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Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis
Accepted Manuscript Title: Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis Author: Dennis Olson Taisier Zoubi PII: DOI: Reference:
S1062-9769(16)30041-2 http://dx.doi.org/doi:10.1016/j.qref.2016.06.013 QUAECO 950
To appear in:
The
Received date: Revised date: Accepted date:
3-1-2016 26-6-2016 29-6-2016
Quarterly
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Finance
Please cite this article as: Olson, D., and Zoubi, T.,Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis, Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.06.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
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Dennis Olson (Corresponding author) Department of Economics and Finance Gulf University for Science & Technology Block 5, Building 1 Mubarak Al-Abdullah Area/West Mishref Kuwait [email protected]
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Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis
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Taisier Zoubi Department of Accounting School of Business Administration American University of Sharjah P.O. Box 26666 Sharjah, UAE +9716 515-2367 [email protected]
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Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis Abstract
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This study examines whether the Global Financial Crisis (GFC) has led to a convergence in performance between Islamic and commercial banks in the Middle East, Africa, and Southeast Asia (MENASA) region in recent years. Using the largest sample to date for 1996 - 2014, we find that Islamic banks (IBs) initially weathered the onslaught of the GFC better than commercial banks (CBs) in 2007-2008. Then, as the crisis spread to the real economy in 2009, profitability declined substantially for IBs relative to CBs. Beta and sigma convergence tests suggest convergence toward the mean for all banks and all financial ratios. The speed of convergence is generally slower for Islamic banks but the difference has declined in the aftermath of the GFC. The recently developed more robust Phillips and Sul (2007a) log-t test for convergence shows little convergence over the whole sample period, but for the years 2010-2014, all banks appear to be converging toward similar levels of profitability as measured by ROA and ROE. The log-t test shows convergence in profitability across all banks (IBs and CBs) in the post-crisis period. However, it does not show convergence across all asset composition and risk measure—meaning that IBs and CBs still operate differently even if they are moving toward similar profitability results. Club convergence results indicate a lack of convergence over the whole sample, but quite strong convergence across all banks post-crisis. However, some clusters, such as the Southeast Asia region does not display convergence in profitability ratios—suggesting that the GFC has differentially impacted various countries and regions.
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JEL Classification: G21, G15 Keywords: Islamic and commercial banks, bank performance, financial crisis, convergence tests, speed of adjustment
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Highlights
We examine whether the Global Financial Crisis has led to a convergence in performance between Islamic and commercial banks in the Middle East, Africa, and Southeast Asia.
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We test for beta and sigma convergence of bank financial ratios and apply the Phillips and Sul (2007a) log-t test to check for convergence.
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Loan specialization, securitization, and stock market listing are positively related to bank profitability for both IBs and CBs, while cost inefficiency is negatively related to profitability. Capitalization is positively related to return on assets, but negatively related to return on equity.
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Our beta and sigma convergence tests suggest that IBs and CBs are both converging toward the mean for all banks in the MENASA region over a range of financial ratios during the period 1996-2014. CBs generally display faster convergence than IBs— particularly for variables such as ROA. However, differences in speed of adjustment between IBs and CBs have generally lessened in the post-crisis years.
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The log t test shows convergence across all banks in terms of profitability (ROA and ROE) and ROE--to a greater extent in the aftermath of the Global Financial Crisis. However, IBs are not converging toward the MENASA mean across all variables, which shows there are still some operational differences between IBs and CBs.
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1. Introduction During the last decade, the Islamic banking sector has become one of the fastest growing
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industries world-wide. There are currently more than 300 Islamic banks (IBs) operating around the world with total assets worth more than $1.50 trillion US dollars. These banks will likely
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continue to grow at a fast pace in countries where they are presently operating due to increases in the Muslim population, which is projected to reach 2.3 billion by 2030, up from 1.7 billion in
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2014. Also, even though Islamic finance has not significantly penetrated the banking markets in
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Western countries, serving the banking needs of the sizeable Muslim populations in these nations represents an important opportunity for future growth.
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In general, IBs are governed by Sharia rules and principles that lead to five important differences between the operations of Islamic and commercial banks (CBs).1 (1) The most
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important characteristic of IBs is the prohibition of both the receipt and payment of interest
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(riba) in all transactions. (2) Secondly, although IBs obtain funds primarily by issuing equity
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and receiving customer deposits, CBs can also issue debt instruments to finance their investment activities. (3) A third tenet of Islamic finance is the principle of risk sharing on both sides of the balance sheet. To raise funds, IBs use profit sharing (mainly Mudarabah-based) arrangements rather than conventional deposits. On the asset side, in place of conventional loans, IBs issue exchange-based contracts (Murabaha), lease-based contracts (Ijarah and Istisna), and profitsharing contracts (Mudarabah and Musharka). Although the profit/loss sharing contracts may
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Commercial banks represent about 80% of the bank-year observations available in Bankscope for “conventional” banks in the Middle East, North African and Southeast Asia (MENASA) region. Data for bank holding companies, coop banks, investment banks, microfinance institutions, multi-lateral government banks, central banks, real estate banks, credit institutions, and investment and trust corporations are not included in our analysis because these specialty financial institutions often operate differently than full-service commercial and Islamic banks.
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embody the central tenets of Islamic finance better than the exchange-based or lease-based products, IBs have been cautious in relying upon Mudarabah and Musharka contracts due to their higher perceived risk.2 (4) The fourth major difference between IBs and CBs involves
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restrictions on the uses of funds. CBs face few investment constraints except for the need to meet desired levels of return and risk. In contrast, IBs are limited in the types of investments they
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can offer to customers due to Sharia-imposed Islamic restrictions. For example, IBs may not
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invest in haram activities, such as gambling, alcohol production or distribution, gambling, etc. IBs are also precluded from undertaking many types of potentially rewarding, but excessively
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risky investments. This restriction follows from the Islamic prohibition on gharar, which precludes gambling and most types of speculation. As a result, IBs facing a restricted set of
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investment opportunities may be more prone to risk concentration and may be less flexible in
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responding to changing economic conditions than CBs.3 (5) Finally, internal corporate
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governance structure adds to the differences between CBs and IBs since the latter need to establish a “Sharia Committee” consisting of experts in Islamic law whose role is to ensure that
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IBs’ activities are in compliance with Sharia. As a result, IBs exhibit a “multi-layered” corporate governance structure that includes the Sharia Committee in addition to the regular board of directors. The Sharia Committee restrains the board of directors and the management of the bank from taking excessive risk, such as creating credit against credit (which would be in A more detailed description about the specifics of these Islamic financial products is available in the Appendix. The interested reader is also referred to Abedifar et al. (2013) for a thorough discussion of the principles of Islamic banking and finance. 3 For instance, the cost plus contract (Murabaha), widely utilized by IBs, is based on the sale of a physical asset to a customer on a cost plus profit basis (e.g., a car purchase financed through a sale to the customer after the bank purchases the car). Profit on the sale is fixed at inception. Unlike CBs, IBs cannot profit from changes in the underlying inter-bank offer rates. However, IBs can hold Sukuk and make similar profits to CBs. 6 2
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violation of riba), or undertaking doubtful investments such as sale of goods which are not present at hand (which would be in violation of gharar). Mollah and Zaman (2015) show that Sharia supervisory boards have a positive impact on Islamic bank profitability, while Sharia
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boards that merely serve in an advisory capacity have a negligible impact on performance.
Against this background, an exacerbated debate on the comparative performance of IBs
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and CBs has emerged during the recent financial crisis. Academic and anecdotal evidence
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suggest that IBs weathered the onslaught of the Global Financial Crisis (GFC) in 2007-2008 better than CBs [Bourkhis and Nabil, 2013; Alqahtani et al. (2016)]. Khan and Crowne-
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Mohammed (2009-2010) and Hassan and Dridi (2010) argue that Islamic banking principles (particularly investment restrictions that prohibited IBs from holding derivatives and other
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potentially “toxic” assets) protected IBs during the GFC, while Čihák and Hesse (2010), Rajhi
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(2013), Bourkhis and Nabi (2013), and Mobarek and Kalonov (2014) show that IBs (particularly
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small IBs) in the Middle East, Africa, and Asia are more financially stable than CBs. Nevertheless, Rashwan (2010) notes that the superior performance of IBs during the initial
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financial phase of the GFC shifts to under performance in 2009 as the financial crisis spread to the real economy. For the years 2007 – 2009, Bourkhis and Nabi (2013, p.68) find “no significant difference in terms of the effect of the financial crisis on the soundness of Islamic and conventional banks.” A comparative study conducted by Olson and Zoubi (2008) establishes that while IBs and CBs in the Middle East operated similarly, financial ratios used in nonlinear classification models could effectively help to distinguish between the two types of banks. In the aftermath of the GFC, competitive banking conditions could cause a further convergence between IBs and CBs. For example, Escribano and Stucchi (2014) found that
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recessions lead to productivity convergence across Spanish manufacturing firms by creating symmetric incentives for less productive firms to become more competitive. The issues of comparative performance and whether there are significant differences between the business
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orientation of IBs and CBs can be formally addressed by adopting techniques used in the recent literature on economic convergence. Three commonly used tests for convergence and economic
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integration include tests for beta, sigma, and log t-tests of convergence. Results from tests of
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banking sector integration in Europe provide mixed results for what to expect for IBs and CBs in the MENASA region. Weill (2009) and Casu and Girardone (2010) find beta and sigma
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convergence in European bank cost efficiency ratios using data up to 2005, but Rughoo and Sarantis (2012) and Matousek et.al. (2015) mostly reject convergence based on log t-tests once
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the years of the financial crisis are included in the analysis.4
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The primary purpose of this paper is to determine whether there has been a convergence
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in performance across a range of financial variables between Islamic and commercial banks in the MENASA region during and after the Global Financial Crisis5. We also address the
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comparative performance and profitability of IBs and CBs over time and examine the speed of adjustment for both types of banks to changing economic conditions. Adopting a dynamic panel approach to analyze the determinants of individual bank profitability and financial stability, in Alternatively, panel unit root tests could be used to test for stationarity and convergence. However, Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) all point out that panel unit root tests are less precise than Phillips and Sul (2007a) log t-test of convergence. Based on studies such as Guetat and Serranito (2007), we could expect results similar to those obtained when examining beta and sigma convergence for any data period. Although not reported in the paper, various panel unit root tests did produce similar results to those from beta and sigma convergence tests. 5 The Global financial crisis started in U.S. and Europe in late 2007 [Jutasompakor et al. (2014)] and took several months to fully transmit its negative effect to the MENASA banking sector. Hence, the GFC did not significantly affect MENASA banks until 2008-2009. 8 4
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general we find that the same variables are significant for both IBs and CBs and have a similar impact on both types of banks. Consistent with Berger and Bouwman (2013), we highlight the crucial role of higher bank capitalization in explaining bank performance and profitability (ROA)
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over the years 1996-2014. Also, we observe that loan specialization and securitization ratios are positively related to bank profitability for both IBs and CBs while cost inefficiency and being
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unlisted (not being listed or even delisted from a national stock market) are negatively related to
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bank profitability. The variables determining financial performance are similar for both CBs and IBs—suggesting that profitability is determined more by individual bank characteristics than by
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the type of bank. More formally, beta, sigma, and log t convergence tests indicate that although there are still some differences between IBs and CBs, both types of MENASA banks have been
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generally converging toward similar values in the aftermath of the Global Financial Crisis. Our
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results are consistent with arguments made by Chong and Lui (2009) and Khan (2010) that IBs
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have become more similar to their commercial counterparts in recent years. Our results have particular implications for regulators and bank managers: First, although
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IBs are governed by Islamic principles, profitability of both types of banks is determined more by individual bank characteristics than by type of bank. Therefore, managerial performance can be assessed on the basis of profitability for both IBs and CBs. Nevertheless, there are several noticeable differences in financial ratios between bank types. Although convergence across all MENASA banks has occurred across most profitability ratios and some asset composition and efficiency ratios, convergence is generally rejected across risk measures and some asset composition ratios. Thus, regulators should not treat the two types of banks identically when setting up and implementing bank regulations.
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Our results show that IBs weathered the initial onslaught of the Global Financial crisis in 2007-2008 better than CBs. However, as the financial crisis spread to the real economy, the profitability of IBs fell relative to CBs in 2009-2010. From 2011 to 2014, IBs and CBs have
CBs have recovered.
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moved toward similar levels of profitability, but IBs have still not recovered to the extent that Based on beta, sigma and t-log tests, it seems that all MENASA banks are
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converging toward common values for profitability ratios, such as return on assets and return on
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equity. However, results are mixed for convergence on net interest and net non-interest margins. The overall evidence may suggest that even though profitability is converging, both IBs and CBs
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continue to operate differently and that there are still some country differences. Also, the speed of convergence varies by geographical area—meaning that the GFC has had a differential impact
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across countries and regions.
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The remainder of the paper is structured as follows. Section 2 provides a review of the
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literature on the determinants of profitability and bank efficiency while Section 3 describes the data and sample. Section 4 next examines profitability ratios for IBs and CBs, followed by
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Section 5 that considers other financial ratios and differences between IBs and CBs over time. Section 6 analyzes convergence and the speed of adjustment for CBs and IBs over various time periods. Section 7 concludes. 2. Literature Review
Historically, IBs have been shown to be more profitable than CBs. Over the period 1990 to 2006, Ariff et al. (2011) report a return on average assets (return on average equity) of 1.40% (11.61%) for IBs compared to 1.27% (9.61%) for CBs in the same countries. However, the net interest margin (NIM) of the latter was 3.91% compared to 3.66% for IBs. For the years 2000 10
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2005, Olson and Zoubi (2008) show that IBs in the Gulf Cooperation Council (GCC) region had a 2.40% annual average return on assets (ROA) and 18.20% return on equity (ROE) compared to 2.00% and 14.40% respectively for CBs. For the years 2000-2008, Olson and Zoubi (2011)
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show that IBs in the Middle East and North Africa (MENA) region had 2.69% ROA (20.18% ROE) compared to 1.56% ROA (12.80% ROE) for CBs. Alqahtani et al. (2016) adopt a
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CAMEL framework and find that IBs led (lagged) CBs in terms of profitability in the early
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(later) stage of the financial crisis period for a sample of Islamic and conventional banks from the GCC region. To summarize, the literature indicates that IBs have generally outperformed
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CBs up to 2009 (i.e., before and in the initial year of the Global Financial Crisis). Delving into the determinants of banks profitability, Demirgüç-Kunt and Huizinga (1999)
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find that profitability differences among banks in 80 countries over the period 1988-1995 could
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be reasonably explained by a set of bank-specific financial ratios (e.g., staff expenses to total
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assets, cash and securities to total assets, bank capital to total assets, size, etc.), macroeconomic variables (e.g., money supply growth, inflation, and interest rates), and industry variables (e.g.,
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concentration and ownership structure). Several studies have followed this seminal work to examine the profitability of Islamic banks [e.g., Mollah et. al. (2016)]. More recent studies using accounting ratios to investigate the determinants of performance generally adopt panel techniques, rather than simple ordinary least squares. To illustrate, Olson and Zoubi (2011) explore the determinants of profitability of IBs and CBs in ten MENA countries for the years 2000-2008 and show that larger bank size, greater dependence upon loans for revenue, higher market concentration, greater GDP growth, and higher proportions of equity capital to assets are generally associated with greater profitability. Higher
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liquidity, greater provisions for loan losses, cost inefficiencies, and more reliance on debt have been indicative of lower bank profits. The authors also find that profitability is positively related to capitalization strength, as measured by the equity to assets ratio and in each bank’s loan
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specialization ratio, which is the degree to which a bank relies on loans relative to other earning assets. Performance is found to be negatively affected by cost inefficiencies and credit risk (as
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measured by loan loss provisions to total loans).
