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Assignment states the efficiency of Islamic banking from the early 2000’s to the most recent literature on the topic

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EFFICIENCY IN ISLAMIC BANKING

Background

Islamic banking is a form of banking system which operates and runs activities in principles that are founded in Shariah principles (Rosly, 2008). This therefore means that all the transactions that will involve either deposits or financing will all be founded upon Shariah principles (Jalil and Rahman, 2010). What differentiates Islamic banking from the conventional banking is that when transactions are executed, there is no component of Riba’ which is clearly prohibited in the Quran (Ahmad and Hassan, 2007). The principle governing this is founded on the concept of fairness and justice on the interests of the society as a whole (AkramLaldin and Furqani, 2013).

The five countries that have been recognized as having the largest Shariah compliant assets have been Saudi Arabia, Iran, Kuwait, United Arab Emirates (UAE) and Malaysia (IFSI, 2018). In the last few years, a number of countries (e.g. Bangladesh and Indonesia) other than Gulf Council Countries and Middle East and North Africa (GCC MENA) countries have gone in favor of establishing new Islamic banks and also expanding the banking activities of the same. The growth of Islamic banking in the MENA region can be explained as being due to the dependence on oil by the global market and additionally, the higher oil prices which have in turn bolstered the demand for Islamic banking products and also most Muslims in these regions have avoided conventional banking due to the need to become Shariah compliant (Miah & Uddin, 2017).

Problem Statement

Islamic financial institutions have been on the rise over the years as the demand for Islamic financial products has grown (El-Hawary et al., 2004). The major difference between conventional banks and Islamic banks is that, the former works on the principle of charging interest on their deposits while the latter does not allow for interest and follows the profit and loss sharing (PLS) principle while undertaking their duties (Yudistira, 2004). This principle is based on the financial trust relationship between the lender, borrower and the intermediary.

In the face of the considerable development of the Islamic banking industry, there does not exist enough literature that covers their efficiency. Bashir (2001) looked at profitability and banking characteristics in the Middle East while Mokhtar (2008) looked into efficiency and competition in Islamic banking but in Malaysia. If banks are efficient, then there can be an expectation of improved profitability levels, greater funds intermediation and better quality service for the bank customers which will increase the volume of business. Furthermore, the increased efficiency may lead to the economy developing further with proper management and may have a good chance of being sustainable in case of a global financial crisis (Ivanovic, 2016). This has therefore created a gap in the literature examining the efficiency of Islamic banks which is what this study aims to investigate.

Study Objectives and Research Questions.

The study aims to enrich the available empirical literature looking into the efficiency of Islamic banks. This will be done through investigating empirically, the efficiency of the Islamic banks in various Islamic countries. In doing this, this study attempts to answer the following research questions;

Are Islamic banks stable and efficient?

Which Islamic bank is the most efficient?

Justification of Study

The Islamic Financial Services Industry IFSI Stability Report of 2018 provided insights into the global Islamic finance industry and highlighted various trends in the sector. In the banking industry, the report showed that the Islamic banking sector grew in most jurisdictions, with the exception of Qatar, and Kuwait was the 4th largest market commanding a 6% share of the global market. The growth rates of the assets in other jurisdictions, achieved double digit growth rates in assets and deposits (IFSI, 2018). 

The report also showed that the sector has been resilient and majority of its stability indicators were in compliance with the international regulatory requirements. This means that Islamic banks are becoming competitive in the global scale. To be able to maintain this competitiveness, the banks therefore need to understand their efficiency in terms of their operations to ensure that this growth can be sustained into the future (Ariff and Rosly, 2011). This study will attempt to look at the efficiency of Islamic banks and based on the findings, be able to provide recommendations to the different stakeholders as to how to improve the efficiency of their banking operations to make them competitive. Additionally, the insights provided by this study will serve to inform policy that will aid in the regulation of the activities of Islamic banks and how these policies can enable the expansion of the Islamic banking industry to comparable levels with conventional banking. This is of paramount importance since the spread of Islamic banks vary in different MENA countries. It will help banks understand how to operate in the post crisis market and how their efficiencies will change based on whatever factors will be found to be of importance to driving the different kinds of efficiency. 

LITERATURE REVIEW

The literature that exists on efficiency in Islamic banking has been growing considerably over the last few years (e.g. Bashir, 2001; Yudistira, 2004; Majid and Saal, 2010). This is due to the increasing competition among the players in the banking industry since the arrival of Islamic banking and this growth has directed the interest of policy makers and researchers alike into finding a way to assess the efficiency of banks in the industry (Sahut et al., 2011). Studies looking into the efficiency of banks have looked into various types of efficiency; however, the two most commonly looked at types of efficiency are technical efficiency and scale efficiency (Sufian, 2007; Rosman et al., 2014). Technical efficiency looks at maximising the output of a bank given a certain level of input while scale efficiency looks into minimizing the input of a bank to achieve a certain output level for the bank (Kamaruddin et al., 2008). The other types of efficiency include cost efficiency, profit efficiency and revenue efficiency (Sufian et al., 2009). 

