI ⎛ D x , y DoD(x⎜, yx ) D, yo (x⎞, y ) ⎟ t t t+1 t+1 o ( ⎝ ) o⎝ ( Do (x ,)y ) Do (⎠x , y ) ⎠ (1) is represented by values that is more than 1 and if performance is declining over the year, it is represented by M o (x , y , x , y )= ⎜ ⎟ (1) ⎜ t t t t+1 t t ⎟ ⎝ Do (x , y ) Do (x , y ) ⎠ values less than 1 (Färe, et al., 1994). This explanation is used to explain the values of each of the element of the t t+1 t+1 (1) From equation (1) above, t , denotet+1 t+ 1the distance between the period (t +1) inspection to the period t general Total Factor Productivity (TFP) directory. If the value of MI is greater than 1, then it is considered to be FromFrom equation equation (1) (1)D above,o (above,x , y D ) x , y ,, denote denote the the distance distance between between the the period period (t +1) (t + inspection 1) to the period t o ( ) efficient indicating improvement in the productivity. Also if the value of the technological change element is technology, where xt andt+1 y representt+1 input and output in that order. Equation (1) can be re-written following (Färe, From equation (1)inspection technology,above, D too ( xthe where ,periody x) and, tdenote technology, y represent the distance where input andbetweenx and output y representthe in periodthat order.input (t +1) Equationand inspection output (1) in can tothat the be order. periodre-written t following (Färe, more than 1, it is referred to as efficient in the technology advancement. et al., 1998)et asal., follows: 1998) as follows: technology, whereEquation x and y (1) represent can be inputre-written and output following in that (Färe, order. et al.,Equation 1998) (1)as follows:can be re-written following1 (Färe, t+1 t+1 t+1 t t+1 t+1 t t t 2 1 et al., 1998) as follows: t+1 t⎡+1 t+1 t t+1 t+1 t⎤ t t 2 From equation (4), distance is measured for each operative in each pair of duration by means of mathematical t t t+1 t+1 Do (x , y ) ⎛ Do (x ⎡, y ) ⎞ ⎛ Do (x , y ) ⎞ ⎤ t t t+1 t+1 Do (x , y ) ⎛ Do (x , y ) 1⎞ ⎛ D(2)o (x , y ) ⎞ M o (x , y , x , y )= t t t × ⎢⎜ t+1 t+1 t+1 ⎟ ⎜ t+1 t t ⎟⎥ programming method. DEA is based on the assumption that k = 1, where k are organizations (in this case banks) M o (x ,t+y1 , xt+1 , yt+1 )= tt tt+⎜1 t t+1 × ⎢⎜ t+t 1 ⎟t t⎜+1 t t+1 2⎟ ⎜ t⎟+1 t t ⎟⎥ D x , yDo (x ,⎡y⎛ )D x⎢⎝ ,Dyo (x⎞ ⎛⎜, yD )x⎠ ,⎝yDo ⎞(⎤x⎟,⎜y )⎠⎥ ⎟ t t t t t+1 t+1 o ( ) Doo((x⎣, y ) ) ⎣⎢⎝ Doo ((x , y) )⎠ ⎝ Do ⎦(x , y )⎠⎦⎥ (2) M o (x , y , x , y )= × ⎢⎜ ⎟ ⎜ ⎟⎥ (2) that produce m = 1. M denote outputs yk ,m , whereas N=1, where N denote inputs xk,n , at every time period t=1, T. Dt xt , yt ⎜ Dt+1 xt+1, y t+1 ⎟ ⎜ Dt+1 xt , yt ⎟ o ( ) ⎣⎢⎝ o ( )⎠ ⎝ o ( )⎠⎦⎥ (2) In equation (2) the first part of the ratio before multiplication measures the changes in relation to efficiency (that In equation (2) the first part of the ratio before multiplication measures the changes in relation to efficiency (that In DEA, the indication of technology through Constant Returns to Scale (CRS) at every time period t from the is the levelis of the production level of production changes year changes on year) year between on year) year’s between t and year’s t + 1. t Theand tother + 1. Thepart otherof the part right of hand the rightside ofhand side of In equation (2) theIn equationfirst part (2)of thethe ratiofirst beforepart of multiplicationthe ratio before measures multiplication the changes measures in relation the changes to efficiency in relation (that data can be expressed as: the equationthe stand equation for arithmetic stand for arithmeticmean of the mean two ofratios. the two It measures ratios. It themeasures technological the technological changes (that changes is, changes (that is, in changes in is the level of production changes year on year) between year’s t and t + 1. Thet othert+ part1 of the right hand side of to efficiency (that is the level of production changes year on year) betweent year’st+1 t and t + 1. the equationthe frontierstand forthe function arithmeticfrontier itself) function mean between ofitself) the the twobetween periods ratios. the underIt periodsmeasures examination under the technologicalexamination x and x xchanges. Thatand xis: (that . That is, is:changes in k The other part of the right hand side of the equation stand for arithmetic mean of the two ratios. t ⎡ t t t t t ⎤ t t+1 G = x , y : y ≤ z y m =1,...., M , the frontier functionIt measures itself) between the technological the periods changes under examination (that is, changes x and inx the .frontier That is: function itself) between ⎢( ) m ∑ k k,m ⎥ ⎣ k=1 ⎦ t t+1 19 the periodsMalaysian under Islamic examination Banks’ Efficiency: x and x An. IntraThat- Bankis: Comparative Analysis of Islamic k Windows and Full-Fledged Subsidiaries t t t ∑ zk xk,n ≤xn =1,..., N, t+1 t+1 t+1 (3) k=1 Do (x , y ) ChangeChange in Efficiency == (3) t t t t zk ≥ 0 k=1…... K (5) Do (x , y )

In equation (5) – zt stand for the weight on each exact cross-sectional examination. Using (Afriat, 1972) theory, ⎡ t t+1 t+1 t t t ⎤ (4) k ⎛ Do (x , y ) ⎞ ⎛ Do (x , y ) ⎞ Technological Change = ⎜ ⎟ ⎜ ⎟ (4) to allow the Variable Returns to Scales (VRS), CRS have to be relaxed by adding the following constraint: Technological Change = ⎢⎜ t+1 t+1 t+1 ⎟ ⎜ t+1 t t ⎟⎥ ⎣⎝ Do (x , y )⎠ ⎝ Do (x , y )⎠⎦ k t (VRS) (6) Equation (3) examines the level at which the production procedure changes inputs into outputs (to meet up to the ∑ zk =1 frontier) and equationEquation (4) (3) examines examines the the development level at which in technology. the production Improvement procedure in the changes productivity inputs yield into in MI k=1 is representedoutputs by values (to meetthat isup more to the than frontier) 1 and and if performanceequation (4) examinesis declining the over development the year, init technology.is represented by values less thanImprovement 1 (Färe, et al.,in the 1994) productivity. This explanation yield in MIis used is represented to explain bythe values values thatof each is more of the than element 1 and of the This research adopted Fare et al (1994) which spelled out the breakdown of the MI. The study categorized MI general Total ifFactor performance Productivity is declining (TFP) directory. over the year,If the it value is represented of MI is greater by values than less 1, thenthan it1 is(Färe, considered et al., to be into the efficiency change component and scale efficiency change component. The efficiency change component efficient indicating1994). improvementThis explanation in theis used productivity. to explain Alsothe values if the of value each of thethe elementtechnological of the generalchange Totalelement is measures CRS technology in relation to pure efficiency component. The scale efficiency change component on more than 1, itFactor is referred Productivity to as efficient (TFP) indirectory. the technology If the value advancement. of MI is greater than 1, then it is considered to the other hand measures the variation in the difference connecting the VRS and CRS technology.