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Another strand of the literature examines the financial stability of Islamic versus conventional banks using z-scores6. Čihák and Hesse (2010) focus on banks in 18 countries
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during the years 1993-2004, and find that large CBs are more stable over time than large IBs, but that small IBs are more financially stable than large IBs or small CBs. Bourkhis and Nabi (2013)
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adopt a paired approach to 34 IBs and 34 CBs across 16 countries for the years 2006 – 2009 and
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examine the comparative soundness of the two types of banks during the GFC. Using the Z-
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score indicator of bank stability, they conclude that there is no significant difference between the stability of IBs and CBs over the entire period. Beck et al. (2013) further investigate the reasons
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for these differences and argue that CBs are less stable than IBs primarily in those countries where IBs have a larger market share. Mobarek and Kalonov (2014) examine the relationship between efficiency and financial stability of both IBs and CBs using the Z-score methodology. Their results indicate that IBs are more financial stable than CBs. Rajhi (2013) extends the analysis of Čihák and Hesse (2010) and confirms their results using robust estimation techniques on a data set for the years 2000-2008 that contains substantial outliers,7 Abedifar et al., (2013)
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Although there are some differences across studies in constructing this variable, the z-score consists of the sum of the expected ROA plus the equity to assets ratios, divided by the variability of returns (i.e., the standard deviation of each bank’s return on assets). 7 He finds that the median Southeast Asian Islamic bank has a significantly higher z-score of financial
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assess the risk in Islamic banking, and report that IBs (particularly small IBs) have lower credit risk than CBs across 23 countries for the years 1999-2009. However, in contrast to previous work, they find that IBs have become less financially stable over time and faced greater
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insolvency risk than CBs during the years 2008-2009. Grira et al. (2016) estimate the implicit risk-based premiums for deposit insurance of Islamic banks and conventional banks for the
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period 1999-2013. The results of this study suggest that publicly listed conventional banks are
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riskier than their Islamic counterparts.
Which type of bank has been less damaged by the global financial crisis? Tentative
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answers to this question may be found in Čihák and Hesse (2010) and Hasan and Dridi (2010). Hasan and Dridi (2010) examine the impact of the GFC on CBs and IBs in emerging markets for
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2007 – 2009 and find that IBs performed better than CBs in 2007 – 2008. They argue that weak
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profitability in 2009.
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risk management practices in some IBs may then have contributed to a large decline in IBs
Beck et al. (2013, p.445) using data over the entire period 2005-2009 show that “the
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higher capitalization and better asset quality have helped IBs outperform CBs during the latest crisis.” Nevertheless, an extensive comparative analysis of the performance for the two types of banks for years after 2009 (post-crisis years) is, to the best of our knowledge, yet to be documented8.
To summarize, the extant literature indicates that IBs are less affected initially by financial shocks, but that CBs may adjust more quickly to longer-term changes in economic stability than the median CBs. He also confirms that smaller IBs are more stable than larger IBs.
Some clues about the relative performance of IBs and CBs post-crisis appear in few studies. For instance, Dahduli (2010) reports that ROA and ROE of IBs improved relative to CBs in 2008. 13 8
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conditions. If there are benefits from both types of banking models, IBs and CBs potentially could learn from one another so that performance could converge across all banks in the MENASA region in the aftermath of the GFC. To more formally test this hypothesis, we refer to
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the growing literature on economic convergence that is used to test whether a variable or ratio is moving toward some common current or historical mean value for that variable. To date, four
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main approaches have been adopted to test for convergence: beta convergence, sigma
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convergence, stochastic convergence based upon unit root and cointegration tests, and finally relative convergence based on log t-tests.
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The solution adopted in the economic growth literature pioneered by Barro (1991) and Barro and Sala-i-Martin (1992) has been to look at changes from one period to the next in
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income or output growth, or in the dispersion in the cross sectional values of a variable between
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each period. Beta convergence can be measured by regressing the growth rate of a variable on
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the beginning of period level of that variable. For example, when applied to income or GDP growth, there is beta convergence if countries or regions with low values grow at a faster rate
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than those with high initial levels of income or output. Hence, the beta coefficient in the so called “Barro regression” must be negative. For banking sector data, beta convergence would imply mean reversion in panel units, or for individual banks. Sigma convergence, also attributable to Barro (1991) and Barro and Sala-i-Martin (1992), tests how quickly the level of some variable converges to the cross sectional average of some group of countries, regions, or firms. Sigma convergence requires that the beta-like coefficient of dispersion away from the cross sectional annual mean is negative. Beta convergence is a necessary, but not sufficient condition for sigma convergence and sigma convergence in the banking sector would occur if
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there was an overall reduction in cross sectional dispersion of some financial variable over time. In a study relevant for what might be expected in the MENASA region, Weill (2009) uses data for the banking sector in ten European countries for the years 1994 – 2005 and finds evidence of
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beta and sigma convergence in the levels of cost efficiency across countries. Such results
support the notion of increased financial integration in the banking sector over time. Similarly,
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Casu and Girardone (2010) support beta and sigma convergence in bank efficiency across 15
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European countries using data for 1997 – 2003. Beta and sigma convergence tests, since their development in the 1990s, have certainly become the most frequently used tests for convergence
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and have been applied to test convergence for a wide range of economic and financial variables across many countries.
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Stochastic convergence is often applied to panel data and it focuses on unit root tests of
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stationarity to determine whether a variable is stationary and reverts to some mean value over
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time, or whether it just wanders and fails to mean-revert or converge to some value. To illustrate the application of such tests, Guetat and Serranito (2007) applied panel unit root tests to examine
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the convergence of GDP per capita across 11 MENA countries over the period 1960 – 2000. They reported mixed results with levels of convergence varying by time period considered, but generally found convergence across countries for the years 1960 – 1990 and then convergence clubs of various countries over the entire period or for various subperiods. Lin and Huang (2012) applied a battery of univariate and panel stationarity test to measures of income inequality across 48 states in US for the years 1916 – 2005. Although traditional univariate unit root tests and simple panel tests failed to reject non-stationarity, the more sophisticated panel techniques that include structural breaks and cross correlation rejected the unit root hypothesis and
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supported stochastic convergence in inter-state incomes. Nevertheless, Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) point out that panel unit root tests require many
test for convergence as developed by Phillips and Sul (2007a).
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restrictive assumptions and are not as precise as measures of relative transition, such as the log t-
Currently, the Phillips and Sul (2007a) log t-test represents the state of the art in testing
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for convergence in any variable. It is generally applied to panel data and convergence cannot be
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rejected if the gamma coefficient on the relative transition parameter in a log t regression is positive, or if it is negative and insignificant. This relative transition variable measures the
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dispersion of a financial variable away from the annual mean in an initial period divided by dispersion in later periods. Convergence requires cross sectional dispersion to decrease over
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time and with the way this variable is constructed, it happens when the relative transition
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variable is positively related to time. For banking sector data, the log t-test can be used to test
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for overall convergence across all banks, for convergence clubs that include only some countries, or for clubs that contain only certain types of banks. Hence, following Phillips and Sul (2007b),
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the log t-test can be used to see whether IBs are converging toward their own mean or toward the mean of all banks (IBs plus CBs). The same tests can be done to see if CBs converge toward the mean of all banks. When applied to the question of financial integration in the European banking sector, Rughoo and Sarantis (2012) adopt the log t-test and support convergence in deposit and lending rates across 15 European countries for the years 2003-2007. However, when the years 2008-2011 are included, the null of convergence in the deposit and credit markets is strongly rejected. Similarly, Matousek et al. (2015) reject convergence in bank cost efficiency across the 15 countries in the Eurozone for the years 2005 – 2012. Based on the European
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experience, the GFC may have reduced convergence in performance of all banks across countries, but our focus in the next section is only on whether IBs and CBs as a group are becoming more alike over time.
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3. Data and Sample
To identify the determinants of bank profitability in emerging markets and to examine
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operational differences between IBs and CBs, we collect financial statement data from
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Bankscope for the years 1996 – 2014.9 This database provides standardized figures so that most of the items are comparable across time and banks. Furthermore, many prior studies [i.e.
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Abedifar et al. 2013; Daher et al. 2015] used the Bankscope data as a reliable source for Islamic bank data. The period of analysis represents the years for which electronic data are currently
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available for 22 countries in Asia and Africa that have both IBs and CBs in their national
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banking sector. Although IBs and Islamic windows exist outside these 22 countries, they do not
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represent a significant portion of the banking sector in those countries. For example, IBs in the United Kingdom consist of only ½ of 1 % of total bank-year observations available in Bankscope
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or that country and Iran is excluded because it had no CBs during the period of analysis. Data for Algeria, Morocco, Gambia, Kazakhstan, and Senegal have been omitted because of too few usable observations. Although banking in Sudan and Pakistan follows general Islamic principles, data are included because Bankscope categorizes each bank as either an IB or a CB. Panel A of Table 1 shows the number of bank-year observations for 22 countries: Bahrain, Bangladesh, Brunei, Egypt, Indonesia, Iraq, Jordan, Kuwait, Lebanon, Malaysia, Mauritania,
External variables affecting bank performance (e.g., inflation, GDP) were collected from the International Monetary Fund (IMF). In general, however, country dummies provided greater explanatory power for firm performance than country-specific macroeconomic variables. 17 9
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Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, Turkey, United Arab Emirates, and Yemen for the years 1996-2014. The data contain 6520 bank-years of observations for the various financial variables used in this study. All data are expressed in US
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dollars and the definitions of the variables are given in Table 2. These variables include a set of
variables that capture individual bank characteristics.
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Insert Tables 1 and 2 about here
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profitability ratios, asset composition measures, efficiency ratios, risk measures, and dummy
The largest number of usable data points come from Indonesia—950 bank-year observations,
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while Malaysia has the most Islamic bank-year observations—172. Palestine and Brunei have only 43 and 46 usable observations. The annual sample size begins with 240 observations in
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1996 and peaks at 454 in 2013. Due to problems with outliers, we only included banks with
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positive reported numbers for equity, deposits, fixed assets, operating expenses, operating
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income, net loans, interest expense, and interest income. To obtain the largest possible data set, we included banks with missing observations for cash, labor expenses, and loan loss provisions,
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but then deleted those observations when calculating financial ratios involving those variables. Finally, we followed Beck et al (2013) and winsorized all variables at the 1% and 99th percentiles.
Panel B of Table 1 shows the composition of banks by country and by type (IB or CB) over the entire period of the data sample. There are s 5296 bank-year observations for CBs and 1224 observations for IBs. IBs comprise about 19% of our sample, while country percentages of usable observations for IBs range from about 2% in Lebanon up 75% in Sudan. 4. Profitability and bank financial ratios 18
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Table 2 shows the financial ratios examined in our study, beginning with five measures of profitability. Panel A of Figure 1 graphically indicates the average annual values for profitability as measured by return on assets (ROA), return on equity (ROE), net interest margin
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(NIM), net non-interest margin (NNIM), and return on deposits (ROD)] for IBs and CBs for the years 1996-2014. Kosmidou et al. (2007) and Van Horen (2007) have argued that ROA is the
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most useful measure of profitability over time because assets have a direct impact on both
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income and expenses. However, our discussion below examines both ROA and ROE.10
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Insert Figure 1 about here
Average ROA for IBs and CBs were similar for the years 1996-1999. Then, as IBs
M
proliferated across Asia and Africa in the period 2000 - 2007, ROA for IBs initially dropped in 2000, but remained notable high relative to CBs for the years 2001 - 2008. Note that average
d
ROAs for both IBs and CBs in 2007 (2.12% and 1.52%, respectively) were above historical
te
averages for 1996 – 2006 (1.41% and 1.27%). Similarly, Panel A of Figure 1 indicates peak
Ac ce p
profitability for ROE and ROD for the years 2004-2007 and a big drop from 2007 to 2008 and from 2008 to 2009. Hence, we define the financial crisis period for MENASA banks as the years 2008-2009.
During the financial phase of the crisis in 2008, average ROA fell to 1.49% for IBs and to 1.30% for CBs in 2008. Then, as the crisis spread to the real economy, the economic phase of
Bankscope also provides data for the return on average assets (ROAA). Its denominator is calculated as the average of the end of period and beginning of period total assets, rather than from end of period total assets for ROA. The two ratios are over 98% correlated, so results for ROAA are not presented. Similarly, the Bankscope return on average equity (ROAE) is 93% correlated with ROE. 10
19
Page 18 of 54
the GFC reduced average ROA to 0.85% for IBs and to 1.22% for CBs in 2009. For 2010-2014 average ROA has been 1.24% for commercial banks and 1.09% for Islamic banks. Looking at the time pattern of response of banks to the financial crisis, IBs may initially weather the shock
ip t
better, but they decline more in terms of profitability in a prolonged crisis. As pointed out by Grassa (2012), Islamic profit-loss sharing products present greater insolvency risk than products
cr
offered by CBs and this type of risk has a more detrimental impact on performance during a
us
prolonged crisis. IBs have taken longer to move back toward pre-crisis levels of profitability, but differences in recovery speed may be due to the large expansion of Islamic banking in the
an
pre-crisis era and reflect a type of mean reversion that was hastened by the Global Financial Crisis. Also, profitability ratios for Islamic banks show greater year to year variability than those
M
for commercial banks so that the current underperformance of IBs relative to CBs may be due to
d
normal fluctuations.
te
Panels B – E of Figure 1 present average annual values for the variables presented in Table 2. These are bank-specific variables that have been used in a variety of studies to explain
Ac ce p
bank performance and profitability. These panels illustrate the trend over time for various asset composition variables, efficiency ratios, and measures of risk for IBs and CBs in the MENASA region. Although there appear to be some convergence in profitability ratios between IBs and CBs, there is less convergence in other ratios. To further assess the convergence proposition, Table 3 shows the average values for CBs
before, during and after the crisis of 2008-2009. Values are obtained from ordinary least squares regressions of each variable on three dummy variables, adjusted for heteroskedasticity and autocorrelation. The dummy variable PRE represents pre-crisis years (1996-2007), CRISIS is
20
Page 19 of 54
for the years 2008 – 2009, and AFTER is a dummy variable for period 2010 – 2014. Insert Table 3 about here
ip t
The profitability ratios, ROA and ROD were significantly larger at the 1% level for IBs relative to CBs for 1996-2007. ROD remained higher for IBs during 2008-2009, but both ROA
cr
and ROD are essentially the same for both bank types in the aftermath of the crisis. ROE was
us
similar for IBs and CBs in the pre-crisis era, but ROE has been significantly smaller for IBs after 2007.
an
NIM was similar for IBs and CBs from 1996 – 2009, but then smaller for IBs in the years 2010 – 2014. The net interest margin has been slightly higher for CBs relative to IBs over the
M
entire period 1996 – 2014. In contrast, NNIM has been slightly higher on average for IBs over the entire period.
te
d
Regarding the asset composition ratios, Islamic banks are smaller than commercial banks. They tend to have higher loan specialization ratios, loan to deposit ratios, cash to asset ratios,
Ac ce p
fixed assets to total assets, and greater proportion of non-interest bearing assets than commercial banks. IBs have lower security specialization and deposit to asset ratios. IBs had lower inefficiency ratios pre-crisis, but they have significantly higher inefficiency ratios than CBs from 2008 – 2014. IBs have higher labor costs and other operating expense to income ratios, but lower interest expense to income ratios than CBs. For the risk measures, IBs had significantly lower credit risk pre-crisis, but have since taken on more such risk than CBs. IBs have lower loan impairment ratios and greater capital strength than CBs. Nevertheless the Z-scores for IBs have fallen relative to CBs since 2008. In summary, IBs and CBs have similar levels of profitability, but they do it with different operating characteristics due to different asset 21
Page 20 of 54
composition ratios and different risk exposures. 5. Determinants of ROA and ROE A substantial body of literature has examined the variables that determine individual bank
ip t
profitability. Our purpose in this section is to discover whether the determinants differ between
cr
IBs and CBs. Following Olson and Zoubi (2011), we estimate ROA using an unbalanced
dynamic panel model with the financial variables listed in Table 2 as the independent variables
us
that may identify operational and profitability differences between IBs and CBs.
an
The basic framework for our random effects panel model is: ROAit = αi + β Xit + Dummy Variables + εit,
(for i = 1, 6520)
(1)
M
where ROAit is the dependent variable, α is the common intercept across banks in the random effects model, Xit is a vector of explanatory variables described in Table 2 (including bank-
d
specific accounting ratios and financial variables such as size), β is a vector of regression
te
coefficients, and εit is the disturbance term that is assumed to be normally distributed with a
Ac ce p
mean of zero. Dummy Variables include country dummies for the 21 of the 22 countries with Malaysia as the intercept term and an UNLISTED dummy variable that equals one if a bank is neither listed on a national stock exchange, nor delisted from a national exchange. The constant (α) term can be further expanded to illustrate differences between time periods (PRE, CRISIS, and AFTER) and between IBs and CBs in any of the three time periods. The PRE-Malaysia constant term in Table 4 is a projected base ROA for commercial banks in Malaysia in the precrisis period given the values of other variables. Any country could have served as the constant and the choice of Malaysia rather arbitrary, but Malaysia was selected as the constant because it has the most bank-year observations for Islamic banks and because it is reasonably close to the 22
Page 21 of 54
sample average for the various profitability ratios. Hence, PRE-Islamic denotes the differential returns to IBs relative to Malaysian CBs in 1996 – 2007, Crisis (2008-2009) is the base point
the crisis, and After and After-Islamic apply to the years 2010-2014.
ip t
ROA for Malaysian CBs during the crisis, Crisis-Islamic is the differential return for IBs during
Although a fixed effects model dominates the random effects specification on the basis of
cr
the log of the likelihood function, results from the random effects model are presented to
us
illustrate the impact of country-specific dummies and the impact of stock market listing. The adjusted R-squared value is taken from the equivalent fixed effects model that does not include
an
the time-invariant dummy variables.
