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E-PROCEEDING OF THE 6TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES RESEARCH 2017

DO CITIZENS’ RIGHT INFLUENCE THE PRODUCTIVITY OF DOMESTIC AND FOREIGN ISLAMIC BANKS? EMPIRICAL

EVIDENCE FROM INDONESIA AND MALAYSIA

Nur Ainna Ramli

Faculty of Economics and Muamalat, University Sains Islam Malaysia71800, Bandar BaruNilai, Negeri Sembilan, Malaysia

[email protected]

Fakarudin KamarudinFaculty of Economics and Management

Universiti Putra Malaysia,Serdang, Selangor Darul Ehsan, 43400 Malaysia

[email protected]

Nazratul Aina Mohamad Anwar

Faculty of Economics and Muamalat, University Sains Islam Malaysia71800, Bandar BaruNilai, Negeri Sembilan, Malaysia

[email protected]

ABSTRACT

This study provides new empirical evidence on the level of productivity of domestic and foreign Islamic banks in Brunei, Indonesia, and Malaysia over the period of 2006 to 2015. Furthermore, this study also investigate the impact of citizens’ right and other potential determinants on banks’ productivity. The analysis comprises two main stages. In the first stage, we employ the Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) method to measure the Total Factor Productivity Change (TFPCH) of Islamic banks. We then used the Multiple Panel Regression Analysis (MPRA) framework based on the Ordinary Least Square (OLS). The Breusch Pagan and Lagrangian Multiplier test need to be execute in order to identify either the data suitable to be pooled or panel. Furthermore, for the robustness results, the Generalized Least Square (GLS) methods comprising the Fixed Effect (FE) and Random Effect (RE) models adopted to investigate the effect of citizen’s right and potential determinants on banks’ productivity. The selection of estimation method of FE and RE regression analysis are based on the Chi-Square (X²) of the Hausman test.

Field of Research : Citizens’ right, Islamic banks, data envelopment analysis, Malmquist productivity index, Multiple panel regression analysis, total factor productivity change

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E-PROCEEDING OF THE 6TH INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES RESEARCH 2017 (ICSSR 2017).(E-ISBN: 978-967-0792-23-1). 4th December 2017, Melia, Kuala Lumpur, Malaysia.

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1. Introduction

The Islamic banking system has been in existence since the 1970s (Dixon, 1992), and it is progressively expanding worldwide, with signs of positive growth moving forward. According to El-Oorchi (2005), the expansion of Islamic financial institutions has grown from one in 1975 to more than 300 institutions worldwide over the past three decades. The number of total assets of Islamic financial institutions is estimated to exceed surpassed USD1 trillion and is still growing at a rate of 15 to 20 percent per year over the past five years (Ahmed, 2010; Abdullah et al., 2013). Supported by Ariss (2010), the Islamic banking industry has progressed into a viable mode of finance, and the number is still growing enormously. The Islam banking and finance system are getting popular because its offers more ethical alternative than the conventional financial system (Mansoor and Ishaq, 2008). Furthermore, due to the 2008 world financial crisis, this crisis has further intensified the attraction of Islamic finance to related parties in their search for a more feasible and resilient alternative financial system (Ibrahim, 2015).

Even though the Islamic banking and finance system has seen great progress in recent years since the early 1990s, many studies have focused mainly on the efficiency level of financial institutions where efficiency is used as an indicator to measure a bank’s performance (Mokhtar et al., 2008). To date, only limited studies have been conducted to investigate whether conventional and Islamic banks have performed productively as an intermediary. This could partly be due to the unavailability of data as most of the Islamic financial are not publicly traded (Sufian et al., 2008).

Despite the growing interest in the Islamic banking sector, few studies (Beck et al., 2013; Ariss, 2010) have shown that the Islamic banking industry is still less efficient than its conventional counterparts in other parts of the world. This is because empirical works on Islamic bank performance are still in their infancy. Furthermore, current studies on Islamic banking sectors are generally focused on theoretical issues, and empirical work has relied mainly on the analysis of descriptive statistics rather than rigorous statistical estimation (El-Gamal and Inanoglu, 2004). Even though the competition between conventional banks and Islamic banks is not at a significant level at the current moment, Islamic banks have their own viral nature among the public. Therefore, studies on the productivity level of Islamic banking sectors have become an important part of the banking literature.

Given the uprising number of Islamic financial institutions worldwide with a greater variety of financial services offered, there may be uprising demand for its quality of products and services. In addition to its rapid growth in the Middle East and Southeast Asian countries, more remarkable development of the Islamic finance sector is its penetration into non-Muslim countries, primarily in Europe and North America (Pollard and Samers, 2007). Therefore, Islamic financial institutions are required to pay closer attention to the manner of providing sustainable business operations.