be efficient indicating improvement in the productivity. Also if the value of the technological Pure efficiency element calculates the extent to which the banks can change inputs into outputs and the scale From equationchange (4), distance element is is measuredmore than for 1, iteach is referred operative to asin efficienteach pair in of the duration technology by means advancement. of mathematical programming method. DEA is based on the assumption that k = 1, where k are organizations (in this case banks) efficiency on the other side assesses the degree at which the banks can obtain benefit from returns to scale by t t increasing or decreasing in size towards achieving the best possible scale. that produce mFrom = 1. equationM denote (4), outputs distanceyk ,m is, whereas measured N=1, for whereeach operative N denote in inputs each xpairk,n , ofat everyduration time by period means t=1, T. of mathematical programming method. DEA is based on the assumption that k = 1, where k Therefore, under DEA approach to compose the MI productivity of any organization k' between t and t + 1, these t In DEA, the indicationare organizations of technology (in this casethrough banks) Constant that produce Returns m to= 1.Scale M denote (CRS) outputs at every y k,mtime, whereas period N=1, t from the distance functions have to be measured: t data can be expressedwhere N as:denote inputs x k,n, at every time period t=1, T. t t t t+1 t t t t+1 t+1 t+1 t+1 t+1 Do (x , y ), Do (x , y ), Do (x , y ), Do (x , y ) ⎡ k ⎤ G t t t t t t = ⎢(x , y ): ym ≤ ∑ zk yk,m ⎥m =1,...., M , ⎣ k=1 ⎦ k t t t ∑ zk xk,n ≤xn =1,..., N, k=1 t zk ≥ 0 k=1…... K (5)

t In equation (5) – zk stand for the weight on each exact cross-sectional examination. Using (Afriat, 1972) theory, to allow the Variable Returns to Scales (VRS), CRS have to be relaxed by adding the following constraint:

k t (VRS) (6) ∑ zk =1 k=1

This research adopted Fare et al (1994) which spelled out the breakdown of the MI. The study categorized MI into the efficiency change component and scale efficiency change component. The efficiency change component measures CRS technology in relation to pure efficiency component. The scale efficiency change component on the other hand measures the variation in the difference connecting the VRS and CRS technology.

Pure efficiency element calculates the extent to which the banks can change inputs into outputs and the scale efficiency on the other side assesses the degree at which the banks can obtain benefit from returns to scale by increasing or decreasing in size towards achieving the best possible scale.

Therefore, under DEA approach to compose the MI productivity of any organization k' between t and t + 1, these distance functions have to be measured:

t t t t+1 t t t t+1 t+1 t+1 t+1 t+1 Do (x , y ), Do (x , y ), Do (x , y ), Do (x , y )

19 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries 19 Malaysian Islamic Banks’ Efficiency: An IntraDt+-Bank1 xt+ 1Comparative, yt+1 Analysis of Islamic Windows and Full-Fledgedo ( Subsidiaries) Change in Efficiency = t t t (3) tD+1o (xt+,1 y t)+1 Do (x , y ) Change in Efficiency = t t t (3) Do (x , y ) 19 Malaysian Islamic Banks’ Efficiency:⎡⎛ DAnt Intraxt+1,-Bankyt+1 Comparative⎞ ⎛ Dt xt , yAnalysist ⎞⎤ of Islamic Technological Change = ⎜ o ( ) ⎟ ⎜ o ( ) ⎟ (4) Windows and⎢⎜ Fullt+1-Fledgedt+1 t +Subsidiaries1 ⎟ ⎜ t+1 t t ⎟⎥ t t+1 t+1 t t t ⎡⎝ Do (x t+,1y t+)1⎠ ⎝t+D1 o (x , y )⎠⎤ ⎣⎛ Do (x , y ) ⎞ ⎛ Do (x , y ) ⎞⎦ Technological Change = Do (x , y ) (4) ⎢⎜ t+1 t+1 t+1 ⎟ ⎜ t+1 t t ⎟⎥ Change in Efficiency⎜ = t t ⎟ t⎜ ⎟ (3) Do (x , y ) Do (x , y ) Equation (3) examines the level at which the production⎣⎝ procedureDo (x ,changes⎠y⎝) inputs into⎠⎦ outputs (to meet up to the frontier) and equation (4) examines the development in technology. Improvement in the productivity yield in MI Equation (3) examines the level at which the production procedure changes inputs into outputs (to meet up to the is represented by values that is more than 1 and if⎡ ⎛performanceDt xt+1, yt+ 1is declining⎞ ⎛ Dt x tover, yt the⎞⎤ year, it is represented by valuesfrontier) less and than equation 1 (Färe, (4) etTechnological examines al., 1994) the. This Changedevelopment explanation = ⎜ in is otechnology. (used to explain) ⎟Improvement⎜ theo ( values )in ⎟of the each productivity of the element yield( 4)ofin theMI ⎢⎜ t+1 t+1 t+1 ⎟ ⎜ t+1 t t ⎟⎥ generalis represented Total Factor by values Productivity that is more (TFP) than directory. 1 and if⎣If⎝ performancetheDo value(x ,ofy MIis) ⎠declining is⎝ greaterDo (x over ,thany ) the⎠1,⎦ thenyear, it itis isconsidered represented to beby efficientvalues less indicating than 1 (Färe, improvement et al., 1994) in .the This productivity. explanation Also is used if theto explain value ofthe the values technological of each of changethe element element of the is moregeneralEquation tha Totaln 1,(3) it Factorexamines is referred Productivity the to levelas efficient at(TFP) which in directory. thethe technologyproduction If the valueadvancement.procedure of MI changes is greater inputs than into 1, then outputs it is (toconsidered meet up to bethe efficientfrontier) indicatingand equation improvement (4) examines in the developmentproductivity. inAlso technology. if the value Improvement of the technological in the productivity change elementyield in MIis Frommoreis represented thaequationn 1, it is(4),by referred valuesdistance tothat asis isefficientmeasured more thanin forthe 1 each technologyand operativeif performance advancement. in each is pairdeclining of duration over the by year,means it ofis representedmathematical by programmingvalues less than method. 1 (Färe, DEA et al.,is based 1994) on. This the explanationassumption thatis used k = to1, explainwhere kthe are values organizations of each of(in the this element case banks) of the Fromgeneral equation Total26 Factor(4), distance Productivity is measured Malaysian(TFP)t directory.for Islamic each Banks’ operativeIf Efficiency: the value inAn Intra-Bankofeach MI pair is Comparativegreater of duration than tAnalysis 1,by thenof meansIslamic it is ofconsidered mathematical to be that produce m = 1. M denote outputs yk ,m , whereas N=1, where N denote inputs xk,n , at every time period t=1, T. programmingefficient indicating method. improvement DEA is based in onthe the productivity. assumptionWindows Alsothat and Full-Fledged kif = the 1, wherevalue Subsidiaries kof are the orga technologicalnizations (in change this case element banks) is thatmore produce than 1, m it =is 1. referred M denote to as outputs efficientyt in, thewhereas technology N=1, where advancement. N denote inputs xt , at every time period t=1, T. In DEA, the indicationIn DEA, the of indicationtechnology of through ktechnology,m Constant through Returns Constant to Scale Returns (CRS) to Scale atk, nevery (CRS) time at periodevery timet from the data can be expressedperiod t fromas: the data can be expressed as: From equation (4), distance is measured for each operative in each pair of duration by means of mathematical In DEA, the indication of technology through Constant Returns to Scale (CRS) at every time period t from the dataprogramming can be expressed method. as: DEA is based on the assumption thatk k = 1, where k are organizations (in (5)this case banks) t⎡ ⎤ t that produce m = 1. M denote outputsG t y , twhereast t N=1, wheret t N denote inputs x , at every time period t=1, T. = k⎢,m(x , y ): ym ≤ ∑ zk yk,m ⎥m =1,...., M , k,n ⎣ k=k1 ⎦ t ⎡ t t t t t ⎤ Gk = x , y : y ≤ z y m =1,...., M , In DEA, the indication of technology throught ⎢(t tConstant) m ∑ Returnsk k,m to⎥ Scale (CRS) at every time period t from the z ⎣x ≤x =1,..., Nk=,1 ⎦ data can be expressed as: ∑ k k,n n k=k1 t t t t zk xk,n ≤xn =1,..., N, z∑k ≥ 0 k=1…... K k (5) k=1 ⎡ ⎤ G t xt , yt : yt ≤ z t yt m =1,...., M , z t ≥= 0⎢ ( k=1…...) mK ∑ k k ,m ⎥ (5) t k ⎣ k=1 ⎦ In equation (5) – zk stand for thet weight on each exact cross-sectional examination. Using (Afriat, 1972) theory, In equation (5) – Z k standk for the weight on each exact cross-sectional examination. Using to allow the Variablet Returns to Scales (VRS),t t CRSt have to be relaxed by adding the following constraint: In equation (5)(Afriat, – stand1972) for theory, the weight to allowzk onxk , theneach≤ xVariablen exact=1,..., cross ReturnsN, -sectional to Scales examination. (VRS), CRS Using have (Afriat, to be relaxed1972) theory, zk ∑ by adding the followingk =constraint:1 to allow the Variable Returns to Scalest (VRS), CRSk have to be relaxed by adding the following constraint: k=1…... Kt (5) zk ≥ 0 z =1 (VRS) (6) ∑ k (6) k=k1 t z t =1 (VRS)(VRS) (6) In equation (5) – zk stand for the weight on each∑ exactk cross-sectional examination. Using (Afriat, 1972) theory, This research adopted Fare et al (1994) which spelledk=1 out the breakdown of the MI. The study categorized MI to allow the Variable Returns to Scales (VRS), CRS have to be relaxed by adding the following constraint: into the efficiency change component and scale efficiency change component. The efficiency change component measuresThis research CRS adopted technology Fare in et relation al (1994) to whichpure efficiency spelled out component. the breakdown The scale of the efficiency MI. The changestudy categorizedcomponent MIon This research adopted Fare et al (1994)k which spelled out the breakdown of the MI. The study theinto other the efficiency hand measures change the component variation in and the scale difference efficiency connectingt change the component. VRS and CRSThe efficiencytechnology. change component categorized MI into the efficiency change zk component=1 (VRS) and scale efficiency change component. (6) measures CRS technology in relation to pure efficiency∑ component. The scale efficiency change component on The efficiency change componentk= 1 measures CRS technology in relation to pure efficiency Purethe other efficiency hand measures element thecalculates variation the in extentthe difference to which connecting the banks the can VRS change and CRSinputs technology. into outputs and the scale component. The scale efficiency change component on the other hand measures the variation efficiencyThis research on the adopted other sideFare assesseset al (1994) the degreewhich spelledat which out the the banks breakdown can obtain of the benefit MI. Thefrom study returns categorized to scale byMI in the difference connecting the VRS and CRS technology. increasingPureinto theefficiency efficiency or decreasing element change incalculates sizecomponent towards the andextentachieving scale to efficiency whichthe best the possible changebanks scale.cancomponent. change inputsThe efficiency into outputs change and component the scale efficiencymeasures onCRS the technology other side inassesses relation the to degreepure efficiency at which component.the banks can The obtain scale benefitefficiency from change returns component to scale by on Therefore,increasingthe other handorunder Puredecreasing measures DEA efficiency approach in the size variationelement towards to compose calculatesin achieving the differencethe theMI the extentproductivity best connecting topossible which of the scale.theany VRS banksorganizat and can CRS ionchange k'technology. between inputs into t and outputs t + 1, these distance functionsand the have scale to be efficiency measured: on the other side assesses the degree at which the banks can obtain Therefore,Pure efficiency underbenefit elementDEA from approach calculates returns to tocompose the scale extent by the increasingto MI which productivity the or banksdecreasing of anycan organizatchangein size towardsinputsion k' intobetween achieving outputs t and the and t +best the1, thesescale distance functionspossible have scale. to be measured:t t t t+1 t t t t+1 t+1 t+1 t+1 t+1 efficiency on the other side assessesDo (x , ythe), degreeDo (x at, y which), Do the(x banks, y )can, D oobtain(x ,benefity ) from returns to scale by increasing or decreasing in size towards achieving the best possible scale. t t t t+1 t t t t+1 t+1 t+1 t+1 t+1 Therefore, under DEADo (x approach, y ), D oto (composex , y ), Dtheo ( MIx productivity, y ), Do of(x any, y organization) k' between Therefore, undert and DEAt + 1, approachthese distance to compose functions the haveMI productivity to be measured: of any organization k' between t and t + 1, these distance functions have to be measured:

t t t t+1 t t t t+1 t+1 t+1 t+1 t+1 Do (x , y ), Do (x , y ), Do (x , y ), Do (x , y )

These four distanceOladipupo functions Luqman can Salami be equated and Abideen with Farrell’sAdeyemi Adewale output-based that measure 20 technical efficiency. In DEA, Farrell’s output-based measure the technical efficiency for every These four organizationdistance functions k' =1,…., can wherebe equated k, can with be statedFarrell’s as: output -based that measure technical efficiency. In DEA, Farrell’s output -based measure the technical efficiency for every organization k' =1,…., where k, can be stated as: t t t −1 'k' (7) [Do (xk', yk' )] = maxλ (7)

Subject to k k ' t+1 t t λ yk ,m ≤ ∑ zk yk ,m m =1,...,M , k =1 k zt xt ≤ xt+1 =1,..., N, ∑ k k,n k',n k =1 k t (VRS) ∑ zk =1 k=1 t zk ≥ 0 k =1,..., K. (8)

t+1 t+1 t+1 The calculation of Do (x , y ) is the same as Equation (8), anywhere t + 1 is replaced by t. To calculate the MI, it also requires the construction of two mixed-distance functions that is calculated by evaluating the observation in one time period with the best practice frontier of another time period. The opposite of the mixed-distance function for observation k' can be obtained from:

t t t −1 'k' [Do (xk', yk' )] = maxλ (9) Subject to k k ' t+1 t t λ yk ,m ≤ ∑ zk yk ,m m =1,...,M , k =1 k zt xt ≤ xt+1 =1,..., N, (10) ∑ k k,n k',n k =1 k t =1 (VRS) ∑ zk k =1 t zk ≥ 0 k =1,..., K.

Therefore, in order to calculate change in scale efficiency under the VRS technology, the contrary output distance functions must also be measured merging equation (6) into the restrictions in equations (8) and (10). Technical change can be measured in relation to the CRS technology. Scale efficiency change in every time period is measured in percentage of the distance function fulfilling CRS in relation to the distance function in VRS. Pure efficiency change on the other side measures the percentage of the own-period distance function in each period in VRS. These two distance functions will break down equation (2) to become:

1 ⎛ Dt+1 xt , yt ⎞⎛ Dt+1 xt+1, yt+1 ⎞ 2 ⎛ Dt xt , yt ⎞ M xt , yt , xt+1, yt+1 = ⎜ o ( )⎟⎜ o ( )⎟ ×⎜ o ( ) ⎟ o ( ) ⎜ t t t ⎟⎜ t t+1 t+1 ⎟ ⎜ t+1 t+1 t+1 ⎟ ⎝ Do (x , y ) ⎠⎝ Do (x , y ) ⎠ ⎝ Do (x , y )⎠ 1 ⎛ Dt+1 xt , yt Dt+1 xt+1, yt+1 Dt xt , yt Dt xt+1, yt+1 ⎞ 2 ×⎜ oc ( ) o ( ) oc ( ) o ( )⎟ (11) ⎜ t+1 t t t+1 t+1 t+1 t t t t t+1 t+1 ⎟ ⎝ Do (x , y ) Doc (x , y )Do (x , y )Doc (x , y )⎠