M
Insert Table 4 about here
The final coefficients selected in the Table 4 are based on an exhaustive search to
d
maximize the log likelihood function using a 5% cutoff value for coefficients, as estimated by
te
version 9.1 of the RATS statistical package. For each non-dummy variable in Table 2, the
Ac ce p
financial variable was multiplied by an Islamic dummy variable to determine if there were any differences in the determinants of ROA between IBs and CBs. Only one variable, SIZE-Islamic is significantly different from the coefficient for CBs. Although SIZE is positively related to profitability for CBs, it is negatively related to ROA for IBs. This result confirms the findings of Beck et al (2013) that smaller IBs are more profitable than larger IBs. Other explanatory variables are roughly of the same magnitude and sign for both IBs and CBs. Overall, Table 4 indicates that the determinants of individual bank profitability, as measured by ROA, are essentially the same for both IBs and CBs. The coefficients on the pre-crisis, crisis, and aftercrisis dummy variables for differences between IBs and CBs are insignificant. That is, 23
Page 22 of 54
individual bank characteristics determine profitability. To summarize the ROA results from Table 4, more profitable banks have greater capital strength and loan specialization ratios, but lower inefficiency ratios. Hence, issuing loans,
ip t
controlling costs, and maintaining adequate equity capitalization are important for all banks, regardless of whether they are Islamic or commercial banks. Similar variables have been
cr
identified in previous studies as the primary determinants of individual bank profitability. For
us
example, Kosmidou et al (2007) finds that larger bank size is a major determinant of profitability. Berger and Bouwman (2013) show that having adequate bank capital (CAPSTR) is
an
an important factor for bank survival during financial crises. Finally, the banking literature has focused substantially on cost efficiency and the inefficiency ratio appears to be perhaps the major
d
significant determinant of profitability.
M
determinant of profitability for our sample of IBs and CBs. For our data set, risk is not a
te
The dummy variable UNLISTED is negatively related to profitability, meaning that stock market listing contributes to profitability. Stock market listing is another measure of size, age,
Ac ce p
and the reputation of the bank. The significantly positive country dummies for Bangladesh, Indonesia, Sudan, Syria, and the United Arab Emirates show above average profitability for banks in those countries, even after adjusting for the impact of individual bank characteristics. None of the countries show distinctly lower ROA than the reference country of Malaysia, but the lower ROA for Saudi Arabia would be significant if a country such as the United Arab Emirates Turkey, or Indonesia were used as the reference country instead of Malaysia. Similar to Beck et al. (2013) once individual bank results have been adjusted for variables like capital strength and the inefficiency ratios, there is no significant difference 24
Page 23 of 54
between the performance of IBs and CBs. The determinants of ROA are essentially the same for both IBs and CBs—it is primarily individual differences in bank characteristics that explain performance differences and not whether a bank is commercial or Islamic.
ip t
Equation (1) can be used to examine the determinants of ROE and the results are shown
cr
in Table 4. Explanatory variables are similar to those for ROA with one exception. Capital strength is negatively related to ROE. The reason is that higher values of equity in the
us
denominator of the ROE ratio reduce its value—apparently by more than higher equity increases net income. As was the case for ROA the inefficiency ratio is negatively related to profitability
an
and appears to be the most significant determinant of ROE. The securitization ratio is positively
M
related to ROE, while the loan specialization ratio is not a significant determinant of ROE. The deposit to assets ratio is also positively related to ROE. It is also positively related to ROA, but
d
loses significance once other variables are included in the ROA equation. UNLISTED is
te
significantly negatively related to ROE, as it is to ROA. Once again the PRE-Islamic, CrisisIslamic, and After-Islamic dummy variables are insignificant in explaining profitability
Ac ce p
differences between banks. ROE in Bangladesh is significantly higher than in the reference country and ROE is significantly lower in Brunei. Once again profitability would be significantly lower in Saudi Arabia than other countries if a country such as Turkey had been used as a reference point, instead of Malaysia. The determinants of ROE are similar to those for ROA and serve as a robustness check for measures of profitability. Basically, bank profitability is determined by individual bank-specific variables such as the inefficiency ratio, capital strength, asset specialization ratios, and whether a bank is listed or unlisted. The banking models may be different, but profitability does not seem to be determined by whether a bank is an 25
Page 24 of 54
Islamic or a commercial bank. 6. Convergence and speed of adjustment The graphical results presented in Figure 1 as well as the general lack of significance in
ip t
differentials between IBs and CBs after the GFC in Table 3 might suggest that the two types of
cr
banks are becoming more similar over time. To more formally test this hypothesis, we refer to the growing literature on economic convergence that is used to test whether a variable or ratio is
us
converging toward some common current or historical mean value. This issue has been addressed by four different approaches: beta convergence, sigma convergence, panel unit root
an
tests, and log t convergence tests. However, for the sake of brevity, we do not present results for
M
panel unit root tests because studies such as Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) indicate that such tests are generally not as precise as the Phillips and Sul
te
6.1 Beta convergence
d
(2007a) log t-test of convergence.
We denote yit (for bank i in year t) as the value of any of the ratios or bank variables
Ac ce p
listed in Table 2. Then, following procedures developed by Barro and Sala-i-Martin (1992), beta convergence is then estimated from a fixed effects panel estimator of the following equation:
ln (yit / yit 1 ) ln ( yit 1 ) Islamic ln ( yit 1 ) Islamici it ,
(2)
where β and βIslamic are the coefficients to be estimated for each financial variable, ln is the natural log function, and εit is an error term. Beta convergence occurs if the coefficient for β based on the initial beginning of period level of the variable or ratio is negative.11 In Equation
11
For ratios that can take on negative values (e.g., ROA, ROE, NNIM, etc.), Equation (2) is estimated using the log of (1 + yit) with the ratio expressed in decimal format. These variables could instead be
26
Page 25 of 54
(2), the beta coefficient represents speed of adjustment and it is further divided so that β represents commercial banks and βIslamic shows the differential speed of adjustment for IBs relative to CBs. As shown in Table 5, all of the beta coefficients (labeled βCommercial) are negative
ip t
and statistically for all 21 financial variables. This indicates that all banks are converging toward some common value for each ratio over time. The βIslamic coefficients are positive and
cr
statistically significant for 7 of 21 ratios: ROA, ROD, NIM, NIBA, INEFF, LCI and IMPAIR.
us
IBs adjust faster than CBs only for the case of the loan to deposits ratio. In general, IBs adjust to changes slower than CBs across several variables; however, given the small magnitude of the
an
Islamic differentials, IBs still display convergence toward some overall mean for each ratio. Moving to the After-Crisis period of 2010-2014, all 21 variables display convergence toward a
M
common value. For many of the ratios, convergence is faster than over the entire period, but the
d
positive Islamic differential for slower convergence is still observed for 7 of 21 financial
te
variables. Focusing on ROA, IBs react slower than CBs to profitability changes, both over the entire period and in the post-crisis years. However, both IBs and CBs appear to be converging
Ac ce p
toward the mean for all banks in the MENASA region. 6.2 Sigma convergence
We denote the difference between the value of yit for an individual bank and the cross n
y
sectional mean of that variable for each year as: Wit ln (yit ) (1/ n)
i 1
it
. Then sigma
convergence is estimated from a fixed effects panel estimator of the following equation: estimated using raw, untransformed data. For all variables, there was little difference between using log or non-log data in testing for convergence. Some studies, such as Casu and Girardone (2010) include lagged values of the dependent variable in equations (1) and (2). This formulation for beta and sigma convergence tests had little impact on other coefficients and we adopt the formulation in the paper for greater simplicity.
27
Page 26 of 54
ln (Wit / Wit 1 ) ln ( yit 1 ) Islamic ln ( Wit 1 ) Islamici it ,
(3)
where Wit replaces the yit values in Equation (2), and sigma convergence requires that β < 0. Sigma convergence measures whether there is a lessening in the cross sectional dispersion in
ip t
each variable over time. It is a more stringent condition than beta convergence because beta
cr
convergence is a necessary, but not sufficient condition for sigma convergence. Nevertheless, the results for sigma conversion in Table 6 are nearly identical to those for beta convergence in
us
Table 5. Sigma convergence across all 21 variables is indicated by the negative coefficients on βCommercial over the whole period. Similarly, sigma convergence is observed for all 21 ratios over
an
the period 2010-2014. Once again IBs display slower convergence than CBs for 7 of 21
M
variables over the whole period and for the post-crisis period. Overall, sigma convergence is observed across all banks; with CBs generally shower faster convergence than IBs—particularly
te
6.3 Gamma (Log t) convergence
d
for variables such as ROA.
Since its introduction in 2007, the Phillips and Sul (2007a) log t-test has become the
Ac ce p
preferred test for measuring convergence of economic or financial variables. It offers better discriminatory power than tests for beta convergence, sigma convergence, or various unit root and cointegration tests for stationarity or co-movement between variables. Since the now standard methodology for relative transition parameters and log t-tests of convergence has been extensively presented by Phillips and Sul (2007a, 2007b, and 2009) and summarized in Matousek et al. (2015), we only briefly discuss how the test is developed for our data set. We define the relative transition parameter hit as:
28
Page 27 of 54
hit
yit
(4)
n
(1/ n) yit i 1
ip t
It describes the transition path for each bank relative to the panel or cross section average in each year. The average value for hit must be one, so the cross sectional dispersion of the relative
n
ln ( H1 / Ht ) (1/ n) (hit 1)2 .
(5)
an
i 1
us
cr
transition parameters relative to their average in any year may be defined as:
Then, Phillips and Sul (2007a) perform an ordinary least squares regression on the
M
dispersion of the relative transition parameters (Ht) as follows:
(6)
d
ln(H1/ Ht ) 2ln[ln(t1)] a ln(t) et
te
This notation follows Phillips and Sul (2009), where et is the error term and the
Ac ce p
regression equation is adjusted so that the parameters are corrected for heteroskedasticity and autocorrelation. Since the γ parameter on log t indicates speed of adjustment, Equation (6) can be referred to as a test for gamma convergence. However, it is similar to sigma convergence since it involves the dispersion of cross sectional variance over time. Values of γ ≥ 2 imply convergence in levels for a variable or ratio, while values of 2 > γ ≥ 0 imply conditional convergence or that growth rates converge over time. Convergence requires that the cross sectional dispersion in a variable decreases over time. Since H1 represents dispersion in the first year and Ht is the dispersion in any year, the ratio (H1/Ht) will increase over time if convergence is occurring (because Ht will decline relative to H1). Hence, convergence requires a positive 29
Page 28 of 54
gamma coefficient. Phillips and Sul (2007a) have set up the log t test so that the null hypothesis is convergence over time. If the t-statistic for the γ coefficient is t < -1.65, the null hypothesis of convergence can be rejected with a one-sided t-test at the 5% confidence level. If t > -1.65 the
ip t
null hypothesis of convergence is accepted. The -2 ln [ln(t+1)] term in our Equation (6) behaves like a penalty function for the regression and has been defined as -2 ln [ln(t)] in some empirical
cr
applications of the log t-test. Finally, many studies de-trend the data using a Hodrick-Prescott
us
filter. We experimented with this filter and various smoothing techniques for the ROA variable and found that the results were about the same as winsorizing at the 2nd and 98th percentile levels.
an
Hence, we adopted this rather simple winsorizing technique for all 21 financial ratios when implementing the log t-test.
M
To implement the log t regressions, Phillips and Sul (2007a) recommend discarding from
d
20% to 30% of the observations after calculating ln (H1/Ht). For the full sample, H1 is 1996, but
te
we discard the first five of nineteen years (about 26% of the data) to estimate the log t regressions. To examine convergence in the more recent 2008 – 2014 period, H1 becomes 2008.
Ac ce p
We then discard the first two of seven years (about 29% of the data) and convergence is examined for 2010-2014.
Table 7 shows the results of the log t convergence test over the entire period from 1996 – 2014, as well as for 2010-2014. The second column (for γAll) shows significantly large negative gamma coefficients for 7 of 21 variables—meaning that the log-t tests rejects convergence across all banks for these seven ratios. The gamma coefficients are insignificant for 10 of 21 variables (including ROA) —meaning that the log t results are inconclusive regarding convergence. Finally, the γAll coefficients are positive and statistically significant for 4 of 21 ratios (NIM, 30
Page 29 of 54
SIZE, CTA, and CAPSTR) and suggest convergence in rates for these ratios. The coefficients for γ Islamic and γ Commercial are obtained from separate log-t regressions following the procedures in equations (4) – (6), except that yit represents only Islamic or only
ip t
commercial banks in equation (4) and the cross sectional mean is the mean ratio for all banks (IBs and CBs) in a given year. Note that the signs and magnitudes of the γ Islamic and γ Commercial
cr
coefficients are similar for most variables. These coefficients indicate whether IB financial
us
variables are converging toward the values for those of all banks and whether CB financial variables are converging toward the values for those of all banks. The results provide only weak
an
support for convergence, but they show that there is little difference in convergence characteristics between IBs and CBs in the MENASA region.
