Efficiency and Performance of Conventional and Islamic Banks in GCC Countries





Lawrence Taii

Correspondence:
Lawrence Tai, PhD, CPA
Professor of Finance
Zayed University
PO Box 144534, Abu Dhabi
United Arab Emirates
Email: Lawrence.Tai@zu.ac.ae

Abstract

This paper examines the efficiency and performance of 58 publicly listed conventional and Islamic national banks in the Gulf Cooperation Council (GCC) countries between 2003 and 2011. A translog cost function is used to evaluate the efficiency of the GCC banking sector and multiple regression analysis is employed to identify factors affecting the performance of the 58 national banks. Empirical findings reveal that Masraf Al Rayan of Qatar (an Islamic bank) was the most efficient bank while Kuwait Finance House (also an Islamic bank) was the least efficient bank during the study period. Conventional banks were more profitable, liquid, and solvent than Islamic banks during the earlier years of the study period while Islamic banks were more profitable, liquid, and solvent than conventional banks during the later years of the study period. Regression results indicate that economic conditions, bank size, financial development, operating costs, and type of bank (conventional or Islamic) are significant variables affecting return on average assets.




Introduction

This paper examines the efficiency and performance of 58 publicly listed conventional and Islamic national banks in the Gulf Cooperation Council (GCC) countries between 2003 and 2011. GCC has six member states: Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UAE).

The banking sector is crucial to the development of any economy; it is also one of the major driving forces of economic growth in developing countries. Banks are special financial intermediaries whose operations are unique in financial markets and impact strongly on an economy. Hence, research on efficiency and performance of the banking sector has important policy implications. A higher degree of efficiency and performance in banking markets is expected to provide welfare gains by reducing the prices of financial services and thereby accelerating investment and growth.

The objective of this paper is to study the efficiency and performance of conventional and Islamic banks in GCC countries. As commercial banks play a vital role in the financing of an economy, banking efficiency exerts an important impact on a country's economic development. Bank performance has been a key issue particularly in developing countries as commercial banks are the dominant financial institutions in these countries and they represent the major source of financial intermediation. Evaluating their efficiency and performance is crucial to depositors, owners, potential investors, managers, and regulators.

GCC Economic Review
The GCC is an oil-based economy with the largest proven crude oil reserves in the world. This region ranks as the largest producer as well as exporter of petroleum. The economies of GCC countries have been growing very rapidly during the 2000-2008 period. This spectacular economic boom came to a sudden halt in 2009 after the emergence of the global financial crisis in late 2008. Growth resumed in 2010.

Table 1 displays the real GDP growth rate for the six GCC members and three industrialized countries between 2000 and 2011. Compared to the world's three most industrialized countries, the GCC economic growth has been very impressive and all six GCC countries outperformed the three industrialized countries. In particular, Qatar's economic growth has been phenomenal, with double-digit average growth rate over this period.

Table 1: Real GDP Growth Rate (%), 2000-2011


Source: Knoema.com

Given the volatility in oil prices, the GCC countries have realized that economic diversification is the only feasible way forward to create long-term, sustainable growth. They have focused their development on industries such as tourism and financial services. As a result, the GCC banking industry is gearing up for change in a region that is accelerating its growth.

Table 2 shows the number of publicly listed national banks for each GCC country at the end of 2011. UAE has the largest number of conventional and Islamic banks. There is no Islamic bank in Oman.

Table 2: Number of Public National Banks by Country and Type, 2011


Source: Gulfbase.com

Table 3 presents four selected banking indicators for GCC banks for 2011. Saudi Arabian banks have the highest average total assets, average total deposits, average loans and advances, and average capital and reserves, while Bahraini banks have the lowest.

Table 3: Selected Banking Indicators
(End of 2011 average per bank, in billions of USD)


Source: Central Bank Statistical Bulletins, 2012.