Where

Oladipupo Luqman Salami and Abideen Adeyemi Adewale 20

These four distance functions can be equatedOladipupo with Farrell’s Luqman outputSalami- basedand Abideen that measure Adeyemi technicalAdewale efficiency. In 20 DEA, Farrell’s output-based measure the technical efficiency for every organization k' =1,…., where k, can be These four distance functions can be equated with Farrell’s output-based that measure technical efficiency. In stated as: OladipupoOladipupo Luqman Luqman Salami Salami and Abideen and Abideen Adeyemi Adewale Adeyemi Adewale 27 20 DEA, Farrell’s output-based measure the technical efficiency for every organization k' =1,…., where k, can be Oladipupo Luqmant Salamit t and−1 Abideen Adeyemi'k' Adewale 20 These fourstated distance as: functions can be equated[Do ( xwithk', y kFarrell’s' )] = max outputλ -based that measure technical efficiency. (7) In Subject to TheseDEA, four Farrell’s distance output functions-based can measure be equated the technical with Farrell’s efficiency output for- basedevery−1 thatorganization measure ktechnical' =1,…., efficiency.where k, can In be t t t 'k' (7) DEA,statedSubject Farrell’s as: to output-based measure the technical efficiency[Do (x fork', yeveryk' )] =organizationmaxλ k' =1,…., where k, can be stated as: t t tk −1 'k' k ' Dot+1xk', yk' t =t maxλ (7) Subject to [ ( −1)] (8) λ t ykt ,m ≤t ∑ zk yk ,m m'k' =1,...,M , [Do (xk', yk' )]k =1 = maxλ k (7) k ' t+1 t t Subject to k λ y ≤ z y m =1,...,M , t t t+1 k ,m ∑ k k ,m z x ≤ xk =1,...,kN=1 , Subject to ∑ kk' tk+,1n k',n t t k =1λ y ≤k k z y m =1,...,M , k ,m ∑ t k kt ,m t+1 k ' t+1 k =t1z t x ≤ x =1,..., N, λ ytk ,m ≤ ∑zk yk k ,m k ,mn =1,...,k',n M , kz =1 ∑ (VRS)k =1 ∑ k t tk =1 t+1 kk=1 z x k≤ x =1,..., N, ∑ k k,n tk',n t t t t+1 k =1 zk =1 (VRS) zk ≥zk0 x k , ≤k∑x=k',1n,...,=1K,...,. N, (8) ∑ k n k=1 k =1 t t k zk =1 (VRS) t+1 t+1 t+1 ∑ zk ≥ 0 k =1,..., K. (8) k=t1 The calculation of Do (x , y ) is the same zas =Equation1 (VRS) (8), anywhere t + 1 is replaced by t. To calculate the ∑ tk k=1 MI, it also requires the constructiont+1 t+of1 twot+1z kmixed≥ 0 - distance k =1,..., functionsK. that is calculated by evaluating (8) the TheThe calculationcalculation ofof D o ( x , y t ) is is thethe samesame asas EquationEquation (8),(8), anywhereanywhere tt + 1 is replaced by t. To calculate the observation in one time period with the bestzk ≥ practice0 k frontier=1,..., Kof. another time period. The opposite (8) of the tMI,. To itcalculate alsot +1requires thet+1 MI, t+the1 it alsoconstruction requires theof constructiontwo mixed-distance of two mixed-distancefunctions that functionsis calculated that by evaluating the mixedThe -calculationdistance function of D forx observation, y is thek' can same be asobtained Equation from: (8), anywhere t + 1 is replaced by t. To calculate the isobservation calculatedo inby( one evaluating time) period the observation with the best in onepractice time periodfrontier with of anothether best time practice period. frontier The opposite of the The calculation of Dt+1 xt+1, yt+1 is the same as Equation (8), anywhere t + 1 is replaced by t. To calculate the MI, it alsoofmixed requiresanother-distanceo time(the period.functionconstruction) The for observationoppositeof two ofmixed thek'−1 can -mixed-distancedistance be obtained functions from: function that foris calculatedobservation by k' canevaluating be the t t t 'k' (9) MI,observation it also requiresobtained in one the from:time construction period with of the[ Dtwoo (bestx kmixed', ypracticek' )]-distance= maxfrontier λfunctions of ano therthat timeis calculated period. byThe evaluating opposite ofthe the observationmixedSubject-distance in to one function time period for observation with the best k' can practice be obtained frontier from: of− 1 ano ther time period. The opposite of the t t t 'k' (9) mixed -distance function for observation k' can be obtained[Dk o ( xfrom:k', yk ' ) ] = maxλ k ' t+1 t t Subject to −1 (9) λ t ykt,m ≤t ∑ zk yk ,m 'k' m =1,...,M , Do xk', yk' = maxλ (9) [ ( −1k)=]1 k t t t k ' t+1 'k' t t Subject to Subject to Do kxk', yk' =λmaxy λ≤ z y m = 1,...,M , (9) [ ( t t)] t+1 k ,m ∑ k k ,m z x ≤ xk =1,...,k =N1 , (10) Subject to ∑ kk' tk+,1n k',n t t k =1λ y ≤k k z y m =1,...,M , k ,m ∑ kt kt,m t+1 k ' t+1 k =t1 zt x ≤ x =1,..., N, (10) λ ykt ,m ≤ ∑zk ykk,m k , n km',n =1,...,M , (10) kz =1 ∑ k (VRS)=1 ∑ k t kt =1 t+1 kk=1 z x ≤k x =1,..., N, (10) ∑ k k,n kt',n t t t t+1 k =1 zk =1 (VRS) zk ≥zk0x kk, =≤1∑,...,xk',nK=.1 ,..., N, (10) ∑ k n k =1 k =1 t t k zk =1 (VRS) ∑ zk ≥ 0 k =1,..., K. Therefore, in order to calculate change in scalekz=t1 =1 efficiency (VRS) under the VRS technology, the contrary output distance functions must also be measured ∑mergingtk equation (6) into the restrictions in equations (8) and (10). k =1zk ≥ 0 k =1,..., K. Technical changeTherefore, can bein measuredorder to calculatein relationt change to the inCRS scale technology. efficiency Scale under efficiency the VRS change technology, in every the time contrary output z ≥ 0 k =1,..., K. period is measureddistance in functions percentage must of thealso distance bek measured function merging fulfilling equation CRS in(6) relation into the to restrictions the distance in functionequations in (8) and (10). Therefore, in order to calculate change in scale efficiency under the VRS technology, the contrary output VRS. Pure efficiencyTechnical change change on can the be other measured side measures in relation the to percentage the CRS oftechnology. the own- periodScale efficiencydistance function change inin every time distance functions must also be measured merging equation (6) into the restrictions in equations (8) and (10). Therefore,each period in inperiod orderVRS. is to These measured calculate two distance inchange percentage functionsin scale of the willefficiency distance break down underfunction equatio the fulfillingVRSn (2) technology, to CRS become: in relation the contrary to the distanceoutput function in Technical Therefore,change can in be order measured to calculate in relation change to the in scaleCRS efficiencytechnology. Scaleunder efficiency the VRS change technology, in every the time distance functionsVRS. mustPure efficiencyalso be measured change mergingon the other equation side measures(6) into the the restrictionspercentage inof equationsthe own-period (8) and distance (10). function in period is measured in percentage of the distance function fulfilling CRS in relation to the distance function in Technical changecontraryeach periodcan outputbe in measured VRS. distance These in functionsrelation two distance to must the functions CRS also technology. be will measured break Scale down merging efficiency equatio equationn (2)change to (6) become: in into every the time periodVRS. is Puremeasuredrestrictions efficiency in percentage inchange equations on of the the (8) other distance and side(10). functionmeasures Technical fulfilling the change percentage CRS can1 in beof relation measuredthe own to- periodthe in relationdistance distance tofunction thefunction in in t+1 t t t+1 t+1 t+1 2 t t t VRS.each Pure period efficiencyCRS in VRS. technology. change t Theset t on+twoScale1 the tdistance+1 efficiencyother⎛ D side functions x measureschange, y will⎞⎛ inD thebreak every percentagex down time, y equatioperiod⎞ of the⎛ nis (2) ownDmeasured tox- periodbecome:, y in distance ⎞ percentage function of in M x , y , x , y = ⎜ o ( )⎟⎜ o ( )⎟ ×⎜ o ( ) ⎟ o ( ) t t t t t+1 t+1 t+1 t+1 t+1 each period inthe VRS. distance These function two distance fulfilling ⎜functions CRS in will relation⎟ break⎜ to down the distance equatio⎟ nfunction ⎜(2) to become: in 2 VRS. Pure⎟ efficiency ⎝ Do (x , y ) ⎠⎝ t+D1 o (tx t, y ) t⎠+1 t⎝+1Do t+(1x , y )⎠ t t t t t t+1 t+1 ⎛ Do (x , y )⎞⎛ Do (x , y )⎞ ⎛ Do (x , y ) ⎞ change on the otherM sidex , ymeasures, x , y the =percentage⎜ of the⎟⎜ own-period distance1⎟ ×⎜ function in each⎟ ot+(1 t t t+1 )t+1 ⎜ t+1 t tt t ⎟t⎜ t t t+t1+1 t+t1+1 2⎟ ⎜ t+1 t+1 t+1 ⎟ ⎛ t+1 t t D x t,+1y t+1 Dt+1 x 2 , y ⎞ t t Dt x , y period in VRS. TheseDoc (twox , y distance)⎛DDo ( xfunctionsx ,,⎝yy ⎞o)⎛D (willDoc (x breakx,)y⎠⎝,) Dydowno o(x(⎞ equation, y⎛ )D) ⎠(2)x to,⎝y become:o (⎞ )⎠ t ×t⎜ t+1 t+1 o ( ) o ( 1) ⎟ o ( ) (11) M x , y ⎜, x t+1, y t =t ⎜t+1t+1 t t+1t t+1⎟⎜t+1t tt+1 t t+1 t 2 t⎟+1 ×⎜t+1 t ⎟ t t ⎟ 1 o ( D x , y) ⎜Dt+1t xtt , tyt ⎟⎜tD+1 xtt+1, ty+1 t+D1 t+1 tx⎟ ,t y⎜ t t+1t t+1 t+1 ⎟ 2 t t ⎝t+1 o t+(1 ⎛ D) o ocD(x(x, y, y)⎞⎛ D)o Do((x x, y,)y oc)⎞( ⎛ DDo)(⎠x x, y ), y ⎞ ⎛⎝Doc o((x , y ))D⎠⎝o (xo ( , y )D)oc⎠(x , ⎝y )Do o (x , y )⎞⎠ M o ( x , y , x , y ) = ⎜ t t t ⎟⎜ t t+1 t+1 ⎟ ×⎜ t+1 t+1 t+1 ⎟ (11) ×⎜ ⎜ t+1 t t⎟⎜ t+1 t+1 t+1 ⎟ t ⎜t t t 1t+1 t+⎟1 ⎟ t+1 t ⎜Dt o (x t,+1y )t+1 Dt+1o (x t , yt )t t t+D1 o t(+x1 2, y ) ⎟ ⎛ ⎝ ⎝ Do (x , y⎠)⎝Doc (x , y )D⎠o (x⎝, y )Doc (⎞x , y ⎠ )⎠ Where Doc (x , y ) Do (x , y )Doc (x , y )Do (x , y 1) ×⎜ 2 ⎟ (11) ⎜t+1 t+1t tt t t+1 t+1t+1 t+1t+1 t+1t t t t t t t t t+1 t+1t+1 t+1 ⎟ ⎛ Doc (x , y ) Do (x , y )Doc (x , y )Do (x , y )⎞ ×⎜ ⎝ Do (x , y ) Doc (x , y )Do (x , y )Doc (x , y ⎟ ) ⎠ (11) Where ⎜ t+1 t t t+1 t+1 t+1 t t t t t+1 t+1 ⎟ ⎝ Do (x , y ) Doc (x , y )Do (x , y )Doc (x , y )⎠ Where Where