M
The right-most three columns in Table 7 examine convergence in the post-crisis period of
d
2010-2014. Positive statistically significant gamma coefficients indicate convergence across 7 of
te
21 variables. Ratios have been converging to a greater extent in the aftermath of the Global Financial Crisis than was seen in earlier years. In particular, we note that convergence in rates
Ac ce p
across all banks has occurred for our two main measures of profitability—ROA and ROE. Results are inconclusive for 9 ratios and convergence is rejected for only 5 of 21 ratios (NIM, FATA, IMPAIR, CRISK, and Z-score). Signs and magnitudes of gamma coefficients are similar for both IBs and CBs—once again indicating little difference between IBs and CBs. Note that convergence is rejected across all banks for the net interest margin—a variable where one might expect differences between IBs and CBs given the prohibition of riba in Islamic banking. Also, three of the non-converging variables--credit risk, the impairment ratio, and Z-score—are all measures of risk in banking. Since IBs and CBs operate under different principles, the risks
31
Page 30 of 54
facing them may be different so that convergence across risk ratios does not occur. This is consistent with findings of Baele et al. (2014) that default rates on Islamic loans in Pakistan were only about one-half that for loans issued by commercial banks. To summarize, competitive
ip t
pressures in the aftermath of the GFC may have led to converge in profitability measures across MENASA banks. However, since IBs and CBs operate differently, they have reached the
cr
profitability results in different ways so that there has not been a convergence in all asset
us
composition and risk ratios. 6.4 Gamma (Log t) club convergence
an
Studies that find little evidence of overall convergence often test for club convergence
M
across a subset of countries or firms where convergence still may occur. Since we generally support convergence in profitability ratios across all banks, further testing for club convergence
d
should only strengthen our results. Nevertheless, we follow the example of Phillips and Sul
te
(2007a, 2007b, 2009) and test for club convergence with various subsets of our data. We limit our tests to the profitability ratios ROA, ROE, NIM, and NNIM based on clustering using only
Ac ce p
the ROA variable. Based on our previous results, we expect to discover stronger convergence in clubs for ROA and ROE, but lack of convergence in NIM. The results for NNIM are unclear a priori.
Club convergence results for three geographical regions are shown in Table 8. First is the Gulf Cooperation Council (the six GCC countries) consisting of Bahrain, Kuwait, Oman, Qatar, United Arab Emirates and Saudi Arabia. The second region is Southeast Asia, consisting of Bangladesh, Brunei, Indonesia, Malaysia and Pakistan. Third is the Africa region consisting of banks in Egypt, Mauritania, Sudan and Tunisia. Other regions could be considered, but our 32
Page 31 of 54
results are illustrative of what could be expected based on geographical regions. For the GCC, convergence results for the entire period 1996-2014 are about the same as for all countries in Table 7. For 2010-2014, we observe much stronger club convergence across
ip t
all banks for both ROA and ROE. In fact, the results suggest convergence in levels, which is a
cr
stronger result than convergence in rates observed in Table 7. There is no evidence of
convergence across all banks for NIM or NNIM, but we observe convergence in rates for Islamic
us
banks relative to all banks in the GCC region for the variable NIM.
an
Results for Southeast Asia are mixed with convergence in rates across all banks for the variables NIM and NNIM over the whole period 1996-2014. However, for 2010-2014
M
convergence is rejected for ROA, ROE, and NIM. Such results probably have arisen because of the heterogeneous nature of the countries placed in this group.
d
For Africa, results are inconclusive for 1996-2014 except for the rejection of convergence
te
in NIM. For 2010-2014, results are as hypothesized. There is convergence in levels across
Ac ce p
African banks for ROA and ROE. Convergence is rejected across all banks for NIM and NNIM. Once again the signs and magnitudes of the gamma coefficients are similar for IBs and CBs. The club convergence results are more decisive than seen for all MENASA countries in Table 7. Another way to select clubs is to adopt a clustering algorithm, as in Phillips and Sul (2007a, 2007b, 2009). We place countries identified as having high historical ROA in one group and countries with low historical ROA in another group. The high ROA cluster consists of Iraq, Qatar, Saudi Arabia, Sudan, and the United Arab Emirates. This group is similar to the GCC in composition and not surprisingly the results for 2010-2014 support convergence in levels across all banks in the cluster for the profitability variables ROA and ROE. Convergence is rejected 33
Page 32 of 54
across all banks for the variables NIM and NNIM. The low ROA cluster consists of the countries Brunei, Egypt, Lebanon, Malaysia, Pakistan, Syria, and Tunisia, and is a combination of banks from the Africa and Southeast Asia
ip t
regions. For the period 2010-2014, convergence in levels across all banks is found for ROA and
cr
ROE. Convergence is rejected for NIM and NNIM. A third club of middle ROA countries
us
could be formed, but not surprisingly, most of the results for this cluster were inconclusive. 7. Summary and conclusion
an
This paper has examined the profitability of commercial and Islamic banks before, during and after the financial crisis of 2008-2009. Using the largest sample to date of Islamic and
M
commercial banks operating in Africa, Asia, and the Middle East, we confirm that Islamic banks were more profitable and more financially stable than their commercial counterparts prior to the
d
Global Financial Crisis. Islamic banks weathered the initial onslaught (financial phase) of the
te
crisis better than their commercial counterparts in 2008. However, as the crisis spread to the real
Ac ce p
economy (economic phase) in 2009, Islamic banks noticeably underperformed commercial banks. Islamic banks have been catching up, but at a slower rate than commercial banks, and they have still not returned to pre-crisis levels of profitability. Convergence tests indicate that all banks are converging toward similar levels of profitability as measured by return on assets and return on equity, but convergence is not occurring across all financial ratios. In particular, banks are not converging across net income margins or risk characteristics. This non-convergence results hold for the whole period from 1996 to 2014 and in the post-crash period of 2010-2014. A possible explanation is that competitive pressures force banks toward convergence in profitability metrics. However, because Islamic and commercial 34
Page 33 of 54
banks have different operating philosophies, the profitability results are obtained in different ways so that convergence does not occur across risk measures and some asset composition ratios. Our findings have several policy implications: Although there have been periods where one
ip t
type of bank outperformed the other, we expect them to perform rather similarly in the future. Since Islamic banks and commercial banks maintain different asset composition characteristics
cr
and risk profiles, regulators need not apply all of the same regulations to both types of banks.
us
Even though IBs are governed by Islamic principles, profitability of both types of banks is determined more by the characteristics of the individual bank than the type of bank (IBs vs.
an
CBs). Hence, the performance of bank’s management can be assessed on the basis of profitability measures for both IBs and CBs. Yet, there are several noticeable differences in
M
financial ratios between bank types. Thus, regulators should not treat the two types of banks
d
identically when setting up and implementing bank regulations. Islamic banks still need to find
te
ways to enhance non-interest based revenues, while commercial banks should note that a more conservative philosophy toward banking as evidenced by Islamic principles may serve them to
Ac ce p
avoid losses caused by the so called “toxic assets” that may have contributed to the Global Financial Crisis.
35
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ip t
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cr
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us
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M
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d
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te
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Ac ce p
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Chong, B., Lui, M. 2009. Islamic banking: Interest-free of interest-based? Pacific Basin Finance Journal 17(1), 125-144.
ip t
Dahduli, M., 2010. Islamic banking and the credit crunch. Working paper, Bangor University, UK.
cr
Daher, H., Masih, M., Ibrahim, M. 2015. The unique risk exposures of Islamic banks’ capital buffers: A dynamic panel data analysis. Journal of International Financial Markets, Institutions and Money 36, 36-52.
us
Demirgüç-Kunt, A., Huizinga, H., 1999. Determinants of commercial bank interest margins and profitability: Some international evidence. The World Bank Economic Review 13, 379-408.
an
Escribano, A., Stucchi, R., 2014. Does recession drive convergence in firms’ productivity? Evidence from Spanish manufacturing firms. Journal of Productivity Analysis 41, 339-349.
M
Grassa, R., 2012. Islamic banks' income structure and risk: evidence from GCC countries. Accounting Research Journal 25, 227-241.
d
Grira, J., Hassan, M., Soumare, I. 2016. Pricing beliefs: Empirical evidence from the implied cost of deposit insurance for Islamic banks. Economic Modelling 55, 152-168.
te
Guetat, I., Serranito, F., 2007. Income convergence within the MENA countries: A panel unit root approach. Quarterly Review of Economics and Finance 46, 685-706.
Ac ce p
Hasan, M. & J. Dridi J., 2010. The effects of the global crisis on Islamic and conventional banks: A comparative Study. IMF working paper 10/201. Jutasompakor, P., Brooks, R., Brown, C., Treepongkaruna, S., 2014. Banking crises: Identifying dates and determinants. Journal of International Financial Markets, Institutions and Money 32, 150-166. Kim, Y., Rous, J. 2012. House price convergence: Evidence from U.S. state and metropolitan area panels, working paper. Khan, F., 2010. How “Islamic” is Islamic banking? Journal of Economic Behavior & Organization 76, 805-820. Khan, B., Crowne-Mohammed, E.A., 2009-2010. The value of Islamic banking in the current financial crisis. Review of Banking and Financial Law 29, 441-464.
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Kosmidou, K., Pasiouras, F., Tsaklanganos, A., 2007. Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad. Journal of Multinational Financial Management 17, 1-15.
ip t
Lin, P., Huang, H, 2012. Inequality convergence revisited: Evidence from stationarity panel tests with breaks and cross correlation. Economic Modelling 29, 316-325.
cr
Matousek, R., Rughoo, A., Sarantis, N., Assaf, A., 2015. Bank performance and convergence during the financial crisis: Evidence from the ‘old’ European Union and Eurozone. Journal of Banking and Finance 52, 208-216.
us
Mobarek, A., Kalonov, A. 2014. Comparative performance analysis between conventional and Islamic banks: empirical evidence from OIC countries. Applied Economics 46, 253-270.
an
Mollah, S., Zaman, M. 2015. Shari’ah supervision, corporate governance and performance: conventional vs. Islamic banks. Journal of Banking and Finance 58, 418-435. Mollah, S., Hassan, M., Farooque, O., Mobarek, A. 2016. The governance, risk-taking, and performance of Islamic banks. Journal of Financial Services Research (forthcoming).
M
Olson, D., Zoubi, T., 2008. Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region. International Journal of Accounting 43, 45-65.
te
d
Olson, D., Zoubi, T., 2011. Efficiency and bank profitability in MENA countries. Emerging Markets Review 12, 94-110.
Ac ce p
Phillips, P., Sul, D. 2007a. Transition modelling and econometric convergence tests. Econometrica 75, 1771-1855. Phillips, P., Sul, D. 2007b. Some empirics on economic growth under heterogeneous technology. Journal of Macroeconomics 29, 455-469. Phillips, P., Sul, D. 2009. Economic transition and growth. Journal of Applied Econometrics 24, 1153-1185. Rajhi, W., 2013. Islamic banks and financial stability: A comparative empirical analysis between MENA and Southeast Asian Countries. Région et Développement 37, forthcoming. Rashwan, M., 2010. A comparison between Islamic and traditional banks: Pre and post the 2008 financial crisis. Working paper, British University in Egypt. Rughoo, A., Sarantis, N. 2012. Integration in European retail banking: Evidence from savings and lending rates to non-financial corporations. Journal of International Financial Markets, Institutions and Money 22, 1307-1327. 38
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Van Horen, N., 2007. Foreign banks in developing countries: Origin matters. Emerging Markets Review 8, 81-105.
Ac ce p
te
d
M
an
us
cr
ip t
Weill, L., 2009. Convergence in banking efficiency across European countries. Journal of International Financial Markets, Institutions & Money 19, 818-833.
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Appendix In general, there are two types of Mudarabah: restricted and unrestricted. Under the restricted Mudarabah, the IB receives the money and the customer specifies a particular business for which
ip t
the IB shall invest the money in. IB acts as a fund manager and normally the funds are
accounted for off-balance sheet. In Unrestricted Mudarabah, an IB receives funds from
cr
depositors that it is subsequently allowed to use the funds in any activity that the management
us
feels appropriate, so long as the activities are not forbidden by Islamic Sharia. In doing so, the IB makes the funds available to entrepreneurs who have ideas and expertise to use the funds in
an
productive activities. The bank and its client share profits but the client does not share in losses. IB usually pools all profits and losses from different Mudarabah investments (entrepreneurs) and
M
shares the profit with depositors of funds based on a pre-agreed ratio. IBs are thus looked upon
d
as partners with both depositors and entrepreneurs, and they share risk with both.
te
In Musharka, IB provides part of the needed funds to an entrepreneur who has a specific project. The partners’ contributions do not have to be equal and contributions may be in the form
Ac ce p
of physical or intangible capital, such as machinery, labor or management. IB may or may not participate in the management of the business. Both parties share profits on a pre-agreed ratio, but the losses are shared based on the ratio of capital provided by each party. This is a joint venture between the bank and the client designed for a certain project and ending within an agreed period of time.
Another type of contract is Ijarah (leasing). It is designed for financing mainly tangible assets such as aircrafts vehicles, equipment and machinery. The IB buys the asset and then leases the asset to an entrepreneur for a mutually agreed charge. IB receives periodic payments
40
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from the lessee so long as the leased-property can be used by the lessee and it is carried as an asset in the balance sheet of the IB (lessor). At the end of the lease term, the ownership (title) of the leased-property can be transferred to the entrepreneur (Ijarah Muntahia Bittamlek). Thus,
ip t
the IB is an ‘investor’ when providing financing for asset services using Ijarah, and perhaps when providing financing for asset acquisitions using Ijarah Muntahia Bittamlek.
cr
Murabaha, not profit-sharing contract, is one of the most widely utilized forms of
us
financing contract by IBs where the bank purchases for a client certain commodities based on her request. The client promises to buy the goods from the bank on a pre-agreed profit basis. The IB
an
may briefly bear some asset price risk in respect of the item being sold, but may mitigate this by requiring a deposit from the customer (e.g., car financed through a sale to the customer after the
M
bank purchases the car). The bank justifies the profit on the grounds that it takes some risks
d
because the client has no legal obligation to fulfill his initial promise. Murabaha in general is
not an investor.
te
used to provide sale on credit resulting in account receivable. In this case, the bank is a creditor
Ac ce p
IBs replace loans with investments that are generally riskier than secured interest bearing loans. The account receivable resulting from the sale is no more risky than an unsecured loan to a customer with a similar credit standing, and may be collateralized in which case it is no more risky than a loan with similar security.