However, to date, very limited study has been conducted to examine the impact of citizens’ right on productivity level of Islamic banks in Southeast Asia countries, which are a strong regional hub for Islamic banking. In general, the citizens’ right is the dimension of process of selecting, monitoring and replacing of the government proxied by country governance that measured by two indices namely i) voice and accountability (VA) and ii) Political stability and absence of violence (PS). Kaufmann et al. (2010) defines country governance as a set of traditions and institutions by which authority in a country is exercised or in the simple word classify as country’s rules. VA is defined as the capabilities of citizens to participate in selecting their government, freedom of expression, freedom of association, and a free media. PV is the stability in politics since there is likelihood that a government could be destabilized or overthrown by unconstitutional or violent means, including politically motivated violence and terrorism (Kaufmann et al. 2010).

Previous studies mainly focused on the impact of micro governance to the bank efficiency. Although governance quality may exert positive influence on efficiency levels (Meon and Weill, 2005), the empirical findings by Bukhari et al. (2013), Darmadi (2013), and Ginena (2014) remain inconclusive at best. This could be due to difficulties in evaluating the indicators of quality in governance due to its subjective evaluation. Usually, such evaluation is based on experts ratings indices produced by private risk rating agencies (e.g. Internal Country Risk Guide or Business Environmental Risk Intelligence). In addition, international and non-governmental organizations provide similar indicators obtained from residents’ surveys and are presented in the World Economic Forum’s Global Competitiveness Report. However, these indicators are not always inclusive, with limited availability, and biased (Kaufmann and Kraay, 2008). By allowing the elimination of the individual indices and providing the overall countries’ aggregate indices, the country governance (macro governance) indicators are capable to avoid these deficiencies.

The earlier studies by Lensink et al. (2008) and Chortareas et al. (2012) find that citizens’ right proxied by country governance significantly influence the efficiency of the banking sector. Similarly, Meon and Weill (2005) and Hwang and Akdede (2011) among others suggest that the more efficient countries tend to report better governance levels. Therefore, it is reasonable to expect good country governance is systematically important to enhance banking sector efficiency. Interestingly, despite bearing significant influence on the banking sector, studies examining the impact of citizens’ right on the productivity of banks are limited and further compounded by the fact that empirical evidence on the Islamic banking sector is completely missing from the literature.

In view of the limitations highlighted, the present paper aims to build on the earlier contributions on the productivity of domestic and foreign Islamic banks operating in Brunei, Indonesia, and Malaysia and to establish new empirical evidence on the impact of citizens’ right. In Southeast Asian countries, Malaysia, Indonesia and Brunei are one of the main players of Islamic banking industry that offer a wide range of Islamic financial products and services (Khan and Bhatti, 2008). For that reason, the paper motivated to focus on these three countries. To do so, we collect and analyse data on both domestic and foreign Islamic banks operating over the period of 2006 to 2015. The analysis comprises of two main stages. In the first stage, we employ the Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) method to measure the Total Factor Productivity Change (TFPCH) of Islamic banks. We then employ Multiple Panel Regression Analysis (MPRA) to examine the impact of citizen’s right on the productivity of both domestic and foreign Islamic banks. The paper also investigates to what extent internal (i.e. bank specific characteristics) and external factors (i.e. macroeconomic conditions) influence the productivity of Islamic banks while controlling for the impact of citizen’s right.





2. Overview of Islamic Bank Performance

Despite the expeditious growth of interest in the Islamic banking and finance industry, analysis of Islamic banking at the cross-country level is still in its infancy (Noor and Ahmad, 2012). Literature on existing studies can be classified into two groups. The first group of studies includes the evaluation of Islamic bank efficiency by geographical factors, while the second group of studies includes the comparative analysis of the efficiency level between the Islamic banks and/or conventional banks (Rosman et al., 2014).

Beck et al. (2013) compared the business model, efficiency, asset quality, and stability of both Islamic and conventional banks over a period from 1995 to 2009; the data includes 510 banks across 22 countries, of which 88 are Islamic banks. Using a range of indicators constructed from the balance sheet and income statement data, the contribution of the paper is consistent with the study of Hasan and Dridi (2010), which asserts that Islamic banks are less cost-effective, but the better stock performance of listed Islamic banks during the crisis is also due to their higher capitalisation and better asset quality.

Hasan and Dridi (2010) examined the performance of Islamic banks and conventional banks. They found that the two types of banks had significant market share during the 2008 global crisis. They review the impact of the crisis towards profitability, credit, asset growth, and external ratings in a group of five Gulf Cooperation Council (GCC) countries and three non-GCC countries which consist of a total 120 banks, one-fourth of which are Islamic banks. They found mixed findings on Islamic banks’ performance during the period of study. Islamic banks face the larger decline of profitability in 2009 compared to conventional bank peers due to the weaker risk management practices. However, Islamic banks’ credit and asset growth performed better than conventional bank peers in 2008/2009, which allows them to contribute to the financial and economic stability.