Oladipupo Luqman Salami and Abideen Adeyemi Adewale 20 These four distance functions can be equated with Farrell’s output-based that measure technical efficiency. In DEA, Farrell’s output-based measure the technical efficiency for every organization k' =1,…., where k, can be stated as: t t t −1 'k' [Do (xk', yk' )] = maxλ (7)

Subject to k k ' t+1 t t λ yk ,m ≤ ∑ zk yk ,m m =1,...,M , k =1 k zt xt ≤ xt+1 =1,..., N, ∑ k k,n k',n k =1 k t (VRS) ∑ zk =1 k=1 t zk ≥ 0 k =1,..., K. (8)

t+1 t+1 t+1 The calculation of Do (x , y ) is the same as Equation (8), anywhere t + 1 is replaced by t. To calculate the MI, it also requires the construction of two mixed-distance functions that is calculated by evaluating the observation in one time period with the best practice frontier of another time period. The opposite of the mixed-distance function for observation k' can be obtained from:

t t t −1 'k' [Do (xk', yk' )] = maxλ (9) Subject to k k ' t+1 t t λ yk ,m ≤ ∑ zk yk ,m m =1,...,M , k =1 k zt xt ≤ xt+1 =1,..., N, (10) ∑ k k,n k',n k =1 k t =1 (VRS) ∑ zk k =1 t zk ≥ 0 k =1,..., K.

Therefore, in order to calculate change in scale efficiency under the VRS technology, the contrary output distance functions must also be measured merging equation (6) into the restrictions in equations (8) and (10). Technical change can be measured in relation to the CRS technology. Scale efficiency change in every time period is measured in percentage of the distance function fulfilling CRS in relation to the distance function in VRS. Pure efficiency change on the other side measures the percentage of the own-period distance function in each period in VRS. These two distance functions will break down equation (2) to become: 28 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries

1 ⎛ Dt+1 xt , yt ⎞⎛ Dt+1 xt+1, yt+1 ⎞ 2 ⎛ Dt xt , yt ⎞ M xt , yt , xt+1, yt+1 = ⎜ o ( )⎟⎜ o ( )⎟ ×⎜ o ( ) ⎟ o ( ) ⎜ t t t ⎟⎜ t t+1 t+1 ⎟ ⎜ t+1 t+1 t+1 ⎟ (11) ⎝ Do (x , y ) ⎠⎝ Do (x , y ) ⎠ ⎝ Do (x , y )⎠ 1 ⎛ Dt+1 xt , yt Dt+1 xt+1, yt+1 Dt xt , yt Dt xt+1, yt+1 ⎞ 2 ×⎜ oc ( ) o ( ) oc ( ) o ( )⎟ (11) ⎜ t+1 t t t+1 t+1 t+1 t t t t t+1 t+1 ⎟ ⎝ Do (x , y ) Doc (x , y )Do (x , y )Doc (x , y )⎠

21Where Where Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries 1 ⎛ Dt+1 xt , yt ⎞⎛ Dt+1 xt+1, yt+1 ⎞ 2 ⎜ o ( )⎟⎜ o ( )⎟ = Technical Change ⎜ t t t ⎟⎜ t t+1 t+1 ⎟ ⎝ Do (x , y ) ⎠⎝ Do (x , y ) ⎠ ⎛ Dt xt , yt ⎞ ⎜ o ( ) ⎟ = Pure efficiency change ⎜ t+1 t+1 t+1 ⎟ ⎝ Do (x , y )⎠ 1 ⎛ Dt+1 xt , yt Dt+1 xt+1, yt+1 Dt xt , yt Dt xt+1, yt+1 ⎞ 2 ⎜ oc ( ) o ( ) oc ( ) o ( )⎟ = Scale Efficiency change ⎜ t+1 t t t+1 t+1 t+1 t t t t t+1 t+1 ⎟ ⎝ Do (x , y ) Doc (x , y )Do (x , y )Doc (x , y )⎠

It is worth to note that if technology in actuality shows CRS, the scale change factor will equal to 1 and it will be equivalentIt to is equation worth to (2).note that if technology in actuality shows CRS, the scale change factor will equal to 1 and it will be equivalent to equation (2).