41
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Totals
ip t
1999 7 27 3 23 33 2 11 8 38 23 2 5 15 1 6 8 6 1 15 19 17
2000 7 29 3 22 44 2 1 8 39 21 4 5 13 1 6 9 6 1 17 17 17
2001 8 33 3 22 38 2 13 8 30 24 4 5 15 1 7 9 8 1 17 21 18
2002 7 33 3 23 36 1 13 8 30 26 6 5 13 1 7 9 9 0 16 24 19
4
5
240
251
2003 10 33 3 23 38 1 13 8 29 26 7 5 18 2 7 9 6 0 14 25 19
2004 10 32 3 23 38 1 14 8 29 26 7 5 19 2 7 10 6 0 14 26 19
2005 15 32 3 23 50 4 13 10 31 30 7 5 21 2 7 10 8 5 14 25 20
2006 15 13 2 22 50 4 14 11 29 34 5 5 25 2 7 10 10 6 14 26 20
2007 19 33 2 24 53 5 1413 10 31 37 5 6 26 2 7 11 13 7 15 28 20
2008 20 33 2 25 53 7 13 9 30 42 4 6 28 3 8 11 14 11 16 29 21
2009 16 33 2 24 58 5 13 9 37 42 6 6 27 4 8 12 14 10 16 31 21
2010 18 37 2 25 60 7 13 9 35 43 8 6 26 4 9 12 14 11 15 31 20
2011 16 38 2 25 64 9 14 10 34 46 7 6 27 4 10 12 15 10 15 30 22
2012 18 38 2 25 72 11 14 11 34 45 5 6 28 4 10 12 14 10 14 31 25
2013 18 45 2 25 85 10 14 11 32 47 5 8 28 4 10 12 12 11 11 32 25
2014 17 43 2 24 86 9 14 11 29 47 3 8 28 4 10 12 9 8 11 34 25
Totals 238 607 46 450 950 85 246 173 633 628 96 106 401 43 144 195 177 95 274 439 378
5
5
5
6
6
7
7
7
7
7
8
8
7
6
8
7
1
116
249
275
288
293
295
302
306
342
349
375
393
402
412
422
437
454
435
6520
M an
1998 6 20 3 24 27 2 11 8 39 23 3 5 15 1 6 9 5 1 14 4 18
ce pt
1997 5 19 3 24 35 2 11 8 39 23 4 5 14 1 6 9 5 1 13 3 16
Ac
Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Malaysia Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
Year 1996 6 18 1 24 30 1 11 8 39 23 4 5 15 0 6 9 3 1 13 3 16
ed
Country
us
Panel A: Number of banks in sample by country for the years 1996 - 2014
cr
Table 1—Description of Data Sample
43
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44 Table 1—(continued) Description of Data Sample
Totals
cr us
an
132 9 18 63 88
45.0 17.8 59.0 11.1 5.9 22.4 19.5 45.7 1.6 27.4 14.6 3.8 18.2 41.9 32.6 22.6
M
45 86 256 376 290
te
107 108 27 50 56 19 48 79 10 172 14 4 73 18 47 44
% Islamic
Ac ce p
Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Malaysia Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
Total Observations Islamic
Commercial 131 499 19 400 894 66 198 94 623 456 82 102 328 25 97 151
d
Country
ip t
Panel B: Number of banks in sample by type (commercial or Islamic) for the years 1996 - 2014
74.6 9.5 6.6 14.4 23.3
78
38
32.8
5296
1224
18.8
44
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45
Table 2--Definitions of variables and ratios
Ac ce p
te
d
M
an
us
cr
ip t
Profitability and performance ratios 1. ROA = return on assets = net income / total assets 2. ROE = return on equity = net income / stockholders’ equity 3. ROD = return on deposits = net income / total deposits 4. NIM = net interest margin = (total interest income – total interest cost) / total assets 5. NNIM = net non-interest margin = (total noninterest income –total noninterest expense) / total assets Asset composition variables 6. SIZE = natural log of total assets 7. LSPEC = loan specialization ratio = total net loans / total assets 8. SECUR = security specialization ratio = other interest bearing assets (non-loans)/ total assets 9. DEPA = deposit specialization ratio = total deposits/ total assets 10. LOANDEP = total net loans to total deposits 11. CTA = cash to assets ratio = cash and cash equivalents / total assets 12. FATA = fixed asset ratio = fixed assets / total assets 13. NIBA = ratio of non-interest bearing assets to total assets Efficiency ratios 14. INEFF = inefficiency ratio = operating expenses/ gross income 15. LCI = labor cost to income = personnel expenses/gross income 16. OOEI = other operating expenses / gross income 17. IEI = interest expense to income = income expenses/ gross income Risk Measures 18. CRISK = credit risk = loan loss provisions/ net loans 19. IMPAIR = impairment ratio = impaired loans / net loans 20. CAPSTR = capital strength = total equity/ total assets 21. Z-score =stability measure = Sum of ROA and CAPSTR / standard deviation of each bank’s ROA Dummy Variables 22. ISLAMIC = dummy variable equal to one if the bank is Islamic, zero for commercial banks 23. UNLISTED = dummy variable equal to one if bank shares are not traded on a stock exchange, nor delisted 24. Country dummies for 21 of 22 countries. Malaysia is the intercept term because it has the most Islamic banks.
Bankscope definitions and conventions used to obtain various Islamic equivalent variables are as follows: Net income = commercial net income before taxes, plus Zakat. Loans = asset value of Mutajata+Murabaha+Istisna+Ijarah+Salam+Musharka+Mudarabah+Wakalat. Customer Deposits = Unrestricted Mudarabah deposits + Murabaha deposits + Medium Term Wakala Financing + savings accounts + current accounts. Interest income = income from Murabaha + Musharka + Istisna + Ijarah + Salam + Mudarabah + Wakalat. Interest expense = distributions (expense) on customer deposits. Non-net interest income = income from commission and fees + portfolio management and Sukuk management fees received. Non-interest expense = portfolio management fees paid + professional and licenses fees paid. Loan loss provisions = net provisions for Islamic financing and investing activities (Murabaha, Musharka, Istisna, Ijarah, Salam, Mudarabah, and Wakalat).
45
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46 Impairment = impaired Murabaha, Musharka, Mudarabah, Istisna, Ijarah, Salam
ip t
Table 3--Means of Variables for Commercial Banks and the differential value for Islamic banks before, during and after the financial crisis of 2008-2009
Variable
cr
Islamic Bank differentials Commercial Banks Before During After Before During After 1996-2007 2008-2009 2010-2014 1996-2007 2008-2009 2010-2014 1.30 12.47 2.30 3.15 -1.41
1.25 11.62 2.22 3.21 -1.59
1.24 10.53 2.28 3.15 -1.56
0.35*** -0.83 1.55*** -0.04 0.21**
-0.08 -3.29*** 1.14** 0.03 -0.22
SIZE LSPEC SECUR CTA FATA NIBA DEPA LOANDEP
13.88 46.67 39.47 8.86 1.77 2.39 69.40 77.27
14.63 50.25 33.68 10.68 1.75 2.43 69.67 80.21
14.84 51.18 33.26 10.67 1.60 2.41 69.34 86.56
-0.52*** 4.45*** -7.07*** 0.11 0.47*** 0.37*** -5.86*** 28.63***
-0.38*** 0.60 -6.92*** 4.05*** 0.56** 0.33** -9.14*** 42.48***
-0.31*** 1.17 -6.33*** 3.43*** 0.54*** 0.23*** -6.61*** 29.38***
INEFF LCI OOEI IEI
81.13 14.64 15.31 53.03
80.49 16.58 16.22 48.26
77.77 18.37 16.92 42.64
-2.28* 2.85*** 5.38*** -10.91***
5.60* 2.17** 8.53*** -4.64**
4.63*** 1.77*** 6.86*** -4.73***
CRISK IMPAIR CAPSTR Z-Score
2.01 9.22 11.25 18.47
1.79 7.21 12.58 19.46
1.47 6.50 13.36 21.93
-0.48*** -3.37*** 5.37*** 0.88
0.46 -3.12*** 3.81*** -1.04
0.18 -1.22** 2.49*** -1.60*
an
M
d
te
Ac ce p
-0.14 -1.57** 0.49 -0.26** 0.03
us
ROA ROE ROD NIM NNIM
All ratios are expressed in percent and mean values are obtained from a regression of each accounting ratio on six dummy variables—one for each of the three time periods across both commercial and Islamic banks. The t-statistics used to judge significance levels are adjusted for heteroskedasticity. Due to missing data only 5977 observations are available for LCI and 5405 for CRISK. Means for all other variables are based on 6520 observations. ***, **, * indicates a ratio for Islamic banks that is significantly different from the same ratio for commercial banks at the 1%, 5%, and 10% levels. Black boldfacing indicates a mean value for an Islamic bank that is statistically larger than the same coefficient for a commercial bank, while red boldfacing shows a coefficient that is statistically smaller. (The results are qualitatively similar using the t-test of means based on unequal variances between bank samples.)
46
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47
Table 4—Determinants of ROA and ROE
(-3.76)*** (0.47) (1.97)* (-0.70) (0.88) (2.23)** (1.11) (0.74) (1.08) (1.32) (-0.23) (0.06) (1.30) (0.03) (0.77) (-1.57) (3.21)*** (2.83)*** (0.69) (1.56) (1.94)* (1.37)
te
Ac ce p
t-statistic (12.49)*** (-0.99) (12.09)*** (-0.57) (11.48) (1.26)
us
70.65% 169.52***
Coefficient 34.12 -0.74 33.22 -0.51 31.41 0.91
an
Adjusted R2 Hausman test
M
-0.159 0.146 0.491 -0.300 0.233 0.523 0.402 0.218 0.330 0.334 -0.105 0.024 0.368 0.012 0.290 -0.529 1.026 0.964 0.214 0.436 0.566 0.575
ROE t-statistic (12.63)*** (1.28) (10.62)*** (1.20) (10.92)*** ( 1.29) (2.12)** (-2.00)** (-87.95)*** (21.88)*** (3.29)***
d
Coefficient 3.644 0.449 3.478 0.448 3.292 0.488 0.029 -0.052 -0.043 0.036 0.003
cr
ROA Independent Variable Constant (PRE-Malaysia) PRE-Islamic (1996-2007) Crisis (2008-2009) Crisis-Islamic After (2010-2014) After-Islamic SIZE SIZE-Islamic INEFF CAPSTR LSPEC SECUR DEPA UNLISTED Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
ip t
Random Effects panel estimation of the determinants of ROA and ROE. The final model is selected from an exhaustive search of the independent (non-profitability) variables in Table 2. A 5% cutoff criterion was used to maximize the log of the likelihood function. The t-statistics are shown in parentheses. Coefficients on continuous independent variables are qualitatively similar for models without dummies included.
-0.290 -0.222
0.021 0.030 -1.093 -3.034 6.305 -12.439 -2.879 5.174 -6.754 -4.946 -3.050 2.847 -1.741 7.531 1.904 -2.267 -1.392 -6.825 5.713 4.939 1.509 3.551 0.074 0.156
(-70.95)*** (-11.02)*** (3.13)*** (3.68)*** (-3.02)*** (-0.75) (1.84)* (-2.40)** (-0.82) (1.58) (-1.63) (-1.29) (-0.79) (0.80) (-0.40) (1.43) (0.51) (-0.51) (-031) (-1.61) (1.41) (1.18) (0.37) (0.95) (0.02) (0.03) 54.96% 104.46***
47
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48 ***, **, and * indicate significance at the 1%, 5% and 10%levels, respectively.
Table 5—Beta Convergence of Financial Ratios
NIM NNIM SIZE LSPEC SECUR DEPA
CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
Ac ce p
LOANDEP
us
-0.720 (-25.66) -0.652 (-27.87) -0.542 (-19.77) -0.492 (-20.98) -0.735 (-27.75) -0.189 (-12.84) -0.454 (-25.44) -0.400 (-21.26) -0.374 (-19.91) -0.400 (-21.96) -0.620 (-28.45) -0.385 (-19.51) -0.347 (-17.35) -0.679 (-30.93) -0.879 (-40.07) -0.549 (-27.37) -0.493 (-25.53) -0.811 (-24.48) -0.546 (-21.67)
an
ROD
0.110 (4.43) 0.028 (0.96) 0.088 (3.87) 0.071 (3.24) 0.044 (1.68) -0.007 (-1.35) -0.009 (-0.96) -0.034 (-1.64) 0.009 (1.21) -0.030 (-3.27) 0.033 (-1.70) 0.063 (3.20) -.033 (-1.57) 0.013 (2.67) 0.045 (2.45) 0.022 (1.87) -0.013 (-1.24) 0.054 (1.42) 0.059 (2.27)
M
ROE
-0.639 (-44.31) -0.640 (-47.34) -0.540 (-38.02) -0.518 (-40.07) -0.613 (-44.37) -0.273 (-30.66) -0.416 (-38.14) -0.433 (-36.91) -0.432 (-39.03) -0.425 (-38.43) -0.590 (-53.79) -0.439 (-36.36) -0.349 (-29.17) -0.554 (-44.69) -0.742 (-65.42) -0.476 (-41.01) -0.440 (-39.31) -0.722 (-43.46) -0.521 (-38.98)
Post-Crisis (2010-2014) β Commercial β Islamic
d
ROA
Entire period (1996-2014) β Commercial β Islamic
te
Variable
cr
ip t
Beta coefficients are from fixed effects panel regressions for the whole period and the post-crisis period. T-statistics are in parentheses. Black boldfacing for the β Commercial coefficients indicates convergence in levels for commercial banks that is significant at the 5% level. The β Islamic coefficients show the differential speed of adjustment for Islamic banks relative to commercial banks. Black (red) boldfacing means significantly faster (slower) at the 5% significance level for Islamic banks relative to commercial banks. Random effect models with listing and country dummies are similar, but the Hausman test rejects the random effects model at the 1% level for all independent variables.
0.088 (2.04) 0.086 (1.80) 0.018 (0.45) 0.009 (0.26) -0.006 (-0.13) -0.011 (-1.76) 0.024 (1.72) -0.119 (-3.59) 0.005 (0.42) -0.005 (-0.38) 0.173 (4.60) -0.009 (-0.32) 0.174 (5.94) 0.001 (0.06) 0.216 (6.49) 0.019 (1.08) -0.035 (-1.83) 0.159 (2.42) 0.181 (4.12)
48
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49 CAPSTR
-0.402 (-37.31) -0.343 (-31.36)
Z-score
0.023 (1.94) 0.032 (1.77)
-0.473 (-23.84) -0.285 (-13.71)
0.039 (2.25) -0.078 (-2.40)
ip t
Table 6-- Sigma Convergence of Financial Ratios
NIM NNIM SIZE LSPEC SECUR
LOANDEP CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
us
-0.014 (-0.56) -0.063 (-2.88) -0.123 (-5.77) 0.011 (0.53) 0.062 (2.69) 0.085 (4.17) 0.100 (3.75) 0.019 (0.93) 0.019 (2.42) 0.062 (2.37) 0.063 (1.70) -0.140 (-4.90)
Ac ce p
DEPA
-0.419 (-34.35) -0.387 (-30.79) -0.406 (-28.12) -0.595 (-52.75) -0.486 (-36.47) -0.414 (-32.82) -0.708 (-47.48) -0.752 (-63.99) -0.610 (-40.71) -0.554 (-41.97) -0.731 (-43.95) -0.416 (-35.94)
-0.726 (-25.39) -0.647 (-23.72) -0.545 (-19.29) -0.491 (-19.09) -0.742 (-27.60) -0.191 (-11.37) -0.367 (-22.03)
an
ROD
0.139 (5.32) 0.052 (1.70) 0.100 (4.20) 0.108 (4.38) 0.045 (1.65) 0.029 (1.35) -0.029 (-1.29)
M
ROE
-0.653 (-44.71) -0.650 (-47.43) -0.544 (-38.06) -0.518 (-38.95) -0.640 (-44.22) -0.271 (-28.69) -0.370 (-30.78)
Post-Crisis (2010-2014) σ Commercial σ Islamic
d
ROA
Entire period (1996-2014) σ Commercial σ Islamic
te
Variable
cr
Sigma coefficients are from fixed effects panel regressions. T-statistics are in parentheses. Black boldfacing for the β Commercial coefficients indicates convergence in levels for commercial banks that is significant at the 5% level. The β Islamic coefficients show the differential speed of adjustment for Islamic banks relative to commercial banks. Black (red) boldfacing means significantly faster (slower) at the 5% significance level for Islamic banks relative to commercial banks. Random effect models with listing and country dummies are similar, but the Hausman test rejects the random effects model at the 1% level for all independent variables.