Literature Review
A large number of studies have been conducted in measuring efficiency and performance in the banking industry. In the literature, two major approaches have been taken to measure efficiency in the banking industry: parametric and nonparametric. Nonparametric approaches like data envelopment analysis (DEA) consider the whole distance from the frontier as inefficiency. These methods are therefore deterministic as they do not include the possibility of measurement errors in the estimation of the frontier and hence they may overestimate the inefficiencies. DEA approach has been used by Ozkan-Gunay & Tektas (2006) to study the efficiency of the Turkish banking sector, by Chang & Chiu (2006) to examine the efficiency of Taiwan's banking industry, and by Fitzpatrick & McQuinn (2005) to investigate the efficiency of UK and Irish credit institutions, just to name a few.

Parametric approaches such as the stochastic frontier approach (SFA) and the distribution-free approach (DFA) do not suffer from the above-mentioned drawback. SFA makes some distributional assumptions to disaggregate the residual from the frontier into an inefficiency term and a random disturbance, which are arbitrary. SFA has been used by Inui, Park, & Shin (2008) to study the comparative efficiency of Japanese and Korean banking and by Fitzpatrick & McQuinn (2005) to investigate the efficiency of UK and Irish credit institutions. DFA has been proposed to resolve the major criticism of the SFA, namely its distributional assumptions, by adopting more intuitive assumptions to separate inefficiency from random disturbance. DFA has been used by Matousek, R. & Taci, A. (2004) and by Pruteanu-Podpiera, Weill, & Schobert (2008) to examine the efficiency of the Czech banking industry.

Using regression, Al-Tamimi (2010) investigated the factors affecting the performance of UAE Islamic and conventional national banks during the 1996-2008 period. His results indicate that liquidity and concentration were the most significant determinants of conventional banks' performance while cost and number of branches were the most significant determinants of Islamic banks' performance (measured by return on assets and return on equity).

Ika & Abdullah (2011) compared the financial performance of Islamic and conventional banks before and after the enactment of Indonesia's Islamic Banking Act of 2008. They found no major difference in the financial performance between the two types of banks, except in liquidity where Islamic banks were generally more liquid than conventional banks.

Fayed (2013) compared the performance of Islamic and conventional banks in Egypt. His findings indicate that conventional banks dominated Islamic banks in profitability, liquidity, credit risk management as well as solvency.

A number of studies have been conducted in identifying factors affecting the performance of Islamic and conventional banks in Pakistan (Jaffar & Manarvi, 2011; Hanif, Tariq, Tahir, & Momeneen, 2012; Sehrish, Saleem, Yasir, Shehzad, & Ahmed, 2012; Usman & Khan, 2012). Jaffar & Manarvi found that Islamic banks performed better in possessing adequate capital and better liquidity position while conventional banks had better performance in management quality and earnings ability. Hanif, et al concluded that in terms of profitability and liquidity conventional banks led, while in credit risk management and solvency maintenance Islamic banks dominated. Sehrish's findings indicate that Islamic banks were less risky in terms of dealing in loans and less efficient in expense management as compared to conventional banks. Usman and Khan's results show that Islamic banks had higher growth rate, profitability, and liquidity power than conventional banks.

Previous research were almost exclusively single country studies that examined either bank efficiency or performance. This paper is the first attempt to investigate the efficiency and performance of conventional and Islamic national banks in GCC countries. DFA is applied to measure efficiency and multiple regression analysis is used to examine factors affecting bank performance.

Methodology
This paper has two objectives. The first objective is to evaluate the efficiency of the GCC banking sector during the 2003-2011 period. A translog cost function is estimated for all the banks in the sample. Each bank's efficiency is then computed as the deviation from the most efficient bank's intercept term. The second objective is to determine the factors affecting the performance of GCC conventional and Islamic banks.