4.4. RESULTSRESULTS

The table The1 below table is 1 the below descriptive is the descriptive statistical analysisstatistical of analysisinputs and of outputsinputs andof Malaysian outputs of Islami Malaysianc banks under study for theIslamic period banks of 2002 under-2011. study The for table the showsperiod theof 2002-2011.mean, median, The maximum, table shows minimum the mean, and median, the total of both the inputs and outputs. Among the banks, May Bank Islamic has the highest amount of total deposit, capital, maximum, minimum and the total of both the inputs and outputs. Among the banks, May Bank total loan, income and investment. This is generally expected given that May Bank is Malaysia’s biggest bank Islamic has the highest amount of total deposit, capital, total loan, income and investment. This and among the early starters of Islamic banking. Affin Islamic Bank has the lowest number of total deposit, capital, totalis generally loan, and expected income, givenexcept that for Mayinvestment; Bank is whereMalaysia’s Public biggest Bank bankIslamic and records among the the lowest early figures. Moreover,starters Table of1 Islamicbelow indicatesbanking. Affinthat over Islamic the lastBank nine has- yearthe lowest in the numbersample ofperiod total deposit,(2003- 2011),capital, the total maximumtotal loan loan, obtained and fromincome, the exceptIslamic for banking investment; operations where from Public all the Bank five Islamic banks increasedrecords the by lowest 445%. That is, from RM8.25figures. billion Moreover, in 2003 toTable RM45.84 1 below billion indicates in 2011. that Thisover indicatesthe last nine-yearthat the responsiveness in the sample ofperiod Malaysians towards the(2003-2011), Islamic banking the total and maximum finance loanduring obtained these yearsfrom thehas Islamicincreased banking tremendously. operations Duringfrom all the years (2002-2011),the fivethe total banks maximum increased incom by 445%.e generated That is,from from the RM8.25Islamic bankingbillion in operations 2003 to RM45.84 from all thebillion five banks increased infrom 2011. RM264 This millionindicates in 2003that theto RM1.39responsiveness billion inof 2011. Malaysians The total towards income the increased Islamic by banking 426%, and this indicates thatand Islamicfinance bankingduring these operations years generatehas increased substantial tremendously. amount ofDuring income the toyears the (2002-2011),banks. This should the have significanttotal implication maximum for income improved generated performance from theof their Islamic parent banking banks. operations from all the five banks increased from RM264 million in 2003 to RM1.39 billion in 2011. The total income increased The maximumby 426%, total investmentand this indicates in the Islamic that Islamic banking banking operations operations has increased generate by substantial 113% from amount RM2.99 of billion in 2003 to RM6.36income billionto the inbanks. 2011. This Compared should withhave thesignificant percentage implication increase infor total improved income performanceit appears that of amount invested quadrupledtheir parent inbanks. returns. This shows income generated from investment is quite high. This is perhaps evidence that Islamic banking and finance is really growing in Malaysia. In addition, the maximum amount of deposits recordedThe maximum by the totalbanks investment from 2003 in to the 2011 Islamic increased banking by 390%.operations That has is, increasedfrom RM12.16 by 113% billion from in 2003 to RM59.66 RM2.99billion in billion 2011. in This 2003 is toevidence RM6.36 that billion the awarenessin 2011. Compared of Islamic with banking the percentage operations increaseis improving as increase in the total deposits proves more people are patronizing the Islamic banking operations. Finally, the total maximum capital used as start-up capital has increased by 395% from RM798 million in 2003 to RM3.96 billion in 2011. This shows that banks are expanding their Islamic banking operations such as changing their operation from window to fully fledged Islamic bank as subsidiary to the main bank.

Table 1: Descriptive Analysis of Yearly Input and Output Variables of the Selected Banks from 2003-2011 OUT Mean Min Max SD

PUT TL Inc Inv TL Inc Inv TL Inc Inv TL Inc Inv 2002 3,751,252 132,588 1,926,947 1,317,700 52,232 1,023,882 8,253,532 264,966 2,992,252 3,903,353 115,515 1,054,301 2003 4,686,589 126,698 1,701,774 1,321,520 44,720 157,464 11,703,438 225,449 3,201,790 2,180,452 74,491 1,197,245 2004 5,570,346 166,593 1,254,636 1,170,625 42,896 127,503 14,581,517 343,841 3,111,229 5,380,992 114,436 1,199,042 2005 6,313,589 245,129 1,284,649 1,495,292 34,120 127,479 16,052,758 551,490 3,401,307 5,827,624 211,040 1,296,113 2006 7,004,123 280,871 1,041,319 1,227,293 92,436 51,736 16,677,354 684,234 2,643,072 6,093,874 249,425 987,464 2007 7,653,635 280,968 1,429,494 1,734,155 105,381 392,670 17,945,079 595,607 3,735,886 6,599,276 211,611 1,334,394 2008 8,970,282 160,167 1,581,024 2,449,939 89,863 782,750 20,929,988 347,870 2,715,435 7,573,774 106,474 808,922 2009 10,439,797 362,848 2,344,126 2,880,708 86,594 1,359,522 25,302,763 828,478 4,102,498 9,463,328 336,963 1,124,317 2010 13,231,667 416,132 2,898,366 3,555,596 90,625 1,334,083 33,410,134 961,767 4,471,808 12,384,960 393,607 1,143,976 2011 17,503,164 525,983 3,487,112 4,374,205 137,790 1,480,275 45,844,219 1,393,248 6,365,100 16,943,869 551,880 1,782,529

Oladipupo Luqman Salami and Abideen Adeyemi Adewale 29 Inv 987,464 808,922 1,054,301 SD 1,296,113 1,782,529 1,197,245 1,199,042 1,334,394 1,124,317 1,143,976 Cap 512,322 445,872 650,694 719,290 770,856 521,327 849,186 340,183 1,139,349 1,376,389 SD Inc 74,491 211,611 115,515 114,436 211,040 551,880 249,425 106,474 336,963 393,607 TD TL 5,110,659 5,110,659 3,876,546 5,509,410 6,286,882 4,476,868 5,538,900 7,728,099 10,412,457 13,468,471 23,254,451 3,903,353 2,180,452 5,827,624 7,573,774 5,380,992 6,093,874 6,599,276 9,463,328 16,943,869 12,384,960 Inv 3,111,229 6,365,100 2,992,252 3,201,790 3,401,307 2,715,435 2,643,072 3,735,886 4,102,498 4,471,808 Cap Max 798,767 1,150,379 1,299,767 1,806,571 1,963,746 1,684,840 2,558,461 3,297,361 3,961,412 1,876,616 Inc Max 264,966 225,449 343,841 551,490 347,870 684,234 595,607 828,478 961,767 1,393,248 TD TL 15,116,880 15,116,880 12,166,584 12,436,015 15,965,833 18,476,399 18,530,067 24,301,550 30,442,998 39,549,999 59,664,840 8,253,532 11,703,438 45,844,219 14,581,517 16,052,758 20,929,988 16,677,354 17,945,079 25,302,763 33,410,134 Inv 51,736 157,464 127,503 127,479 782,750 392,670 Min Cap 1,480,275 1,023,882 1,359,522 1,334,083 207,865 250,788 291,102 315,152 196,696 235,607 262,671 398,071 433,504 481,288 Inc Min 52,232 44,720 42,896 34,120 92,436 89,863 86,594 90,625 137,790 105,381 TD 2,812,932 2,877,670 2,287,365 2,149,384 3,123,870 5,325,725 5,513,324 5,865,789 6,878,560 9,676,383 TL 4,374,205 1,317,700 1,321,520 1,170,625 1,495,292 1,227,293 1,734,155 2,449,939 2,880,708 3,555,596 Inv Cap 3,487,112 1,926,947 1,701,774 1,254,636 1,284,649 1,041,319 1,429,494 1,581,024 2,344,126 2,898,366 Mean 405,962 674,976 695,213 889,818 967,993 888,014 1,070,356 1,219,582 1,469,946 1,747,794 Inc Descriptive Analysis of Yearly Input and Output Variables of the Selected Banks from 2003-2011 Variables Input and Output Yearly Analysis of Descriptive Mean 525,983 132,588 126,698 166,593 245,129 280,871 280,968 160,167 362,848 416,132 TD Table 1: Table 6,300,061 5,780,973 6,375,255 7,661,470 7,951,684 9,723,926 TL 11,903,629 11,903,629 15,080,474 17,745,289 25,653,007 3,751,252 4,686,589 5,570,346 6,313,589 7,004,123 7,653,635 8,970,282 17,503,164 10,439,797 13,231,667 Input: TD – Total Deposit; Cap - Capital Total TD – Input: Output: TL – Total Loan, Inc – Income; Inv- Investment Total – TL Output: Authors’ Computation Authors’

2011 2002 2003 2004 2005 2006 2007 2008 2009 2010 PUT 2011 OUT OUT 2002 2003 2004 2005 2006 2007 2008 2009 2010 INPUT

Notes: Source: 30 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries in total income it appears that amount invested quadrupled in returns. This shows income generated from investment is quite high. This is perhaps evidence that Islamic banking and finance is really growing in Malaysia. In addition, the maximum amount of deposits recorded by the banks from 2003 to 2011 increased by 390%. That is, from RM12.16 billion in 2003 to RM59.66 billion in 2011. This is evidence that the awareness of Islamic banking operations is improving as increase in the total deposits proves more people are patronizing the Islamic banking operations. Finally, the total maximum capital used as start-up capital has increased by 395% from RM798 million in 2003 to RM3.96 billion in 2011. This shows that banks are expanding their Islamic banking operations such as changing their operation from window to fully fledged Islamic bank as subsidiary to the main bank.