-0.380 (-18.95) -0.321 (14.49) -0.207 (-8.64) -0.639 (-28.25) -0.427 (-18.98) -0.443 (-20.96) -0.821 (-31.53) -0.25 (-3.82) -0.665 (-24.97) -0.641 (-27.67) -0.811 (-24.42) -0.301 (-14.79)
0.109 (2.32) 0.108 (2.11) 0.026 (1.00) 0.009 (0.21) 0.042 (0.87) 0.108 (3.37) -0.035 (-1.06)
-0.081 (-2.04) -0.104 (-3.12) -0.198 (-6.06) 0.315 (6.06) 0.047 (1.29) 0.174 (5.72) 0.136 (3.03) 0.022 (2.089) 0.133 (3.37) 0.040 (0.93) 0.144 (2.22) -0.294 (-6.04)
49
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50 CAPSTR
-0.513 (-37.01) -0.345 (-29.82)
Z-score
0.109 (4.80) -.007 (-0.32)
-0.549 (-23.80) -0.287 (-14.50)
0.171 (4.46) -0.109 (-2.99)
ip t
Table 7—Gamma (Log t) Convergence of Financial ratios
NIM NNIM SIZE LSPEC SECUR DEPA
CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
Ac ce p
LOANDEP
1.174 (2.98) 1.657 (5.98) -0.038 (-0.25) -0.706 -2.09) 0.153 (0.41) -0.134 (-0.95) 1.176 (2.20) -0.226 (-1.05) -0.032 (-0.38) 0.796 (3.24) 0.826 (0.95) 0.468 (2.76) -2.120 (-14.13) 0.400 (0.95) 0.561 (2.89) 0.278 (1.28) 0.432 (2.55) -1.100 (-3.35) -1.964 (-15.28)
2010 - 2014 γ Islamic γ Commercial
us
0.426 (0.93) 0.348 (0.74) -0.192 (-0.41) 0.650 (4.18) 0.225 (1.31) 0.182 (3.75) 0.037 (0.39) -0.415 (-5.14) -0.585 (-7.28) 0.132 (2.42) 0.730 (4.12) 0.142 (0.81) -0.242 (-2.36) -0.197 (-1.26) 0.688 (4.58) 0.305 (2.77) -0.888 (-3.81) -0.235 (-2.38) -1.194 (-12.93)
γ All
1.081 (2.06) 4.849 (3.25) 0.600 (2.70) -1.019 (-1.53) 0.141 (0.73) -0.723 (-2.29) 1.320 (3.09) -0.267 (-0.61) 0.537 (0.99) 1.479 (9.22) 3.088 (49.49) -0.534 (-2.28) -2.469 (-5.40) -0.274 (-0.51) 0.116 (1.80) -0.087 (-0.76) 0.128 (0.76) -0.484 (-0.58) -1.354 (-6.75)
an
ROD
-0.301 (-0.73) -0.445 (-1.13) -0.086 (-0.81) 0.327 (2.51) 0.266 (0.92) 0.051 (0.48) 0.234 (1.34) -0.207 (-1.58) -1.126 (-4.06) 0.011 (0.07) 1.078 (4.09) 0.044 (0.54) -0.540 (-2.30) -0.955 (-3.96) -1.167 (-4.46) -0.104 (-0.78) -0.733 (-4.13) -0.614 (-1.31) -0.708) (-4.80)
γ Commercial
M
ROE
0.175 (0.29) 0.190 (0.44) -0.181 (-0.62) 0.523 (4.07) 0.235 (1.22) 0.172 (2.99) 0.061 (0.60) -0.404 (-7.31) -0.788 (-8.56) 0.070 (1.45) 0.773 (4.41) 0.094 (0.63) -0.321 (-2.57) -0.426 (-2.82) 0.118 (1.04) 0.151 (1.34) -0.875 (-4.52) -0.296 (-2.58) -1.109 (-14.16)
1996 – 2014 γ Islamic
d
ROA
γ All
te
Variable
cr
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
1.359 (3.86) 0.734 (1.62) -0.371 (-1.16) -0.535 (-1.80) 0.143 (0.33) 0.011 (0.09) 1.146 (2.00) .0.119 (-0.87) -0.269 (-0.81) 0.551 (1.38) 0.084 (0.07) 0.879 (6.13) -1.888 (-10.80) 0.854 (2.22) 1.196 (4.66) 0.544 (2.07) 0.743 (3.81) -1.156 (-2.43) -2.137 (-14.49)
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51 CAPSTR
0.625 (12.71) 0.061 (0.49)
Z-score
0.675 (10.33) 0.589 (4.50)
-0.057 (-0.17) -1.034 (-10.58)
0.612 (11.56) -0.060 (-0.46)
0.158 (0.36) -1.215 (-6.64)
-0.448 (-3.69) -0.509 (-1.73)
ip t
Table 8—Gamma (Log t) Club convergence results for profitability ratios by region
NIM NNIM
Southeast Asia region γ All Variable ROA ROE
NNIM
Africa region Variable ROA ROE NIM NNIM
γ All
-0.435 (-1.18) -0.234 (-1.06) -0.621 (-3.32) -0.018 (-0.17)
-0.766 (-1.99) -0.160 (-0.32) -.001 (-.00) 0.505 (2.14)
0.041 (0.10) -0.230 (-0.47) 0.446 (5.69) 0.478 (3.05)
1996 – 2014 γ Islamic γ Commercial
-0.544 (-1.50) -0.797 (-0.82) -0.280 (-0.48) 0.003 (0.01)
us
3.001 (2.15) 6.329 (3.57) 0.352 (1.27) 0.263 (0.33)
1996 – 2014 γ Islamic γ Commercial
Ac ce p
NIM
-0.078 (-0.21) -0.190 (-0.48) 0.253 (2.22) 0.469 (2.84)
-1.801 (-3.51) -0.859 (-1.75) -0.680 (-6.35) 0.881 (0.96)
2010 - 2014 γ Islamic 2.680 (1.65) 29.66 (3.84) 1.091 (2.06) 0.273 (0.34)
an
ROE
-1.51 (-3.38) -4.581 (-1.90) 1.410 (3.75) 1.844 (1.56)
γ All
M
-1.756 (-3.97) -1.994 (-3.38) 0.347 (1.51) 1.186 (1.14)
1996 – 2014 γ Islamic γ Commercial
d
ROA
γ All
te
GCC region Variable
cr
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
-0.225 (-0.65 -0.158 (-0.61) -0.453 (-4.45) -0.069 (-.72)
γ All -0.786 (-1.77) -0.887 (-1.67) -0.644 (-1.71) 1.243 (2.78)
2010 - 2014 γ Islamic -1.391 (-5.49) -1.456 (-0.92) -1.740 (-1.88) 0.696 (2.02)
γ All
2010 - 2014 γ Islamic
3.439 (83.92) 2.785 (2.90) -1.578 (-16.18) -1.189 (-2.09)
4.034 (18.45) 6.940 (8.65) -1.173 (-11.56) -1.149 (-1.71)
γ Commercial 4.108 (2.90) 2.189 (0.83) -0.175 (-0.39) 0.200 (0.23)
γ Commercial -0.576 (-1.12) -0.770 (-1.16) -0.190 (-0.64) 1.375 (2.53)
γ Commercial 3.096 (25.29) 1.882 (1.38) -1.951 (-57.62) -1.184 (-2.16)
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52
Table 9—Gamma (Log t) Club convergence results for profitability ratios—Clustering by high, middle and low historical return on asset groups of countries
NIM NNIM
ROA ROE NIM NNIM
γ All -0.435 (-1.18) -0.233 (-1.06) -0.621 (-3.32) -0.008 (-0.17)
-0.544 (-1.50) -0.797 (-0.81) -0.281 (-0.48) 0.003 (0.01)
2010 - 2014 γ Islamic
us
3.145 (5.23) 4.021 (12.83) -0.646 (-3.31) -8.864 (-3.21)
1996 – 2014 γ Islamic γ Commercial
Ac ce p
Low ROA cluster Variable
-0.532 (-1.11) 0.053 (0.09) -1.435 (-6.71) -1.114 (-1.21)
-1.137 (-3.51) -0.618 (-1.51) 0.180 (1.38) -1.023 (-0.98)
γ All
3.561 (5.91) 5.276 (13.29) 0.515 (1.16) -8.756 (-3.35)
an
ROE
γ Commercial
M
-0.837 (-2.06) -0.344 (-0.69) -0.740 (-15.93 -1.076 (-1.12)
1996 – 2014 γ Islamic
d
ROA
γ All
-0.225 (-0.65) -0.128 (-0.60) -0.453 (-4.45) 0.069 (0.72)
te
High ROA cluster Variable
cr
ip t
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
γ All 3.439 (83.92) 2.785 (2.90) -1.578 (-16.18) -1.189 (-2.09)
γ Commercial 2.792 (3.64) 2.941 (4.07) -1.439 (-6.41) -8.952 (-3.12)
2010 - 2014 γ Islamic γ Commercial 4.074 (18.45) 6.940 (8.65) -1.173 (-11.56) -1.145 (-1.71)
3.096 (25.29) 1.881 (1.38) -1.951 (57.62 -1.184 (-2.16)
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53
Figure 1
2.8
16
ROE
ROA 2.4
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Panel A: Profitability Ratios
cr
14
2.0 12
us
1.6 10 1.2 8
0.8
6 1998
2000
2002
2004 ROAI
2006
2008
2010
2012
2014
1996
1998
ROAC
2000
2002
2004
ROEI
7
4.0 ROD
2006
2008
2010
2012
2014
ROEC
M
1996
an
0.4
NIM
6
3.6
5
d
3.2
4
2.8
te
3
2.4
1 1996
1998
Ac ce p
2
2000
2002
2004
RODI
-0.6
2006
2008
2010
2012
2014
2.0
1996
1998
RODC
2000
2002
2004 NIMI
2006
2008
2010
2012
2014
NIMC
NNIM
-0.8 -1.0 -1.2 -1.4 -1.6 -1.8 -2.0 -2.2 1996
1998
2000
2002
2004 NNIMI
2006
2008
2010
2012
2014
NNIMC
53
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54
Figure 1 (continued)
ip t
Panel B: Asset Composition Ratios 70 LSPEC
15.2 SIZE
65 14.8
60
cr
14.4
55
14.0
45
13.2
40
12.8 1998
2000
2002
2004 SIZEI
2006
2008
2010
2012
2014
1996
1998
2000
2002
2004
48
72
DEPA
Securitization Ratio 70
44
68 40
66 64 62
32
60 28
58 56 1998
2000
2002
2004
SECRATI
2006
2008
2010
2012
2014
1996
SECRATC
130
16
2000
te
LOANDEP
1998
120
2002
d
24
2008
LSPECC
2010
2012
2014
M
36
1996
2006
LSPECI
SIZEC
an
1996
us
50
13.6
2004 DEPAI
2006
2008
2010
2012
2014
DEPAC
CTA
14
110
12
100
Ac ce p
10
90
8
80
6
70 60
4
1996
1998
2000
2002
2004
LOANDEPI
3.6
2006
2008
2010
2012
2014
1996
1998
2000
2002
2004
LOANDEPC
CTAI
2006
2008
2010
2012
2014
2010
2012
2014
CTAC
2.8
FATA
NIBA
3.4
2.6
3.2
2.4
3.0
2.2
2.8
2.0
2.6
1.8
2.4
1.6
2.2
1.4
2.0
1.2 1996
1998
2000
2002
2004 NIBAI
2006
2008
NIBAC
2010
2012
2014
1996
1998
2000
2002
2004 FATAI
2006
2008
FATAC
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55
Figure 1 (continued)
24
Inefficiency Ratio
22
84
20
80
18
76
16
72
14
68
cr
LCI
88
LCII
us
92
ip t
Panel C: Efficiency Ratios
2004
2006
12 1998
2000
2002
2004 INEFFI
2006
2008
2010
2012
2014
1996
1998
2000
INEFFC
28
2002
2004
2006
2008
2010
2012
2014
LCIC
an
1996
65 OOEI
IEI
26
60
24
20
M
55
22
50
18
45
16
12
d
40
14
35
2000
2002
2004 OOEII
2006
2008
OOEIC
2010
2012
2014
1996
1998
2000
te
1998
2002
IEII
2008
2010
2012
2014
IEIC
Ac ce p
1996
55
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56
Panel D: Risk Measures 4.0
ip t
CRISK 3.5
3.0
cr
2.5
2.0
us
1.5
1.0 1996
1998
2000
2002
2004 CRISKI
2006
2008
2010
2012
2014
CRISKC
11
22 Impairment Ratio
CAPSTR
10 9
an
20 18
8 16 7 14
M
6 12
5
10
4 3
8 1998
2000
2002
2004 IMPAIRI
2006
2008
2010
2012
2014
1996
1998
2000
d
1996
IMPAIRC
24
2004
CAPSTRI
2006
2008
2010
2012
2014
CAPSTRC
te
Z-Score
2002
22
18
16
14 1996
1998
Ac ce p
20
2000
2002
2004
ZEDI
2006
2008
2010
2012
2014
ZEDC
56
Page 54 of 54
S1062-9769(16)30041-2 http://dx.doi.org/doi:10.1016/j.qref.2016.06.013 QUAECO 950
To appear in:
The
Received date: Revised date: Accepted date:
3-1-2016 26-6-2016 29-6-2016
Quarterly
Review
of
Economics
and
Finance
Please cite this article as: Olson, D., and Zoubi, T.,Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis, Quarterly Review of Economics and Finance (2016), http://dx.doi.org/10.1016/j.qref.2016.06.013 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
an
us
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Dennis Olson (Corresponding author) Department of Economics and Finance Gulf University for Science & Technology Block 5, Building 1 Mubarak Al-Abdullah Area/West Mishref Kuwait [email protected]
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Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis
and
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d
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Taisier Zoubi Department of Accounting School of Business Administration American University of Sharjah P.O. Box 26666 Sharjah, UAE +9716 515-2367 [email protected]
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Convergence in bank performance for commercial and Islamic banks during and after the Global Financial Crisis Abstract
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M
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This study examines whether the Global Financial Crisis (GFC) has led to a convergence in performance between Islamic and commercial banks in the Middle East, Africa, and Southeast Asia (MENASA) region in recent years. Using the largest sample to date for 1996 - 2014, we find that Islamic banks (IBs) initially weathered the onslaught of the GFC better than commercial banks (CBs) in 2007-2008. Then, as the crisis spread to the real economy in 2009, profitability declined substantially for IBs relative to CBs. Beta and sigma convergence tests suggest convergence toward the mean for all banks and all financial ratios. The speed of convergence is generally slower for Islamic banks but the difference has declined in the aftermath of the GFC. The recently developed more robust Phillips and Sul (2007a) log-t test for convergence shows little convergence over the whole sample period, but for the years 2010-2014, all banks appear to be converging toward similar levels of profitability as measured by ROA and ROE. The log-t test shows convergence in profitability across all banks (IBs and CBs) in the post-crisis period. However, it does not show convergence across all asset composition and risk measure—meaning that IBs and CBs still operate differently even if they are moving toward similar profitability results. Club convergence results indicate a lack of convergence over the whole sample, but quite strong convergence across all banks post-crisis. However, some clusters, such as the Southeast Asia region does not display convergence in profitability ratios—suggesting that the GFC has differentially impacted various countries and regions.
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JEL Classification: G21, G15 Keywords: Islamic and commercial banks, bank performance, financial crisis, convergence tests, speed of adjustment
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Highlights
We examine whether the Global Financial Crisis has led to a convergence in performance between Islamic and commercial banks in the Middle East, Africa, and Southeast Asia.
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We test for beta and sigma convergence of bank financial ratios and apply the Phillips and Sul (2007a) log-t test to check for convergence.
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Loan specialization, securitization, and stock market listing are positively related to bank profitability for both IBs and CBs, while cost inefficiency is negatively related to profitability. Capitalization is positively related to return on assets, but negatively related to return on equity.
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Our beta and sigma convergence tests suggest that IBs and CBs are both converging toward the mean for all banks in the MENASA region over a range of financial ratios during the period 1996-2014. CBs generally display faster convergence than IBs— particularly for variables such as ROA. However, differences in speed of adjustment between IBs and CBs have generally lessened in the post-crisis years.
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te
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The log t test shows convergence across all banks in terms of profitability (ROA and ROE) and ROE--to a greater extent in the aftermath of the Global Financial Crisis. However, IBs are not converging toward the MENASA mean across all variables, which shows there are still some operational differences between IBs and CBs.