A cross-country analysis of the Islamic banks made by Ahmed et al. (2010) explained the performance of the Islamic banking industry in the world covering 25 countries, which include 77 Islamic banks during 2003 to 2009. In that study, the efficiency level of each bank is evaluated using a nonparametric DEA method. The results suggested that the Islamic banks’ pure technical efficiency outweighs scale efficiency in Islamic banking industries. According to Ahmad et al. (2010), the banks originating from the high-income countries are the leaders of the greatest efficiency perimeter during the period of study.

Ariss (2010) provided a comprehensive comparative review of the literature in Islamic and conventional global banking markets across 13 countries from 2000 to 2006 using different indicators of market power. The empirical results suggest that the Islamic banks allocate a higher share of their assets to financing activities, which implies greater exposure to credit risk. Furthermore, the Islamic banks’ can balance their portfolio risk with significantly lower financial risk through higher capitalisation levels. Despite the fact that analysis shows that profitability significantly increases with market power, this does not ensure higher profitability levels for Islamic banks.

More recently, Sufian and Kamarudin (2015) provided an analysis of the revenue efficiency features of Islamic banks in Brunei, Indonesia, and Malaysia between 2006 and 2011. Using the panel regression analysis framework based on the ordinary least square and generalised least square methods, they estimate revenue efficiency for a sample of 17 Islamic banks during the period under





study. The interesting contribution of this paper is that bank size, asset quality, capitalisation, liquidity, and management efficiency significantly influence the revenue efficiency of selected domestic Islamic banks. In addition, the analysis also suggests that revenue efficiency has a greater influence on the profit efficiency levels. The results show that the degree of revenue efficiency on the domestic Islamic banks is higher compared to their foreign Islamic bank counterparts.

Viverita (2011) analysed and compared the performance of the Indonesian Islamic and conventional banks during the period from 2004 to 2008 by using three financial efficiency ratios, namely, cost efficiency ratios, revenue efficiency ratios, and profit efficiency ratios. The findings suggested that the Islamic banks experienced higher performance than that of conventional banks during the period of study. The research also found that Islamic banks appear to be able to generate higher revenue and profit efficient than conventional banks. This study also reveals that the bigger size Islamic banks are more cost efficient than their conventional bank peers.

Muda et al. (2013b) examined the efficiency of seventeen Malaysian Islamic banks during the period of 2007 to 2010 by using the nonparametric DEA method approach to estimate the technical efficiency as the dependent variable. They found that the performance of Malaysian Islamic banks was not negatively affected by the global financial crisis. The empirical findings suggest that the bank specifics and financial structure determinants can highly influence the efficiency of Islamic banks in Malaysia.

Irfan et al. (2014) provided a comparative analysis of the efficiency of Islamic banking sectors in South Asian countries by utilising profitability ratios to examine the profitability, management capability, leverage, and cash flow for each bank during the period of 2004 to 2011. The study sample consists of four countries, namely Brunei, Pakistan, Iran, and Bangladesh. The findings showed that the efficiency of Islamic banks in Brunei is highest in South Asian and has demonstrated consistent growth in return on equity in the period of study.

Muda et al. (2013a) employed Generalised Least Square and financial ratios to a small sample of 17 Malaysian Islamic banks during the period of 2007 to 2010, studied the differences in the profitability determinants between domestic and foreign banks. The study reported that the domestic Islamic banks are more profitable than foreign Islamic banks, even though profitability of domestic banks is affected by the recent global financial crisis, thus implying that the domestic banks need to strengthen their risk management aspects. Determinants such as overhead expenses, loans, efficiency, gross domestic product growth rate, and bank size have a significant effect on the profitability of domestic banks.

Another study by Sufian et al. (2014), who examined the revenue efficiency of the Malaysian Islamic banks during the period of 2006 to 2010 showed contradicting results with the study by Muda et al. (2013). They employed a variety of parametric (t -test) and nonparametric DEA techniques to a panel of 17 domestic and foreign Islamic banks. However, they found that the domestic Islamic banks have exhibited lower-level revenue efficiency compared to foreign Islamic banks peers. In this study, revenue efficiency is the main factor which influences the profit efficiency level in this study. Furthermore, the empirical finding indicates that bigger domestic Islamic banks tend to operate a constant return to scale or decreasing returns to scale, while smaller foreign Islamic banks tend to operate at a constant return to scale or to increase the return to scale.

Isik and Hasan (2003) investigated the productivity growth, efficiency change, and technical progress in all Turkish commercial banks. The study evaluated 458 observations during the period of 1981 to 1990 by utilising a DEA-Malmquist Total Factor Productivity Change Index and intermediation





approach was adopted to define bank inputs and outputs. They found that the mean of technical efficiency of foreign banks demonstrated noticeable rate of increase during the period of study. In contrast, during the same period, the state-owned banks faced a decreasing rate of technical efficiency. The study also suggests that scale problems began to dominate the technical inefficiency of domestic banks in the later years of the liberalisation.