4.1. Efficiency of the banks, 2003 to 2011 (VRS)

From Table 2 below, only Hong Leong Islamic Bank is consistently efficient under Variable Return to Scale (VRS). Both Public Islamic Bank and RHB Islamic Bank are found to be efficient under VRS from 2003-2007 and 2008-2011. The banks were only inefficient in the year 2008 under VRS. Whereas, Affin Islamic Bank is found to be efficient under VRS from 2003-2007, and exhibits inefficiency for 2008-2009, the bank is later found to be efficient for 2010-2011. May Bank Islamic is found to be efficient under VRS from year 2003-2005, and the bank result keeps fluctuating. For instance, between 2006 and 2011, the banks oscillate between efficiency and inefficiency every other year. The result shows that majority of the banks have effectively kept pace with precise sufficient production potentials and increased their space to the industrial production frontier under the VRS technology. The only exception in this regard is May Bank Islamic that exhibits inefficiency in 2006.

Table 2: Efficiency of the banks, 2003 to 2011 (VRS) NO BANK 2003 2004 2005 2006 2007 2008 2009 2010 2011 1 AFFIN BANK 1.000 1.000 1.000 1.000 1.000 0.733 0.966 1.000 1.000 2 HONG LEONG BANK 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 3 MAY BANK 1.000 1.000 1.000 0.957 1.000 0.955 1.000 0.995 1.000 4 PUBLIC BANK 1.000 1.000 1.000 1.000 1.000 0.763 1.000 1.000 1.000 5 RHB BANK 1.000 1.000 1.000 1.000 1.000 0.257 1.000 1.000 1.000 MEAN 1.000 1.000 1.000 0.991 1.000 0.742 0.993 0.999 1.000 Source: Authors’ Computation

4.2. Efficiency of the banks, 2003 to 2011 (CRS)

Also, under the CRS starting from 2004 -2007 majority of the banks are efficient. This indicates that those banks are able to produce 100% of their potential output during those years prior to starting operations as full-fledged Islamic banks. Again, the only exception is May Bank Islamic that produced 55.2% of its potential output in 2006. Contrary to the result under VRS in Table 3, most of the banks are found to be inefficient under CRS in Table 4 in 2003. The proportion of their potential output level were Affin Bank 93.5%, Public Bank 37% and RHB Bank 48%. Oladipupo Luqman Salami and Abideen Adeyemi Adewale 31

Table 3: Efficiency of the banks, 2003 to 2011 (CRS) NO BANK 2003 2004 2005 2006 2007 2008 2009 2010 2011 1 AFFIN BANK 0.935 1.000 1.000 1.000 1.000 0.376 0.893 1.000 1.000 2 HONG LEONG BANK 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 3 MAYBANK 1.000 1.000 1.000 0.552 1.000 0.836 1.000 0.793 1.000 4 PUBLIC BANK 0.370 1.000 1.000 1.000 1.000 0.356 0.844 1.000 1.000 5 RHB BANK 0.480 1.000 1.000 1.000 1.000 0.122 1.000 1.000 1.000 MEAN 0.757 1.000 1.000 0.910 1.000 0.538 0.947 0.959 0.998 Source: Authors’ Computation

As shown by the mean scores in Table 3, on average the banks’ efficiency increased from 2003 to 2004. The banks were able to maintain this till 2005, experienced a decline in 2006, but increased efficiency again in 2007. If compared with 2008 to 2011, the increase in the overall arithmetic mean before 2008 is better. As exhibited in the arithmetic mean in Table 3, the average efficiency for the banks remained at unity (highly efficient), but declined slightly in 2006. It increased again in 2007 and kept fluctuating after wards. From Tables 2 and 3, the banks’ efficiency changed from 2003 to 2011 under the CRS and VRS. This shows that the average efficiency performance of the banks is relatively high from latter perspective. Most of the banks are highly efficient from 2003 to 2007 which is considered as pre-full fledge Islamic banking operations (Islamic window), than during the full fledge Islamic banking operations for both VRS and CRS.

4.3. Production Performance for Each Bank

The result shown in Tables 4 to 6 gives the performance of the banks from 2003 to 2011 for changes in TFP and its divisions – Technical Change (TC) and Efficiency Change (EC). The values of the MI TFP productivity index and its division are interpreted as any value that is less than 1 indicates a decrease or deterioration. On the contrary, values that are more than 1 imply development in the relevant area.

The figures shown in the table 4 below indicate average increase or decrease per year for the actual period of time covered. The calculations only focus on performance in relation to the

Table 4: Banks Relative Malmquist TFP Change Between Time Period t and t +1, 2003 to 2011 2003- 2004- 2005- 2006- 2007- 2008- 2009- 2010- NO BANKS MEAN 2004 2005 2006 2007 2008 2009 2010 2011 1 AFFIN BANK 1.486 1.392 1.390 2.582 2.776 1.102 0.893 1.696 1.665 2 HONG LEONG BANK 1.712 1.264 3.091 2.798 2.715 4.091 3.786 1.330 2.598 3 MAY BANK 2.269 1.141 1.290 0.848 0.725 1.336 3.846 0.666 1.515 4 PUBLIC BANK 0.543 0.883 1.193 1.639 2.038 0.362 1.426 0.938 1.128 5 RHB BANK 0.947 1.678 0.634 1.202 2.864 0.412 0.887 0.586 1.151 MEAN 1.391 1.272 1.519 1.814 2.224 1.461 2.168 1.043 1.611 Source: Authors’ Computation 32 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries most excellent practice with appropriate performance or rather in relation to the most excellent practice in the sample.

Table 4 exhibits the change in the Malmquist-based TFP index. As shown in Table 5, May Bank, Hong Leong Bank and Affin Banks’ performance show a positive productivity changes from 2003 to 2011. On the contrary, Public Bank and RHB Banks’ performance shows a highly deteriorating productivity change from 2003 to 2006 and 2008 to 2011. Moreover, Hong Leong Bank has the highest mean of increase in productivity change at an annual average rate of 159%, followed by Affin Bank with annual average mean of 66% and follow next is May Bank with annual average mean of 55%.

Assessing the whole banks in general, all the banks have increased their productivity change on average by 61% per annum for the duration 2003 to 2011. The other division of the Malmquist productivity index – Technical Change and Efficiency Change results are reported in Tables 5 and 6 below.

Table 5: Bank Relative Technical Change Between time Period t and t + 1, 2003 - 2011

2003- 2004- 2005- 2006- 2007- 2008- 2009- 2010- NO BANKS MEAN 2004 2005 2006 2007 2008 2009 2010 2011 1 AFFIN BANK 0.843 0.830 1.023 0.670 0.994 0.669 1.011 1.094 0.892 2 HONG LEONG BANK 0.606 1.865 0.597 0.919 1.959 0.123 0.865 1.080 1.002 3 MAY BANK 0.789 0.994 1.230 0.793 1.602 0.729 0.644 1.511 1.037 4 PUBLIC BANK 0.747 1.109 1.373 0.782 0.651 1.683 1.272 1.723 1.168 5 RHB BANK 0.534 0.875 1.957 1.226 1.004 0.813 1.268 2.169 1.231 MEAN 0.694 1.083 1.151 0.859 1.242 0.606 0.981 1.462 1.066 Source: Authors’ Computation

Table 5 shows the index figures of technical improvement, or decline as calculated by the average changes in the most excellent practice frontier from t to t + 1. As revealed by the results, all the banks experienced both technical improvement and deterioration from 2003 to 2011. Based on the year under review, Hong Leong Bank has the highest change in technical deterioration of (-87.7%) in the year 2008 to 2009. RHB Bank has the highest technical development (26.8%) in the year 2010 to 2011.