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1. Introduction During the last decade, the Islamic banking sector has become one of the fastest growing
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industries world-wide. There are currently more than 300 Islamic banks (IBs) operating around the world with total assets worth more than $1.50 trillion US dollars. These banks will likely
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continue to grow at a fast pace in countries where they are presently operating due to increases in the Muslim population, which is projected to reach 2.3 billion by 2030, up from 1.7 billion in
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2014. Also, even though Islamic finance has not significantly penetrated the banking markets in
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Western countries, serving the banking needs of the sizeable Muslim populations in these nations represents an important opportunity for future growth.
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In general, IBs are governed by Sharia rules and principles that lead to five important differences between the operations of Islamic and commercial banks (CBs).1 (1) The most
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important characteristic of IBs is the prohibition of both the receipt and payment of interest
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(riba) in all transactions. (2) Secondly, although IBs obtain funds primarily by issuing equity
Ac ce p
and receiving customer deposits, CBs can also issue debt instruments to finance their investment activities. (3) A third tenet of Islamic finance is the principle of risk sharing on both sides of the balance sheet. To raise funds, IBs use profit sharing (mainly Mudarabah-based) arrangements rather than conventional deposits. On the asset side, in place of conventional loans, IBs issue exchange-based contracts (Murabaha), lease-based contracts (Ijarah and Istisna), and profitsharing contracts (Mudarabah and Musharka). Although the profit/loss sharing contracts may
1
Commercial banks represent about 80% of the bank-year observations available in Bankscope for “conventional” banks in the Middle East, North African and Southeast Asia (MENASA) region. Data for bank holding companies, coop banks, investment banks, microfinance institutions, multi-lateral government banks, central banks, real estate banks, credit institutions, and investment and trust corporations are not included in our analysis because these specialty financial institutions often operate differently than full-service commercial and Islamic banks.
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embody the central tenets of Islamic finance better than the exchange-based or lease-based products, IBs have been cautious in relying upon Mudarabah and Musharka contracts due to their higher perceived risk.2 (4) The fourth major difference between IBs and CBs involves
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restrictions on the uses of funds. CBs face few investment constraints except for the need to meet desired levels of return and risk. In contrast, IBs are limited in the types of investments they
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can offer to customers due to Sharia-imposed Islamic restrictions. For example, IBs may not
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invest in haram activities, such as gambling, alcohol production or distribution, gambling, etc. IBs are also precluded from undertaking many types of potentially rewarding, but excessively
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risky investments. This restriction follows from the Islamic prohibition on gharar, which precludes gambling and most types of speculation. As a result, IBs facing a restricted set of
M
investment opportunities may be more prone to risk concentration and may be less flexible in
d
responding to changing economic conditions than CBs.3 (5) Finally, internal corporate
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governance structure adds to the differences between CBs and IBs since the latter need to establish a “Sharia Committee” consisting of experts in Islamic law whose role is to ensure that
Ac ce p
IBs’ activities are in compliance with Sharia. As a result, IBs exhibit a “multi-layered” corporate governance structure that includes the Sharia Committee in addition to the regular board of directors. The Sharia Committee restrains the board of directors and the management of the bank from taking excessive risk, such as creating credit against credit (which would be in A more detailed description about the specifics of these Islamic financial products is available in the Appendix. The interested reader is also referred to Abedifar et al. (2013) for a thorough discussion of the principles of Islamic banking and finance. 3 For instance, the cost plus contract (Murabaha), widely utilized by IBs, is based on the sale of a physical asset to a customer on a cost plus profit basis (e.g., a car purchase financed through a sale to the customer after the bank purchases the car). Profit on the sale is fixed at inception. Unlike CBs, IBs cannot profit from changes in the underlying inter-bank offer rates. However, IBs can hold Sukuk and make similar profits to CBs. 6 2
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violation of riba), or undertaking doubtful investments such as sale of goods which are not present at hand (which would be in violation of gharar). Mollah and Zaman (2015) show that Sharia supervisory boards have a positive impact on Islamic bank profitability, while Sharia
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boards that merely serve in an advisory capacity have a negligible impact on performance.
Against this background, an exacerbated debate on the comparative performance of IBs
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and CBs has emerged during the recent financial crisis. Academic and anecdotal evidence
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suggest that IBs weathered the onslaught of the Global Financial Crisis (GFC) in 2007-2008 better than CBs [Bourkhis and Nabil, 2013; Alqahtani et al. (2016)]. Khan and Crowne-
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Mohammed (2009-2010) and Hassan and Dridi (2010) argue that Islamic banking principles (particularly investment restrictions that prohibited IBs from holding derivatives and other
M
potentially “toxic” assets) protected IBs during the GFC, while Čihák and Hesse (2010), Rajhi
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(2013), Bourkhis and Nabi (2013), and Mobarek and Kalonov (2014) show that IBs (particularly
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small IBs) in the Middle East, Africa, and Asia are more financially stable than CBs. Nevertheless, Rashwan (2010) notes that the superior performance of IBs during the initial
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financial phase of the GFC shifts to under performance in 2009 as the financial crisis spread to the real economy. For the years 2007 – 2009, Bourkhis and Nabi (2013, p.68) find “no significant difference in terms of the effect of the financial crisis on the soundness of Islamic and conventional banks.” A comparative study conducted by Olson and Zoubi (2008) establishes that while IBs and CBs in the Middle East operated similarly, financial ratios used in nonlinear classification models could effectively help to distinguish between the two types of banks. In the aftermath of the GFC, competitive banking conditions could cause a further convergence between IBs and CBs. For example, Escribano and Stucchi (2014) found that
7
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recessions lead to productivity convergence across Spanish manufacturing firms by creating symmetric incentives for less productive firms to become more competitive. The issues of comparative performance and whether there are significant differences between the business
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orientation of IBs and CBs can be formally addressed by adopting techniques used in the recent literature on economic convergence. Three commonly used tests for convergence and economic
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integration include tests for beta, sigma, and log t-tests of convergence. Results from tests of
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banking sector integration in Europe provide mixed results for what to expect for IBs and CBs in the MENASA region. Weill (2009) and Casu and Girardone (2010) find beta and sigma
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convergence in European bank cost efficiency ratios using data up to 2005, but Rughoo and Sarantis (2012) and Matousek et.al. (2015) mostly reject convergence based on log t-tests once
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the years of the financial crisis are included in the analysis.4
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The primary purpose of this paper is to determine whether there has been a convergence
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in performance across a range of financial variables between Islamic and commercial banks in the MENASA region during and after the Global Financial Crisis5. We also address the
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comparative performance and profitability of IBs and CBs over time and examine the speed of adjustment for both types of banks to changing economic conditions. Adopting a dynamic panel approach to analyze the determinants of individual bank profitability and financial stability, in Alternatively, panel unit root tests could be used to test for stationarity and convergence. However, Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) all point out that panel unit root tests are less precise than Phillips and Sul (2007a) log t-test of convergence. Based on studies such as Guetat and Serranito (2007), we could expect results similar to those obtained when examining beta and sigma convergence for any data period. Although not reported in the paper, various panel unit root tests did produce similar results to those from beta and sigma convergence tests. 5 The Global financial crisis started in U.S. and Europe in late 2007 [Jutasompakor et al. (2014)] and took several months to fully transmit its negative effect to the MENASA banking sector. Hence, the GFC did not significantly affect MENASA banks until 2008-2009. 8 4
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general we find that the same variables are significant for both IBs and CBs and have a similar impact on both types of banks. Consistent with Berger and Bouwman (2013), we highlight the crucial role of higher bank capitalization in explaining bank performance and profitability (ROA)
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over the years 1996-2014. Also, we observe that loan specialization and securitization ratios are positively related to bank profitability for both IBs and CBs while cost inefficiency and being
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unlisted (not being listed or even delisted from a national stock market) are negatively related to
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bank profitability. The variables determining financial performance are similar for both CBs and IBs—suggesting that profitability is determined more by individual bank characteristics than by
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the type of bank. More formally, beta, sigma, and log t convergence tests indicate that although there are still some differences between IBs and CBs, both types of MENASA banks have been
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generally converging toward similar values in the aftermath of the Global Financial Crisis. Our
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results are consistent with arguments made by Chong and Lui (2009) and Khan (2010) that IBs
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have become more similar to their commercial counterparts in recent years. Our results have particular implications for regulators and bank managers: First, although
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IBs are governed by Islamic principles, profitability of both types of banks is determined more by individual bank characteristics than by type of bank. Therefore, managerial performance can be assessed on the basis of profitability for both IBs and CBs. Nevertheless, there are several noticeable differences in financial ratios between bank types. Although convergence across all MENASA banks has occurred across most profitability ratios and some asset composition and efficiency ratios, convergence is generally rejected across risk measures and some asset composition ratios. Thus, regulators should not treat the two types of banks identically when setting up and implementing bank regulations.
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Our results show that IBs weathered the initial onslaught of the Global Financial crisis in 2007-2008 better than CBs. However, as the financial crisis spread to the real economy, the profitability of IBs fell relative to CBs in 2009-2010. From 2011 to 2014, IBs and CBs have
CBs have recovered.
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moved toward similar levels of profitability, but IBs have still not recovered to the extent that Based on beta, sigma and t-log tests, it seems that all MENASA banks are
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converging toward common values for profitability ratios, such as return on assets and return on
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equity. However, results are mixed for convergence on net interest and net non-interest margins. The overall evidence may suggest that even though profitability is converging, both IBs and CBs
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continue to operate differently and that there are still some country differences. Also, the speed of convergence varies by geographical area—meaning that the GFC has had a differential impact
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across countries and regions.
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The remainder of the paper is structured as follows. Section 2 provides a review of the
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literature on the determinants of profitability and bank efficiency while Section 3 describes the data and sample. Section 4 next examines profitability ratios for IBs and CBs, followed by
Ac ce p
Section 5 that considers other financial ratios and differences between IBs and CBs over time. Section 6 analyzes convergence and the speed of adjustment for CBs and IBs over various time periods. Section 7 concludes. 2. Literature Review
Historically, IBs have been shown to be more profitable than CBs. Over the period 1990 to 2006, Ariff et al. (2011) report a return on average assets (return on average equity) of 1.40% (11.61%) for IBs compared to 1.27% (9.61%) for CBs in the same countries. However, the net interest margin (NIM) of the latter was 3.91% compared to 3.66% for IBs. For the years 2000 10
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2005, Olson and Zoubi (2008) show that IBs in the Gulf Cooperation Council (GCC) region had a 2.40% annual average return on assets (ROA) and 18.20% return on equity (ROE) compared to 2.00% and 14.40% respectively for CBs. For the years 2000-2008, Olson and Zoubi (2011)
ip t
show that IBs in the Middle East and North Africa (MENA) region had 2.69% ROA (20.18% ROE) compared to 1.56% ROA (12.80% ROE) for CBs. Alqahtani et al. (2016) adopt a
cr
CAMEL framework and find that IBs led (lagged) CBs in terms of profitability in the early
us
(later) stage of the financial crisis period for a sample of Islamic and conventional banks from the GCC region. To summarize, the literature indicates that IBs have generally outperformed
an
CBs up to 2009 (i.e., before and in the initial year of the Global Financial Crisis). Delving into the determinants of banks profitability, Demirgüç-Kunt and Huizinga (1999)
M
find that profitability differences among banks in 80 countries over the period 1988-1995 could
d
be reasonably explained by a set of bank-specific financial ratios (e.g., staff expenses to total
te
assets, cash and securities to total assets, bank capital to total assets, size, etc.), macroeconomic variables (e.g., money supply growth, inflation, and interest rates), and industry variables (e.g.,
Ac ce p
concentration and ownership structure). Several studies have followed this seminal work to examine the profitability of Islamic banks [e.g., Mollah et. al. (2016)]. More recent studies using accounting ratios to investigate the determinants of performance generally adopt panel techniques, rather than simple ordinary least squares. To illustrate, Olson and Zoubi (2011) explore the determinants of profitability of IBs and CBs in ten MENA countries for the years 2000-2008 and show that larger bank size, greater dependence upon loans for revenue, higher market concentration, greater GDP growth, and higher proportions of equity capital to assets are generally associated with greater profitability. Higher
11
Page 10 of 54
liquidity, greater provisions for loan losses, cost inefficiencies, and more reliance on debt have been indicative of lower bank profits. The authors also find that profitability is positively related to capitalization strength, as measured by the equity to assets ratio and in each bank’s loan
ip t
specialization ratio, which is the degree to which a bank relies on loans relative to other earning assets. Performance is found to be negatively affected by cost inefficiencies and credit risk (as
cr
measured by loan loss provisions to total loans).
us
Another strand of the literature examines the financial stability of Islamic versus conventional banks using z-scores6. Čihák and Hesse (2010) focus on banks in 18 countries
an
during the years 1993-2004, and find that large CBs are more stable over time than large IBs, but that small IBs are more financially stable than large IBs or small CBs. Bourkhis and Nabi (2013)
M
adopt a paired approach to 34 IBs and 34 CBs across 16 countries for the years 2006 – 2009 and
d
examine the comparative soundness of the two types of banks during the GFC. Using the Z-
te
score indicator of bank stability, they conclude that there is no significant difference between the stability of IBs and CBs over the entire period. Beck et al. (2013) further investigate the reasons
Ac ce p
for these differences and argue that CBs are less stable than IBs primarily in those countries where IBs have a larger market share. Mobarek and Kalonov (2014) examine the relationship between efficiency and financial stability of both IBs and CBs using the Z-score methodology. Their results indicate that IBs are more financial stable than CBs. Rajhi (2013) extends the analysis of Čihák and Hesse (2010) and confirms their results using robust estimation techniques on a data set for the years 2000-2008 that contains substantial outliers,7 Abedifar et al., (2013)
6
Although there are some differences across studies in constructing this variable, the z-score consists of the sum of the expected ROA plus the equity to assets ratios, divided by the variability of returns (i.e., the standard deviation of each bank’s return on assets). 7 He finds that the median Southeast Asian Islamic bank has a significantly higher z-score of financial
12
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assess the risk in Islamic banking, and report that IBs (particularly small IBs) have lower credit risk than CBs across 23 countries for the years 1999-2009. However, in contrast to previous work, they find that IBs have become less financially stable over time and faced greater
ip t
insolvency risk than CBs during the years 2008-2009. Grira et al. (2016) estimate the implicit risk-based premiums for deposit insurance of Islamic banks and conventional banks for the
cr
period 1999-2013. The results of this study suggest that publicly listed conventional banks are
us
riskier than their Islamic counterparts.
Which type of bank has been less damaged by the global financial crisis? Tentative
an
answers to this question may be found in Čihák and Hesse (2010) and Hasan and Dridi (2010). Hasan and Dridi (2010) examine the impact of the GFC on CBs and IBs in emerging markets for
M
2007 – 2009 and find that IBs performed better than CBs in 2007 – 2008. They argue that weak
te
profitability in 2009.
d
risk management practices in some IBs may then have contributed to a large decline in IBs
Beck et al. (2013, p.445) using data over the entire period 2005-2009 show that “the
Ac ce p
higher capitalization and better asset quality have helped IBs outperform CBs during the latest crisis.” Nevertheless, an extensive comparative analysis of the performance for the two types of banks for years after 2009 (post-crisis years) is, to the best of our knowledge, yet to be documented8.
To summarize, the extant literature indicates that IBs are less affected initially by financial shocks, but that CBs may adjust more quickly to longer-term changes in economic stability than the median CBs. He also confirms that smaller IBs are more stable than larger IBs.