Wang et al. (2012) suggests that one of the mechanisms to ensure the efficiency of banks is through the quality of corporate governance. In general, the majority of studies that examine the effect of governance on bank efficiency focus on the micro dimension which is governance within the banking institution (Darmadi, 2013; Bukhari et al. 2013; Jiang et al. 2009; Berger et al. 2005). Lensink et al. (2008) and Chortareas et al. (2012) among others point out that country governance (macro governance) which primarily covers the governance within a particular country may also significantly influence the efficiency of the banking sector.

Chortareas et al. (2012) conclude that the citizens’ right that measured by voice accountability (VA) has a positive relationship with bank efficiency. This result indicates that more freedom of expression and free media in the system of the country will produce a more developed and democratic system that is associated with banks’ efficiency.

Another study by Lin et al. (2010) examines the effects of bank competition and information sharing on the efficiency of the banks. In their findings, VA is found to have a positive and significant relationship with bank efficiency. This implies that the higher the citizens’ freedom is in participating in the process of selecting a government in the voice and accountability, the higher bank efficiency is.

Meanwhile, Huang et al. (2011) investigate the impacts of citizens’ right proxied by political stability (PS) on banking development and operational efficiency. Their findings show the negative (positive) impacts on bank development and operational efficiency of political instability (stability) in individual countries. Another study by Meon and Weill (2005) suggest that political stability is the perceptions of the government in power, which can be destabilized or overthrown by unconstitutional means. They argue that political stability has a negative (positive) relationship with the macroeconomic technical inefficiency (efficiency).

Chen (2009) studies bank efficiency in Africa’s Sub-Saharan middle income countries. He investigates the factors that influence bank efficiency in view of internal and external determinants. In addition, he additionally examines the impact of political stability (PS) on the efficiency of the banks. The result indicates a positive relationship between political stability and bank efficiency since he discovers that banks enjoy higher efficiency with more political stability.

Chortareas et al. (2012) and Lin et al. (2010) suggest that the citizens’ right proxied by Voice and Accountability (VA) positively influence bank efficiency, while Eisazadeh and Shaeri (2013), Huang et al. (2011), and Chen (2009) suggest that Political Stability (PS) positively and significantly influence bank efficiency.

The majority of the previous studies suggest citizens’ right that measured by i) voice and accountability (VA) and ii) Political stability and absence of violence (PS) plays a main role in influencing bank efficiency since they evidenced a positive relationship between political stability and bank efficiency (Chortareas et al., 2012; Lin et al., 2010; Huang et al., 2011; Chen, 2009). Nevertheless, none of the literature investigates the impact of the citizens’ right on the productivity in





the domestic and foreign Islamic banks. Therefore, this study will cover the gap by examining the influences of citizens’ right on the productivity of the both domestic and foreign Islamic banks in Brunei, Indonesia, and Malaysia.

3. Description of Methodology

3.1. Data Collection Methods

The present study gathers data on the Islamic banks from Southeast Asian countries namely Indonesia and Malaysia, Indonesia from the period of 2006 to 2015. Our primary source of data for the respective banks for the years under study is obtained from the Orbis Bank Focus database (formally known as Bankba database) produced by the Bureau van Dijk. Orbis Bank Focus is a global database that provides financial information on banks’ annual balance sheets and income statements of both listed and non-listed banks, with both conventional and Islamic banks. Data for the empirical analysis of the macroeconomic conditions is sourced from the World Bank (World Development Indicators) and International Monetary Fund (International Financial Statistics databases). To ensure that comparison of the selected banks across the country is consistent, all data are measured in millions of United States Dollars (USD) for the purpose of comparability.

The number of observations is varied across time due to bank entry and exit factor during this period; it gave us an unbalance sample total of 29 Islamic banks yielding 164 bank-year observations. The total observations consist of 17 banks operating in Malaysia, 11 banks operating in Indonesia and one bank operating in Brunei Darussalam, which categorised to 23 domestic ownership banks and six foreign ownership banks

3.2. Input-Output Specification

Since the intermediation approach in specifying the input-output definition and measurement have been widely used in the banking literature (Sealey and Lindley, 1977), we adopted an intermediation approach to reflect the production process of the Islamic banks, which act as intermediaries between depositors and borrowers. Islamic banks can be modelled as multi-product firms, as a majority of banking operations are formed by the conversion of the funds deposited by customers and other financial institutions into credits and other security investments (Oncu and Aktas, 2007). In order to achieve the purpose of our research, two outputs and three inputs were considered to investigate the productivity of Islamic banks in selected Southeast Asian countries for the period of 2007 to 2015. We have determined total deposits (x1), total labour (x2), capital (x3) as inputs, while total loans (y1) and total investments (y2) are treated as outputs.