Similarly, Table 5 also presents that all the five banks experienced technical improvement in 2010-2011. The number of banks that are technically improved reduced to three in 2009-2010, while the number drastically declined to one in 2009-2008. The banks that show technical improvement in 2007-2008 are also three. However, in 2006-2007 only one bank out of the five banks experienced technical advancement, four banks in 2005-2006, two banks in 2004- 2005 and no bank experienced technical development in 2003-2004.

Overall average, year 2008-2009 is found to have the highest technical deterioration (-39%). Affin Bank has the highest technical deterioration (-10.8%), while RHB Bank has the highest technical improvement with 23.1% followed by Public Bank with 16.8%, May Bank is found to have 3.7% and Hong Leong Bank with 0.2%. Oladipupo Luqman Salami and Abideen Adeyemi Adewale 33

Table 6: Changes in Banks Relative Efficiency between Time Period t and t + 1, 2003 to 2011

2003- 2004- 2005- 2006- 2007- 2008- 2009- 2010- NO BANKS MEAN 2004 2005 2006 2007 2008 2009 2010 2011 1 AFFIN BANK 1.069 1.000 1.000 1.000 0.376 2.376 1.120 1.000 1.118 2 HONG LEONG BANK 1.000 1.000 1.000 1.000 1.000 1.000 1.000 0.988 0.999 3 MAY BANK 1.000 1.000 0.552 1.813 0.836 1.196 0.793 1.262 1.057 4 PUBLIC BANK 2.704 1.000 1.000 1.000 0.356 2.369 1.185 1.000 1.327 5 RHB BANK 2.083 1.000 1.000 1.000 0.122 8.175 1.000 1.000 1.923 MEAN 1.432 1.000 0.888 1.126 0.424 2.229 1.010 1.045 1.284 Source: Authors’ Computation

5. FINDINGS

Result from Table 6 presents the relative change efficiency for each bank. The findings show the significant difference among the banks and periods. Most of the banks are found to be efficient from 2004 to 2007. A plausible reason could be the global financial crisis that became pervasive since around 2008. As noted by Derbel, Bouraoui & Dammak (2011), the Islamic banking and finance industry was also mildly affected by the crisis as reflected in their relatively dismal performance during the period. As such, for the rest of the years the banks experienced fluctuation in their efficiency; that is the efficiency increased more than 1 in some period, and less than 1 in some other period or the efficiency remained constant – that is no changes in efficiency.

In addition, the findings revealed that majority of the banks have constant efficiency from 2004-2007. During this period most of the banks operated their Islamic banking operations as a window within their parent banks. The reason for this could be to some extent that the operations of the conventional banking operated side-by-side with the Islamic banking operations thereby supporting the efficiency of the Islamic window. There is also a possibility that the activities of the conventional banking operations significantly increase or stabilize the Islamic banking operations during those periods. It could be that conventional banking operations help Islamic banking operation in areas such as loans and advances, capital market investment and money market investment since both banking operations will at the end of the year report a single financial report. Moreover, Samad (2009) noted that the Islamic banking windows demonstrate efficiency if they could leverage on the operational efficiency of the existing conventional banking arrangements within their banks.

It is likely that the motive for running both banking operations (conventional and Islamic) is to generate more returns to the banks. As such, it can be argued that efficiency is maintained during those periods because the banks appear to be able to reduce their personnel and capital expenses. For instance, same staffs that are used to operate the conventional banking operations also operate the Islamic banking operations. This, perhaps save the banks some money, unlike when they operate as a full fledge Islamic bank, in which case as a subsidiary their operations is totally separated from the conventional banking system. This aligns with the opinion of Berger 34 Malaysian Islamic Banks’ Efficiency: An Intra-Bank Comparative Analysis of Islamic Windows and Full-Fledged Subsidiaries and Humphrey (2007) and Hassan et al (2009) that attribute the Islamic banking windows’ efficiency to shared cost of input like personnel and administrative expenses.

More so, efficiency can also be maintained due to capital expenditure - the capital expenditure was borne by the whole bank not the Islamic window. For example: building rent, electricity bill and other maintenance expenses. All the expenses will not affect the operation of the Islamic window thus they will be efficient since the input will be low in terms of expenses and output is maximized. Based on the years under examination, this study findings show that, RHB Bank has the highest level of efficiency change with 92.3%, Public Bank takes second position by recording 32.7%, the next is Affin Bank with 11.8%, followed by May Bank with 5.7% and Hong Leong Bank is found to be the only bank that experienced efficiency deterioration with (-0.1%). This is notwithstanding the fact that Public Bank has the highest level of efficiency deterioration during the year 2007 to 2008 with (-64.4%). Generally, ranging from 2003 to 2011, with exception of 2007 to 2008 with the least record of positive efficiency change follow by 2005 to 2006, all other years present a positive efficiency change.

Moreover, the pure efficiency seems not to be significant means of growth to efficiency change, if compared with the scale efficiency change division for each bank under review. Except for Hong Leong Bank, the other banks under examination experienced changes in both pure efficiency and scale efficiency during the year 2007 to 2008 and 2008 to 2009. Such findings align with those of Mokhtar, Abdullah & Alhabshi (2008) and Sufian (2006). They stated that a likely reason could be because this period marks the year when most of the banks changed their operations from Islamic window to full fledge Islamic banking.

All the banks recorded positive changes in their yearly growth for pure efficiencies from 2003 to 2007, except May Bank that experienced decline in efficiency with about (-4.3%) in 2005 and experienced efficiency improvement of about 4.4% in 2006. RHB Bank has the highest deterioration of pure efficiency of (-74.3%) in 2007 to 2008. Public Bank has the highest deterioration of (-53.3%) in 2007 to 2008 in terms of scale efficiency. Interestingly, RHB Bank has the highest improvement of pure efficiency of 288% in year 2008 to 2009. Public Bank on the other hand also has the highest improvement of scale efficiency of 108% in year 2003 to 2004. For the entire years under examination, year 2008 to 2009 is recognized as the year of pure efficiency advancement. While the rest of the period, is recognized as years of scale efficiency improvement.

6. CONCLUSION

This study compared the efficiency of five Islamic banks in Malaysia during their operation as Islamic window and later transformation to a full-fledged Islamic bank. The aim is to fill an apparent dearth of empirical studies in this context, especially in the case of Malaysia as a hub of the burgeoning Islamic banking and finance industry. Data envelopment analysis is used to assess both the technical and scale efficiency of the banks under sample. Sequel to the results obtained the following conclusion is made in this study.

From the analysis in this study, the efficiency scores for the bank as an Islamic window are relatively better compared to subsequent efficiency scores obtained for same Islamic banks Oladipupo Luqman Salami and Abideen Adeyemi Adewale 35 as a full-fledged subsidiary. As such, a conclusion may be that Islamic windows are more efficient than full fledge Islamic bank.

Moreover, year of commencement of operation seems to exert so much influence on efficiency at least in the context of the Islamic banks under sample. Based on this study’s analysis, banks that have longer years of operation of Islamic banking and finance as windows (May Bank and Public Bank) are more efficient compared with banks that started full fledge Islamic banking early (RHB Bank and Affin Bank). Therefore, this study can conclude that years of existence determine efficiency.

Based on the summary of the MI productivity means of the banks; it is indicated that the Total Productivity Change has been improved over the years covered. However, it will be important to point out here that the improvement in the Total Productivity Change is as a result of increase in both technical change and efficiency change, and with the latter enjoying prominence. It is thus concluded that Islamic banks should leverage more on technological knowledge to be able to optimize the technical procedure so as to enable them increase their competitive edge in the future.

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