Measurement of Efficiency: the Distribution-Free Approach
The distribution-free approach (DFA) is used to provide evidence on the level of banking efficiency in the GCC. Using a fixed-effects model, inefficiency is estimated from the value of a bank-specific dummy variable. The following translog cost function is estimated for all the banks in the sample:


The above translog cost function has one output (loans, y) and three input prices (labor, physical capital, and borrowed funds). The price of labor is measured by the ratio of personnel expenses to total assets (w1). The price of physical capital is defined as the expenses for physical capital to fixed assets (w2). The price of borrowed funds is defined as the ratio of interest expense to borrowed funds (w3).

The DFA approach is applied and it is assumed that the difference in the actual and predicted cost for a given cross-sectional period is a combination of persistent inefficiency component and a random component (Berger, 1993). It is possible to obtain the persistent inefficiency component by averaging out these differences over time. Following Hunter and Timme (1995), the error term bank i in time t can be expressed as:

where ln(vi,t) is a random error component that varies with time and is distributed with a zero mean over time, and ln(ui) is the core efficiency or average efficiency for each bank which is time-independent while random error tends to average out over time. In order to be consistent with this error term specification, the cost function can then be expressed with a residual in the multiplicative form:

Costi,t = Ct(Qi,t,Pi,t)vi,t,ui,

where Ct is a cost function and Qi,t and Pi,t are output and input prices, respectively. This cost function in logarithm is:

1nCosti,t = 1nCt(Qi,t,Pi,t) + 1n(vi,t) + 1n(ui).

The term 1n(ui) is assumed to be orthogonal to the regressors in the cost function. The error term i,t can be estimated for each bank for each year. In this way the parameters in the cost function and the random error term 1n(vi,t) are allowed to change for each year while 1n(ui) remains constant over time.

The next step is to average the estimated cost function, error term i,t for each bank over n years in order to obtain an estimate of 1n(ui), that is 1n(ui) =

For each bank then the percentage efficiency measure can be expressed as:

EFFi = exp[1n(umin) – 1n(ui)],

where 1n(umin) is the minimum value of 1n(ui). From this formulation an efficiency value of 1 corresponds to the most efficient bank while all other banks have values between 1 and 0.

Factors Affecting Bank Performance
Profitability is one of the widely used indicators to measure the performance of any business. Financial ratios used in this study for measuring a bank's profitability are: return on average assets (ROAA), return on average equity (ROAE), and cost to income (C/I) ratio. For ROAA and ROAE, the higher the ratio, the better is the bank's performance. For cost to income ratio, the lower the ratio, the better is the bank's performance.

Maintaining adequate liquidity is one of the major challenges that banks face. Liquidity ratios measure a bank's ability to meet its short-term obligations. In this study three liquidity ratios are used: net loans to total assets (NL/TA) ratio, liquid assets to customer deposits and short-term funding (LA/DSF) ratio, and net loans to total deposits and borrowings (NL/TDB) ratio. The higher the NL/TA ratio, the lower is the bank's liquidity. The higher the LA/DSF ratio, the more liquid is the bank. The higher the NL/TDB ratio, the higher is the chance that the bank faces liquidity risk.
Solvency is the ability of a bank to have enough assets to cover its liabilities. A bank is insolvent if its equity is negative. The first ratio used to gauge solvency is equity to total assets (E/TA) ratio. The higher the E/TA ratio, the larger is the bank's capacity to absorb loan losses. The second ratio used is equity to net loans (E/NL) ratio. The higher the E/NL ratio, the larger is the bank's capacity to absorb loan losses. The third ratio used is impaired loans to gross loans (IL/GL) ratio. The lower the IL/GL ratio, the better is the bank's credit quality and the lower is its loan losses.

To examine factors affecting bank performance, the following multiple regression model is used:

PERF = b0 + b1GDP + b2TA + b3FD + b4LIQ + b5CON + b6SAL +

where


Two controlled variables are used in the regression model. Since larger banks might have enjoyed scale or scope economies that had positive effects on their performance, the size of banks in terms of assets (scale) is used to control for bank size. In addition, as business cycle might also affect bank performance, GDP per capita is used to control for macroeconomic conditions.