According to Cooper et al. (2002) and Banker et al. (1989), there is a rough rule of thumb that needs to be met in order to select the numbers of input and outputs used in the analysis. The sample size should satisfy the rule before proceeding with the measurement of the DEA analysis.

n ≥ max {m × s, 3(m + s)}

where,n = a number of Decision Making Units (DMUs)





m = a number of inputss = a number of outputs

Since the total numbers of DMUs in this study are 29 Islamic banks, it is higher than the number of input and output variables 15 = {3 × 2, 3(3 + 2)}. Therefore, the selection of variables is valid as they comply with the rule of thumb, which allows the productivity of DMUs to be measured.

4. Data Analysis Method

4.1. Data Envelopment Analysis

Data Envelopment Analysis (DEA) was initially developed by Charnes, Cooper, and Rhodes (1978) and further extended by Bankar, Charnes, and Cooper (1984), in which it is based on the linear programming approach, which allows for the use of multiple weighted outputs over weighted inputs. In other words, it suggests that the higher the output produced from given inputs, the more efficient the production is.

According to Wu et al. (2006), the DEA is the main approach that is commonly preferred by researchers for the performance analysis of the banking industry, because it allows the efficiency level to change over time and does not require prior assumption on the specification of the best practice frontier. DEA is a most fitting technique to measure the technical efficiencies of those decision-making units (DMUs), which are homogeneous or in the same types of business.

Three basic methods used in the DEA are the Charnes-Cooper-Rhodes Method (CCR), the Banker-Charnes-Cooper Method (BCC), and the Full Cumulative Method. The DEA can be used to derive measures of efficiency ratio by adopting the BCC method, or the Variable Returns to Scale (VRS) model, along with the CCR method or the Constant Returns to Scale (CRS) method. However, Coelli et al. (2005) noted that the BCC model has been chosen by researchers more often than other methods since the beginning of the 1990s.

However, the CRS assumption is only suitable when all institutions are operating at an optimal scale. Constraints on finances or imperfect competition may cause an institution to be operating away from the optimal proportions. Banker, Charnes, and Cooper (1984) introduced an extension of the CRS model to account for VRS.

4.2. Malmquist Productivity Index

The DEA-based Malmquist Productivity Index (MPI) method was initially introduced by Caves, Christensen, and Diewert (CCD) in 1982 and was empirically applied by Färe et al. (1994). The three most frequently used indices to evaluate TFPCH are the Fisher (1922), Tornqvist (1936), and Malmquist (1953) indices. The nonparametric (Malmquist) and parametric (Fischer and Tornqvist) indices vary in their behavioural assumptions and whether they recognise random errors in the data, which are also known as noise. The advantage of applying MPI indices when compared to others is that it does not necessitate prices while eliminating the need for assumptions about the structure of the technology (Estache et al., 2004).





According to Malmquist (1953), total factor productivity is a measurement of changes between total output relative to inputs. Supported by Casu et al. (2004), Jaffry et al. (2007) stated that the output orientation analysis is more suitable given the objectives of developing countries’ banking sectors.

In this study, the productivity changes of banks are measured by using the output-oriented MPI and assign the change in Total Factor Productivity Change (TFPCH) to Technological Change (TECHCH) and Efficiency Change (EFFCH). Changes in EFFCH are attributed to changes in Pure Technical Efficiency Change (PTECH) and/or Scale Efficiency Change (SECH), following Färe et al. (1994) and Fukuyama (1995). The interaction between the efficiency indices is shown below:

Figure 1: Interaction between MPI efficiency indices

According to Färe et al. (1994) an equivalent way of writing MPI index is:

M o ( x t+1 , y t+1 , x t , y t )=Do

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

t ( x t , y t )×[( D o

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

t+1 ( x t+1 , y t+1 ) )×( D ot ( x t , y t )

Dot+1 ( x t , y t ) )]

12

(1)

M is the productivity change between years’ t and t + 1 where most recent production point (x t+1, y t+1) corresponds with the earlier production point (x t, y t) and Ds are output distance functions. Any value greater (lower) than 1.000 will indicate total factor productivity growth (regress) between two periods, whereas value equivalent to 1.000 signals no change.

The relationship between the MPI and its two sub-indices can be written as:

M o=Efficiency Change× TechnicalChange (2)

where,

Efficiency Change=Do

t+1(x t+1 , y t+1)D o

t (x t , yt) (3)

TechnicalChange=[( D ot (xt+1 , y t+1)

Dot+1(x t+1 , y t+1))×( Do

t (x t , y t)

Dot+1(x t , y t))]

12

(4)


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TFPCH

TECHCH EFFCH

SECHPTECH



The efficiency change index can be further decomposed into its mutually comprehensive components of PTECH (∆ Pure Eff t , t+1¿ ,calculated relative to the VRS technology and a component of SECH (∆ Scalet , t+1¿ ,capturing changes in the deviation between the VRS and CRS technologies according to the suggestion of Färe et al. (1994) as shown below:

Efficiency Change=∆ Pure Eff t ,t+1 × ∆ Scalet , t+1 (5)

where,

∆ PureEff t , t+1=DVRS

t+1 (x jt+1 , y j

t+1)DVRS

t (x jt , y j

t ) (6)

∆ Scalet , t+1=DCRS

t+1 (x jt+1 , y j

t+1) /DVRSt+1 (x t+1 , y t+1)

DCRSt ( x j

t , y jt )/ DVRS

t (x jt , y j

t )

(7)

All efficiency scores are restricted to lie between zero and one. Since 2006 is the reference year, the MPI and its components take an initial score of 1.000. Hence, efficiency scores lower (greater) than one for a firm in subsequent years indicate that it is operating below (above) the frontier. The efficiency score reflects the radial distance from the estimated production frontier to the DMUs under consideration.