One of the earlier studies conducted over the last decade is the one by Bashir (2001) who conducted a regression analysis to find the determinants that influenced the Islamic performance of banks in the Middle East by using bank level data. His findings showed that the bank performance, as measured by profits, was mostly driven by overhead, non-interest earning assets and short term funding from customers. He further asserted that since deposits in Islamic banks are treated as shares, the reserves that banks held exerted negative impacts on them like the reduction of the amounts of funds available to make investments hence affecting performance.

Yudistira (2004) looked into the performance of 18 Islamic banks from the London-based International Bank Credit Analytics Scope Database over a 4 year period of 1997-2000 and measured efficiency through the non-parametric Data Envelopment Analysis technique to look into the technical and scale efficiencies of Islamic banking. His study found that the banks had slight inefficiencies in the global crisis period of 1998-1999 and these were mostly determined by country specific factors. Majid and Saal (2010) investigated the efficiency of a sample of banks in ten countries which were Islamic and they used an output distance function technique. They allowed for environmental influences and bank size and international differences. Their study found that banks can benefit from increased scale and found inefficiencies were affected by these factors differently and their conclusion was that this will be a big challenge for the Islamic banking sector for the next years to come.

Bader et al (2008) looked into a sample of 80 banks from 21 countries. They looked at the profit, cost and revenue efficiencies while using 3 inputs namely fixed assets, labor and funds while also looking at 3 outputs namely off balance sheet items, total loans and other earning assets. Their results showed that smaller banks are less efficient as compared to larger banks and that the newer banks to the market were more revenue efficient but older banks were more profit and cost efficient. The study also found that Islamic banks in the Middle East were more efficient compared to those in Asia and Africa. 

Beck et al. (2010) found Islamic banks to be more efficient when looking at a sample in countries that had both Islamic and conventional banks. The period was 1995-2009 and the sample size spanned 22 countries covering 510 banks of which 88 were Islamic. Their findings show that Islamic banks are less efficient and had higher intermediation ratios which mean that they are well capitalised compared to conventional banks. They found that this better capitalisation is what helped Islamic banks perform relatively better during the financial crisis. However, the study by Shah et al. (2012) looked into the performance of Islamic banks compared to the conventional banks and found that Islamic banks had a better performance when it came to technical efficiency using the loan base approach. The income based approach also showed that Islamic banks had a better performance.

Almumani (2014) applied the DEA approach to look into the relative efficiency of Islamic banks and he looked at the bank size and capitalisation as the main determinants of efficiency. The findings showed that the banks in Saudi were efficient when it came to the management of financial resources and that smaller banks achieved a higher score compared to large and medium sized banks. Sillah et al. (2014) also looked into the efficiency of Saudi banks using a sample of 12 banks. They adopted a stochastic frontier model over a 12 year period and found that when it came to income efficiency, Samba bank and Al-rajhi had better performance and they also found that among foreign owned Saudi banks, Banque Saudi Fransi was the most efficient.

Ferhi&Chkoundali (2015) looked into the efficiency of Islamic and conventional banks over an 11 year period starting 1999 using a sample of 209 banks in total. The DEA approach alongside the Stochastic Frontier Analysis (SFA) was used to assess efficiency and the DEA approach found Islamic banks to be more efficient with banks from the regions such as Qatar, Egypt, Turkey and Bahrain being found to be the most efficient. The SFA findings showed that Islamic banks were less efficient when compared to conventional banks but found that banks in the Arab region had the highest cost efficiency scores. On the other hand, the study by Shawtari (2015) looked into the Islamic banking industry in Yemen adopting the two stage DEA approach to determine efficiency over the period 1996-2011.  The findings showed that the banking industry in the country was on a declining trend with Islamic banks being more efficient than conventional banks. Their findings also showed that the efficiency was affected by loans and profitability as the main determinants and the other determinants affected each type of banks differently depending on the uniqueness of the structure and operation of the bank.

Farandy et al. (2017) measured the efficiency of Islamic banks in Indonesia for the period 2011-2014 using a sample of 10 bank and measured efficiency using the two stage DEA approach. The nonparametric approach was used and the Tobit model in the second stage. The efficiency of Islamic banks was found to be around 91.8% efficiency. This indicates a level of inefficiency; however, they found the banks were able to optimise their inputs well to give the desired output. The Tobit model used the capital adequacy ratio, the total assets, number of branches, non-performing loans and return on assets as its inputs.

Bahrini (2017) evaluated technical efficiency of the Islamic banks that are in the MENA region using the bootstrap DEA approach and found that banks in the Gulf Cooperative Council (GCC) area had a more consistent financial performance and efficiency as compared to the Islamic banks in the Mena region. However, Miah & Uddin (2017) looked into the efficiency of Islamic banks and conventional banks in the GCC having a sample of 28 and 48 respectively for the period 2005-2014. Their study used an SFA approach and also they used accounting ratios and their findings show that Islamic banks were less efficient compared to conventional banks but when it came to stability, Islamic banks were more stable in the short term. 