Before running the regression, a multicollinearity test is used to examine the degree of correlation among the explanatory variables. Table 4 shows the pairwise correlations. As the highest correlation is only 0.41, the explanatory variables are not multicollinear.

Table 4: Correlation Coefficients of Explanatory Variables


Data
The sample consists of 58 GCC conventional and Islamic banks listed in the respective stock exchanges during the 2003-2011 period. All the required data are extracted from the annual reports of the banks and the Statistical Bulletin of the central banks.

Empirical findings
To explore bank efficiency, the panel data for all national banks that operated throughout the whole study period is used. The DFA approach is employed to calculate the efficiency scores of the banks. As shown in Table 5, Masraf Al Rayan of Qatar was the most efficient bank while Kuwait Finance House was the least efficient bank during the study period. It is interesting to note that both are Islamic banks.

Table 5: Efficiency of Islamic Banks, 2003-2011



Table 6 and Figures 1-3 show the profitability ratios of conventional and Islamic banks between 2003 and 2011. In general, all three ratios indicate that Islamic banks were more profitable than conventional banks during the 2005-2009 period.

Table 6: Profitability Ratios of Conventional versus Islamic Banks

Figure 1: Return on Average Assets (ROAA) of Conventional versus Islamic Banks



Figure 2: Return on Average Equity (ROAE) of Conventional versus Islamic Banks



Figure 3: Cost to Income (C/I) Ratio of Conventional versus Islamic Banks


Table 7 and Figures 4-6 present the liquidity ratios of conventional and Islamic banks between 2003 and 2011. In general, all three ratios reveal that Islamic banks were more liquid than conventional banks for the 2006-2011 period.

Table 7: Liquidity Ratios of Conventional versus Islamic Banks



Figure 4: Net Loans to Total Assets (NL/TA) Ratio of Conventional versus Islamic Banks


Figure 5: Liquid Assets to Deposits and Short Term Funding (LA/DSF) Ratio of Conventional versus Islamic Banks



Figure 6: Net Loans to Total Deposits Borrowings (NL/TDB) Ratio of Conventional versus Islamic Banks



Table 8 and Figures 7-9 display the solvency ratios of conventional and Islamic banks between 2003 and 2011. In general, all three ratios indicate that Islamic banks were more solvent than conventional banks during the 2006-2011 period.

Table 8: Solvency Ratios of Conventional versus Islamic Banks


Figure 7: Equity to Total Assets (E/TA) Ratio of Conventional versus Islamic Banks

Figure 8: Impaired Loans to Gross Loans (IL/GL) Ratio of Conventional versus Islamic Banks



Figure 9: Equity to Net Loans Ratio (E/NL) of Conventional versus Islamic Banks

Table 9 presents the results for the two regression models using return on average assets (ROAA) and return on average equity (ROAE) as the dependent variable. The regression results are better when ROAA is used as the dependent variable. GDP per capita, total assets, M2 to GDP, salaries to total assets, and type of bank (conventional or Islamic) are significant variables affecting ROAA.

Table 9: Regression Results


* Significant at 10% level.
** Significant at 5% level.
*** Significant at 1% level.

Conclusion
In this study, we used a sample of 58 publicly listed GCC national banks to examine the efficiency and performance of the GCC banking sector between 2003 and 2011. The results indicate that Masraf Al Rayan of Qatar was the most efficient bank (an Islamic bank) while Kuwait Finance House (also an Islamic bank) was the least efficient bank. Conventional banks were more profitable, liquid, and solvent than Islamic banks during the earlier years of the study period whereas Islamic banks were more profitable, liquid, and solvent than conventional banks during the later years of the study period. Regression results indicate that economic conditions, bank size, financial development, operating costs, and the type of bank (conventional versus Islamic) are significant variables affecting ROAA.

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