5. Research Procedure

5.1. First Stage: Data Envelopment Analysis

At this stage, we will measure the productivity level for selected banks by employing a nonparametric DEA method after all the required data were sorted accordingly. The productivity changes of banks are measured by using the output oriented MPI. This study employs the VRS technology to compute TFPCH (Mo) to Efficiency Change (EFFCH) and Technical Change (TECHCH) in the preceding analysis as shown in equation (2).

Next, as suggested by Färe et al. (1994), we proceed with disaggregating the EFFCH into a component of pure technical efficiency change (PTECH) calculated relative to the VRS technology and a component of scale efficiency change (SECH), which captured change in the deviation between VRS and CRS technologies as given in equation (5). The MPI will allow us to measure TFPCH levels between two data points by calculating the ratio of the distances of each data point relative to a common technology.

The indices in MPI analysis are based on a comparison of banks’ ownership types (Domestic – Foreign) and countries, that is, Brunei – Indonesia, Brunei – Malaysia, and Indonesia – Malaysia.





The issue of interest now is whether the difference in the productivity level of the domestic and foreign Islamic banks are statistically significant. The Mann–Whitney (Wilcoxon) is a relevant test for two independent samples coming from populations having the same distribution. The most relevant reason is that the data violate the stringent assumptions of the independent group’s t-test. In what follows, we perform the non-parametric Mann–Whitney (Wilcoxon) test along with a series of other parametric (t-test) and non-parametric Kruskall–Wallis tests to obtain robust results.

5.2. Second Stage: Multivariate Panel Regression Analysis

The multivariate panel regression analysis (MPRA) technique is employed to search for possible correlations between the information gathered from the balance sheet and income statement as well as the macroeconomic data and measures of Islamic banks’ performance.

Then, we continue by using Ordinary Least Squares (OLS) to examine the relationship between banks’ productivity level and bank-specific and macroeconomic condition determinants, in which similar method adopted by several studies (Sufian and Kamarudin, 2015; Banker and Natarajan, 2008; Staikouras and Wood, 2004). As suggested by McDonald (2009), we estimate the OLS regressions by using White’s (1980) method because when White’s heteroskedastic-consistent standard errors are calculated, the tests can be performed for a range of disturbance distribution assumptions in the second stage of the regression analysis.

Nevertheless, before the results are totally based on the pooled OLS estimator method, the Breusch Pagan and Lagrangian Multiplier test need to be execute in order to identify either the data suitable to be pooled or panel. Thus, if the p-value of the Breusch Pagan and Lagrangian Multiplier Chi-Square (X²) is significant at 5% level, the panel is more appropriate than pooled data. Gujarati (2002) mentioned three kinds of advantages in using panel regression. Firstly, panel data make the data more informative with variability, reduce collinearity among the variables, are efficient and give more degree of freedoms to the data. Secondly, panel data could construct better detection and measurement of effects that simply could not be observed in pure cross-sectional or pure time series data. Thirdly, panel data provide the data to be available into several thousand units and this can minimise the bias that might result if individuals or firms level data are divided into broad aggregates.

Gujarati (2002) pointed out several advantages to using panel data that show several estimation and inference problems. Since such data involve both cross-section and time dimensions, problems that plague cross-sectional and time series data (such as heteroscedasticity and autocorrelation) need to be addressed. There exist some additional problems such as cross-correlation in individual units at the same point in time. So, several estimation techniques are used to address one or more of these problems. The two most prominent ones are the Fixed Effect Model (FE) and Random Effect Model (RE). In FE, the intercept in the regression model is allowed to differ among individuals in recognition to the fact that each individual or cross-sectional unit may have some special characteristics of its own. Meanwhile, RE assumed that the intercept of an individual unit is a random drawing from a much larger population with a constant mean value. If it is assumed that the error component β and X’s regressors are uncorrelated, RE may be more suitable, whereas if β and X’s are correlated, FE may be appropriate.

Hausman test can be used to identify between FE and RE. The null hypothesis underlying the Hausman test is that the FE and RE estimators do not differ significantly. The test statistics developed





by Hausman has an asymptotic Chi-Square (X²) distribution. If null hypothesis is rejected (at 1% to 5% significant levels only), the FE may be more appropriate to be used compared to the RE. But, if null hypothesis is failed to reject or is significant at only 10%, the RE is more suitable to be used.