Kamarudin (2018) looked at Islamic and conventional banks that operated in Qatar, Oman, Saudi, Kuwait, UAE and Bahrain and applied a DEA approach to find the revenue efficiency between the two types of banks. The results showed that revenue efficiency was improved by factors such as regulatory quality, corruption control, political stability among other factors related to government effectiveness.  

Khan et al. (2018) conducted their study on Saudi banks for the period 2008-2016 using non-parametric DEA while looking at results from the individual bank level compared to the industry. Their study found that when it came to the expansion of banks to achieve market share, both Islamic and conventional banks were successful in achieving technical and scale efficiency. Al Rahji bank was the bank that had the best technical, scale and pure efficiency at the individual level while Saudi Hollandi was the top for commercial banks. The study made a recommendation of improving the branch level management skills by focusing their short and long term marketing strategies in that area.

Summary of Chapter

This chapter looked into various empirical research that has been done into the efficiency of Islamic banking from the early 2000’s to the most recent literature on the topic. Some of the studies have shown Islamic banks to be efficient while others have found it not to be efficient based on the technical and size efficiency.

RESEARCH METHODOLOGY

To measure the efficiency of banks, the study will adopt the non-parametric Data Envelopment Analysis (DEA) (AlKhathlan and Malik, 2010). DEA is a linear programming methodology for looking at how a specific decision making unit (DMU), which will be bank in our study, operates in relation to another bank that is contained in the sample under investigation (Favero and Papi, 1995). It works by making a frontier set of the efficient banks and then comparing it with that of inefficient banks to come up with accuracy scores. Moreover, banks are bounded between the scores of 0 and 1, with 1 being the score for a completely efficient bank. In this technique, the most efficient bank does not automatically give a maximum output level from a given set of input variables but rather creates the best practical level of output compared to other banks in the sample.

This technique was first coined by the Charnes et al. (1978) while building upon the study by Farell (1957). Thereafter, the technique has been used extensively when dealing with banking literature. It has been used most notably in studies such as those by Miller and Noulas (1996) and also the study by Berger and Mester (2001). Additionally, it was also used as the tool to evaluate the benefit the European Economic Community to the banking sector.

This approach creates a frontier of the ratios that have been observed from the input and the output by using linear programming techniques. DEA assumes that for n DMUs within the banking sector, all of the sample inputs and outputs are characterized by n and m respectively. The computation of the efficiency of each bank in the sample is then calculated as:

e_s=(∑_(i=1)^m¦?u_i y_is ?)/(∑_(j=1)^n¦?v_j x_js ?),for i=1,…,m and j=1,…,n

Where the y_isis the amount of the ith output of the sth bank and x_is is the amount of the jth input in the sth bank. u_iis the weight of the output while v_j is the weight of the input. The calculated e_s is then maximized to get the optimal weights subject to the conditions;

(∑_(i=1)^m¦?u_i y_ir ?)/(∑_(j=1)^n¦?v_j x_jr ?),≤1 for r=1,…,N and u_i  and v_j  ≥0

With the first inequality being one to make sure the efficiency ratios to be at most 1 and the second to ensure the weights will all be positive.

These can then be transformed to ordinary linear program and also can be converted to a dual program which assumes constant returns to scale where the solution can be looked at as a frontier.

Data

The data will be a panel dataset which will be extracted from the financial statements and balance sheets of the Islamic banks in various Islamic countries for the period of 2014-2018 which will be made available through Thomson Reuters One database alongside the Morningstar database. The variables will be converted from dinar to US dollars using the end of year exchange rate for each year of study. The exchange rate statistics will be obtained from the International Financial Statistics.

Inputs and Outputs

In Islamic banks, the capital structure is an equity-based structure due to the domination of the equity of the shareholders and their investment deposits that are gotten from the PLS sharia Principle (Muljawan et al., 2002). This therefore means that the return of capital for the banks will be calculated ex-post. This therefore implies that when looking at the inputs and outputs for Islamic banking, more care has to be accorded to the process and this study will adopt an intermediation approach where the DEA model will consist of 3 inputs and 3 outputs. The study adopted an intermediation approach due to the main characteristic of Islamic banks of being joint stock companies where shares are easily tradable and transferable (Dar and Presley, 2000). This is why capital and labor are sued in the intermediation of loans and deposits.

To find the overall technical efficiency, we will estimate an OLS model of the form:

ε_s= α+β_1 KA_(s,t)+?+β_6 P?UB?_(s,t)+?_(s,t)

Where the ε_s represents overall technical efficiency while the coefficients will be for each input and output for the sth bank with ?_(s,t) being the error term.

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