Next, we will examine the potential bank-specific characteristics and condition macroeconomic determinants that influence the Brunei, Indonesian and Malaysian Islamic banking sector’s total factor productivity level. Furthermore, we adopt a step-wise regression model to avoid severe multicollinearity problems. Finally, 12 regression models are estimated to examine the relationship between the total factor productivity level of Islamic banks and the potential determinant variables.

5.3 Description of the Variables Used in Regression Models

This section presents the description of the dependent and independent variables used in the regression models. The variables are used to proxy productivity and its determinants. The independent variables (x-axis) will be the size of the bank, credit risk, capitalization, market power, liquidity, and management efficiency. Table 1 presents the description of the variables used in regression analysis.

Table 1: Variables Description Used in the Panel Regression ModelsVariable/Description

Note

Bank Specific

LNTA lnTA is the natural logarithm of the bank’s total assets in year t. It is a proxy for the bank’s size, which captures the possible cost advantages associated with economies of scale. Positive correlation indicates that a larger bank tends to be more productive compared to medium- and smaller-sized peers due to the benefits obtained from it. Benefits could be an increase in profit margin, higher leverage from financial capital, and better quality of service (Goddard et al., 2004).

lnLLRGL lnLLRGL is the natural logarithm of loan loss provisions over total loans. An indicator of credit risk is how much a bank is provisioning its total loans in year t. The coefficient is expected to have an adverse effect on the performance of the bank due to the potential losses from bad quality loans (Berger and DeYoung, 1997; Mansur et al., 1993).

lnETA lnETA is the natural logarithm of a bank’s capitalisation in year t, calculated as equity over total assets. According to Casu and Girardone (2004) and Sufian (2009), high capital asset ratio is assumed to be an indicator of high profitability due to the lower risk and lower leverage. In contrast, one might expect a negative coefficient in this variable where lower capital ratios exhibit a relatively riskier position (Berger, 1995).

lnBDTD lnBDTD is the natural logarithm of market power. A negative relationship happens when increasing numbers of firms with uniform size raise the degree of competition in the market (Mohammed et al., 2015; Tushaj, 2010). In contrast, the positive relationship of market power may suggest that the substantial market power contributes to the high bank concentration, which will change both loan rates and market shares in imperfectly competitive loan markets (Sufian and





Kamarudin, 2015) lnLOANSTA lnLOANSTA is the natural logarithm of liquidity or loan intensity. It indicates

the percentage of the banks’ assets that are tied up with the loans. According to Sufian (2012), loan intensity is expected to affect performance because bank lending is presuming to be the primary source of revenue. The negative (positive) sign indicates a positive (negative) relationship between banks’ productivity levels with the level of liquid assets held by the banks. In other words, banks with lesser liquidity tend to be more productive. Garza-Garcia (2012) suggested that banks with higher loans are relatively able to perform better and to exhibit progressive performance levels.

lnNIETA lnNIETA is the natural logarithm that provides information on the management efficiency relative to the overhead expenses or costs of running branch office facilities. It is calculated as non-interest expenses. Negative coefficient is expected, because reducing overhead expenses may increase the profitability of the bank (Petria et al.,2015; Akbas, 2012). However, Molyneux and Thornton (1992) found contrasting evidence where the higher expenses in remuneration packages may allow the banks to induce more productive human capital, which can increase the performance of the bank.

Macroeconomics

lnGDP lnGDP is the natural logarithm of gross domestic products rates. Kosmidou (2008) suggested that a positive relationship is expected between the performance of the banks. This determinant implies higher supply and demand for loans and deposits during economic growth. During economic growth, more individual firms will require additional capital to invest in their businesses. However, Sufian and Majid (2009) point out that this determinant may have a negative relationship with banks’ productivity levels. The bank’s productivity levels could be badly affected and result in a negative correlation during economic downturns.

lnCPI lnCPI is the natural logarithm of inflation rate. Perry (1992) pointed out that the effect of inflation on bank performance depends on whether the inflation is anticipated or unanticipated. Under anticipated inflation rates, the interest rates are adjusted appropriately, resulting in a positive impact on profitability while an unanticipated change could raise costs due to low interest rate adjustment, which results in a faster increase in bank costs than bank revenues that consequently have an adverse impact on bank profitability.

WFC World Financial Crisis (WFC) is a dummy variable that takes a value of “1” for the period of the crisis and “0” for other periods. This determinant is included to examine the impacts of the world financial crisis in 2008 on the productivity level of Islamic banks in Brunei, Indonesia, and Malaysia. World financial crisis is expected to exhibit a negative relationship with banks’ profitability (Noor and Ahmad, 2012; Sufian, 2011).

Citizens’ RightlnVA Voice and Accountability is the citizens’ participation in selecting their

government, freedom of expression, freedom of association and free medialnPS ii. Political Stability and Absence of Violence is the stability in the politics but with

the likelihood that the government would be destabilised or overthrown by unconstitutional or violent means, including politically-motivated violence and





terrorismDummy Variable

DO iii. Dummy Ownership (DO): A dummy variable that takes a value of “1” for domestic Islamic banks and “0” for foreign Islamic banks.

5.3. Estimation Method

In this section, the following regression models are estimated, in which the natural logarithm of total factor productivity (lnTFPCH) scores derived from the Malmquist productivity index method will be dependent variables (y-axis) in this analysis.

ln (TFPCH) jt = α + βjt ln(TA) jt + βjt ln(LLRGL) jt + βjt ln(ETA) jt + βjt ln(BDTD) jt

+ βjt ln(LOANSTA) jt + βjt ln(NIETA) jt + β jt ln(GDP) + β jt ln(CPI) + β jt ln(WFC) + βjt (DO)jt + βjt (lnTA*DO) jt + βjt (lnLLRGL*DO) jt + βjt (lnETA*DO) jt + βjt (lnBDTD*DO) jt + β jt (lnLOANSTA*DO) jt

+ β j (lnNIETA*DO) jt + βj (lnGDP*DO) j + βj (lnCPI*DO) j

+ βj (WFC*DO) j + βj (lnVA*DO) j + βj (lnPS*DO) j + ε jt where,

j Observation for bank jt Observation for year tα Constant termβ Vector of coefficientsε jt Normally distributed disturbance termln Natural logarithmTA Total bank assets (size of bank)LLRGL Loan loss reserve over gross loans (credit risk)ETA Equity over total assets (capitalisation)BDTD Bank’s deposit over total deposit (market power)LOANSTA Total loan over total assets (liquidity)NIETA Noninterest expenses over total assets (management efficiency)GDP Gross domestic product (economic growth)CPI Consumer price index (inflation)WFC World financial crisis VA Voice and AccountabilityPS Political Stability and Absence of ViolenceDO Dummy ownership

6. Implication of the Study

The Islamic banking sectors are becoming more competitive, resulting from the more liberalized banking sector. Despite this, they are expected to have continuous growth in the future. The evidence from this research may supports the idea that the productivity level of Islamic banks depends heavily on citizens’ right and also other internal and external determinants. Thus, the related parties such as regulators, bank managers, shareholders or investors, and academicians need to seek proactive approaches that ensure the production and operations to reach its optimal utilization point.





First of all, this study may help the regulators to facilitate action toward a more sustainable competitiveness environment of the Islamic banking sector in the future. The results of this study support the idea that the performance of the banking sector is highly gauged on its efficiency and productivity levels. Therefore, it is reasonable to expect that the regulators could play their role in maintaining a stable and healthy financial system to support the efficient allocation of resources and distribution of risks across the banking industry.

Next, the information of this study could provide guidance for the bank management team of Islamic banks to have a better understanding of the influence of total factor productivity change and the determinants of the productivity of their respective banks. For this reason, the results also provide a direction in which the management should not solely look into the efficiency factors, but also a technological factor to improve the overall performance of their respective banks in future planning. Generally, the technological advancement of Islamic banks compared to conventional banks is still relatively behind. Islamic banks could introduce more comprehensive Internet banking services, mobile applications, or even more convenient services by increasing the unit numbers and functionality of Automated Teller Machines (ATMs).

From the perspective of investors or shareholders, this study may be able to guide those whose primary focus is to gain the highest returns from their investments. Results from this study could help investors to create investment strategies in their investment portfolios. Previously, investors could only focus on the profitability factors of the organization, but now they can also measure the performance of the company, not just regarding profitability, but also productivity because according to theory, productivity will contribute to the profitability of the companies. Therefore, they can also look at the frontier analysis for the measure of company performance. Moreover, investment decisions made by investors would highly influence the level of expected returns from the banks in the future.

Lastly, very few studies have examined the performance level of the banks using total factor productivity, specifically in the Islamic banks from Southeast Asian countries. Therefore, academicians can use the new empirical results from this study to fill these scholarly gaps. There is some argument about using this ratio as a determinant of the performance level. However, the figures can be easily manipulated. Therefore, it is highly recommended that researchers use total factor productivity change in a frontier analysis with a combination of input and output that cannot be manipulated easily. In some cases, after researchers obtain the ratio results, they can increase the reliability of their results by cross checking with the results of total factor productivity.

7. Conclusion

This primary purpose of this study is to provide empirical evidence on the impact of citizens’ right and potential determinants on productivity in domestic and foreign Islamic banks. A nonparametric Data Envelopment Analysis (DEA) based Malmquist Productivity Index (MPI) method is employed to examine the total factor productivity level of Islamic banks in Brunei, Indonesia, and Malaysia during the period of 2006 to 2015. Additionally, we perform a series of parametric (t-test) and nonparametric Mann-Whitney (Wilcoxon) and Kruskal-Wallis tests to examine the impact of citizens’ right and bank-specific characteristics and macroeconomic conditions determinant on the domestic and foreign Islamic banks.





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