Some clues about the relative performance of IBs and CBs post-crisis appear in few studies. For instance, Dahduli (2010) reports that ROA and ROE of IBs improved relative to CBs in 2008. 13 8
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conditions. If there are benefits from both types of banking models, IBs and CBs potentially could learn from one another so that performance could converge across all banks in the MENASA region in the aftermath of the GFC. To more formally test this hypothesis, we refer to
ip t
the growing literature on economic convergence that is used to test whether a variable or ratio is moving toward some common current or historical mean value for that variable. To date, four
cr
main approaches have been adopted to test for convergence: beta convergence, sigma
us
convergence, stochastic convergence based upon unit root and cointegration tests, and finally relative convergence based on log t-tests.
an
The solution adopted in the economic growth literature pioneered by Barro (1991) and Barro and Sala-i-Martin (1992) has been to look at changes from one period to the next in
M
income or output growth, or in the dispersion in the cross sectional values of a variable between
d
each period. Beta convergence can be measured by regressing the growth rate of a variable on
te
the beginning of period level of that variable. For example, when applied to income or GDP growth, there is beta convergence if countries or regions with low values grow at a faster rate
Ac ce p
than those with high initial levels of income or output. Hence, the beta coefficient in the so called “Barro regression” must be negative. For banking sector data, beta convergence would imply mean reversion in panel units, or for individual banks. Sigma convergence, also attributable to Barro (1991) and Barro and Sala-i-Martin (1992), tests how quickly the level of some variable converges to the cross sectional average of some group of countries, regions, or firms. Sigma convergence requires that the beta-like coefficient of dispersion away from the cross sectional annual mean is negative. Beta convergence is a necessary, but not sufficient condition for sigma convergence and sigma convergence in the banking sector would occur if
14
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there was an overall reduction in cross sectional dispersion of some financial variable over time. In a study relevant for what might be expected in the MENASA region, Weill (2009) uses data for the banking sector in ten European countries for the years 1994 – 2005 and finds evidence of
ip t
beta and sigma convergence in the levels of cost efficiency across countries. Such results
support the notion of increased financial integration in the banking sector over time. Similarly,
cr
Casu and Girardone (2010) support beta and sigma convergence in bank efficiency across 15
us
European countries using data for 1997 – 2003. Beta and sigma convergence tests, since their development in the 1990s, have certainly become the most frequently used tests for convergence
an
and have been applied to test convergence for a wide range of economic and financial variables across many countries.
M
Stochastic convergence is often applied to panel data and it focuses on unit root tests of
d
stationarity to determine whether a variable is stationary and reverts to some mean value over
te
time, or whether it just wanders and fails to mean-revert or converge to some value. To illustrate the application of such tests, Guetat and Serranito (2007) applied panel unit root tests to examine
Ac ce p
the convergence of GDP per capita across 11 MENA countries over the period 1960 – 2000. They reported mixed results with levels of convergence varying by time period considered, but generally found convergence across countries for the years 1960 – 1990 and then convergence clubs of various countries over the entire period or for various subperiods. Lin and Huang (2012) applied a battery of univariate and panel stationarity test to measures of income inequality across 48 states in US for the years 1916 – 2005. Although traditional univariate unit root tests and simple panel tests failed to reject non-stationarity, the more sophisticated panel techniques that include structural breaks and cross correlation rejected the unit root hypothesis and
15
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supported stochastic convergence in inter-state incomes. Nevertheless, Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) point out that panel unit root tests require many
test for convergence as developed by Phillips and Sul (2007a).
ip t
restrictive assumptions and are not as precise as measures of relative transition, such as the log t-
Currently, the Phillips and Sul (2007a) log t-test represents the state of the art in testing
cr
for convergence in any variable. It is generally applied to panel data and convergence cannot be
us
rejected if the gamma coefficient on the relative transition parameter in a log t regression is positive, or if it is negative and insignificant. This relative transition variable measures the
an
dispersion of a financial variable away from the annual mean in an initial period divided by dispersion in later periods. Convergence requires cross sectional dispersion to decrease over
M
time and with the way this variable is constructed, it happens when the relative transition
d
variable is positively related to time. For banking sector data, the log t-test can be used to test
te
for overall convergence across all banks, for convergence clubs that include only some countries, or for clubs that contain only certain types of banks. Hence, following Phillips and Sul (2007b),
Ac ce p
the log t-test can be used to see whether IBs are converging toward their own mean or toward the mean of all banks (IBs plus CBs). The same tests can be done to see if CBs converge toward the mean of all banks. When applied to the question of financial integration in the European banking sector, Rughoo and Sarantis (2012) adopt the log t-test and support convergence in deposit and lending rates across 15 European countries for the years 2003-2007. However, when the years 2008-2011 are included, the null of convergence in the deposit and credit markets is strongly rejected. Similarly, Matousek et al. (2015) reject convergence in bank cost efficiency across the 15 countries in the Eurozone for the years 2005 – 2012. Based on the European
16
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experience, the GFC may have reduced convergence in performance of all banks across countries, but our focus in the next section is only on whether IBs and CBs as a group are becoming more alike over time.
ip t
3. Data and Sample
To identify the determinants of bank profitability in emerging markets and to examine
cr
operational differences between IBs and CBs, we collect financial statement data from
us
Bankscope for the years 1996 – 2014.9 This database provides standardized figures so that most of the items are comparable across time and banks. Furthermore, many prior studies [i.e.
an
Abedifar et al. 2013; Daher et al. 2015] used the Bankscope data as a reliable source for Islamic bank data. The period of analysis represents the years for which electronic data are currently
M
available for 22 countries in Asia and Africa that have both IBs and CBs in their national
d
banking sector. Although IBs and Islamic windows exist outside these 22 countries, they do not
te
represent a significant portion of the banking sector in those countries. For example, IBs in the United Kingdom consist of only ½ of 1 % of total bank-year observations available in Bankscope
Ac ce p
or that country and Iran is excluded because it had no CBs during the period of analysis. Data for Algeria, Morocco, Gambia, Kazakhstan, and Senegal have been omitted because of too few usable observations. Although banking in Sudan and Pakistan follows general Islamic principles, data are included because Bankscope categorizes each bank as either an IB or a CB. Panel A of Table 1 shows the number of bank-year observations for 22 countries: Bahrain, Bangladesh, Brunei, Egypt, Indonesia, Iraq, Jordan, Kuwait, Lebanon, Malaysia, Mauritania,
External variables affecting bank performance (e.g., inflation, GDP) were collected from the International Monetary Fund (IMF). In general, however, country dummies provided greater explanatory power for firm performance than country-specific macroeconomic variables. 17 9
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Oman, Pakistan, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, Turkey, United Arab Emirates, and Yemen for the years 1996-2014. The data contain 6520 bank-years of observations for the various financial variables used in this study. All data are expressed in US
ip t
dollars and the definitions of the variables are given in Table 2. These variables include a set of
variables that capture individual bank characteristics.
us
Insert Tables 1 and 2 about here
cr
profitability ratios, asset composition measures, efficiency ratios, risk measures, and dummy
The largest number of usable data points come from Indonesia—950 bank-year observations,
an
while Malaysia has the most Islamic bank-year observations—172. Palestine and Brunei have only 43 and 46 usable observations. The annual sample size begins with 240 observations in
M
1996 and peaks at 454 in 2013. Due to problems with outliers, we only included banks with
d
positive reported numbers for equity, deposits, fixed assets, operating expenses, operating
te
income, net loans, interest expense, and interest income. To obtain the largest possible data set, we included banks with missing observations for cash, labor expenses, and loan loss provisions,
Ac ce p
but then deleted those observations when calculating financial ratios involving those variables. Finally, we followed Beck et al (2013) and winsorized all variables at the 1% and 99th percentiles.
Panel B of Table 1 shows the composition of banks by country and by type (IB or CB) over the entire period of the data sample. There are s 5296 bank-year observations for CBs and 1224 observations for IBs. IBs comprise about 19% of our sample, while country percentages of usable observations for IBs range from about 2% in Lebanon up 75% in Sudan. 4. Profitability and bank financial ratios 18
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Table 2 shows the financial ratios examined in our study, beginning with five measures of profitability. Panel A of Figure 1 graphically indicates the average annual values for profitability as measured by return on assets (ROA), return on equity (ROE), net interest margin
ip t
(NIM), net non-interest margin (NNIM), and return on deposits (ROD)] for IBs and CBs for the years 1996-2014. Kosmidou et al. (2007) and Van Horen (2007) have argued that ROA is the
cr
most useful measure of profitability over time because assets have a direct impact on both
us
income and expenses. However, our discussion below examines both ROA and ROE.10
an
Insert Figure 1 about here
Average ROA for IBs and CBs were similar for the years 1996-1999. Then, as IBs
M
proliferated across Asia and Africa in the period 2000 - 2007, ROA for IBs initially dropped in 2000, but remained notable high relative to CBs for the years 2001 - 2008. Note that average
d
ROAs for both IBs and CBs in 2007 (2.12% and 1.52%, respectively) were above historical
te
averages for 1996 – 2006 (1.41% and 1.27%). Similarly, Panel A of Figure 1 indicates peak
Ac ce p
profitability for ROE and ROD for the years 2004-2007 and a big drop from 2007 to 2008 and from 2008 to 2009. Hence, we define the financial crisis period for MENASA banks as the years 2008-2009.
During the financial phase of the crisis in 2008, average ROA fell to 1.49% for IBs and to 1.30% for CBs in 2008. Then, as the crisis spread to the real economy, the economic phase of
Bankscope also provides data for the return on average assets (ROAA). Its denominator is calculated as the average of the end of period and beginning of period total assets, rather than from end of period total assets for ROA. The two ratios are over 98% correlated, so results for ROAA are not presented. Similarly, the Bankscope return on average equity (ROAE) is 93% correlated with ROE. 10
19
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the GFC reduced average ROA to 0.85% for IBs and to 1.22% for CBs in 2009. For 2010-2014 average ROA has been 1.24% for commercial banks and 1.09% for Islamic banks. Looking at the time pattern of response of banks to the financial crisis, IBs may initially weather the shock
ip t
better, but they decline more in terms of profitability in a prolonged crisis. As pointed out by Grassa (2012), Islamic profit-loss sharing products present greater insolvency risk than products
cr
offered by CBs and this type of risk has a more detrimental impact on performance during a
us
prolonged crisis. IBs have taken longer to move back toward pre-crisis levels of profitability, but differences in recovery speed may be due to the large expansion of Islamic banking in the
an
pre-crisis era and reflect a type of mean reversion that was hastened by the Global Financial Crisis. Also, profitability ratios for Islamic banks show greater year to year variability than those
M
for commercial banks so that the current underperformance of IBs relative to CBs may be due to
d
normal fluctuations.
te
Panels B – E of Figure 1 present average annual values for the variables presented in Table 2. These are bank-specific variables that have been used in a variety of studies to explain
Ac ce p
bank performance and profitability. These panels illustrate the trend over time for various asset composition variables, efficiency ratios, and measures of risk for IBs and CBs in the MENASA region. Although there appear to be some convergence in profitability ratios between IBs and CBs, there is less convergence in other ratios. To further assess the convergence proposition, Table 3 shows the average values for CBs
before, during and after the crisis of 2008-2009. Values are obtained from ordinary least squares regressions of each variable on three dummy variables, adjusted for heteroskedasticity and autocorrelation. The dummy variable PRE represents pre-crisis years (1996-2007), CRISIS is
20
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for the years 2008 – 2009, and AFTER is a dummy variable for period 2010 – 2014. Insert Table 3 about here
ip t
The profitability ratios, ROA and ROD were significantly larger at the 1% level for IBs relative to CBs for 1996-2007. ROD remained higher for IBs during 2008-2009, but both ROA
cr
and ROD are essentially the same for both bank types in the aftermath of the crisis. ROE was
us
similar for IBs and CBs in the pre-crisis era, but ROE has been significantly smaller for IBs after 2007.
an
NIM was similar for IBs and CBs from 1996 – 2009, but then smaller for IBs in the years 2010 – 2014. The net interest margin has been slightly higher for CBs relative to IBs over the
M
entire period 1996 – 2014. In contrast, NNIM has been slightly higher on average for IBs over the entire period.
te
d
Regarding the asset composition ratios, Islamic banks are smaller than commercial banks. They tend to have higher loan specialization ratios, loan to deposit ratios, cash to asset ratios,
Ac ce p
fixed assets to total assets, and greater proportion of non-interest bearing assets than commercial banks. IBs have lower security specialization and deposit to asset ratios. IBs had lower inefficiency ratios pre-crisis, but they have significantly higher inefficiency ratios than CBs from 2008 – 2014. IBs have higher labor costs and other operating expense to income ratios, but lower interest expense to income ratios than CBs. For the risk measures, IBs had significantly lower credit risk pre-crisis, but have since taken on more such risk than CBs. IBs have lower loan impairment ratios and greater capital strength than CBs. Nevertheless the Z-scores for IBs have fallen relative to CBs since 2008. In summary, IBs and CBs have similar levels of profitability, but they do it with different operating characteristics due to different asset 21
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composition ratios and different risk exposures. 5. Determinants of ROA and ROE A substantial body of literature has examined the variables that determine individual bank
ip t
profitability. Our purpose in this section is to discover whether the determinants differ between
cr
IBs and CBs. Following Olson and Zoubi (2011), we estimate ROA using an unbalanced
dynamic panel model with the financial variables listed in Table 2 as the independent variables
us
that may identify operational and profitability differences between IBs and CBs.
an
The basic framework for our random effects panel model is: ROAit = αi + β Xit + Dummy Variables + εit,
(for i = 1, 6520)
(1)
M
where ROAit is the dependent variable, α is the common intercept across banks in the random effects model, Xit is a vector of explanatory variables described in Table 2 (including bank-
d
specific accounting ratios and financial variables such as size), β is a vector of regression
te
coefficients, and εit is the disturbance term that is assumed to be normally distributed with a
Ac ce p
mean of zero. Dummy Variables include country dummies for the 21 of the 22 countries with Malaysia as the intercept term and an UNLISTED dummy variable that equals one if a bank is neither listed on a national stock exchange, nor delisted from a national exchange. The constant (α) term can be further expanded to illustrate differences between time periods (PRE, CRISIS, and AFTER) and between IBs and CBs in any of the three time periods. The PRE-Malaysia constant term in Table 4 is a projected base ROA for commercial banks in Malaysia in the precrisis period given the values of other variables. Any country could have served as the constant and the choice of Malaysia rather arbitrary, but Malaysia was selected as the constant because it has the most bank-year observations for Islamic banks and because it is reasonably close to the 22
Page 21 of 54
sample average for the various profitability ratios. Hence, PRE-Islamic denotes the differential returns to IBs relative to Malaysian CBs in 1996 – 2007, Crisis (2008-2009) is the base point
the crisis, and After and After-Islamic apply to the years 2010-2014.
ip t
ROA for Malaysian CBs during the crisis, Crisis-Islamic is the differential return for IBs during
Although a fixed effects model dominates the random effects specification on the basis of
cr
the log of the likelihood function, results from the random effects model are presented to
us
illustrate the impact of country-specific dummies and the impact of stock market listing. The adjusted R-squared value is taken from the equivalent fixed effects model that does not include
an
the time-invariant dummy variables.
M
Insert Table 4 about here
The final coefficients selected in the Table 4 are based on an exhaustive search to
d
maximize the log likelihood function using a 5% cutoff value for coefficients, as estimated by
te
version 9.1 of the RATS statistical package. For each non-dummy variable in Table 2, the
Ac ce p
financial variable was multiplied by an Islamic dummy variable to determine if there were any differences in the determinants of ROA between IBs and CBs. Only one variable, SIZE-Islamic is significantly different from the coefficient for CBs. Although SIZE is positively related to profitability for CBs, it is negatively related to ROA for IBs. This result confirms the findings of Beck et al (2013) that smaller IBs are more profitable than larger IBs. Other explanatory variables are roughly of the same magnitude and sign for both IBs and CBs. Overall, Table 4 indicates that the determinants of individual bank profitability, as measured by ROA, are essentially the same for both IBs and CBs. The coefficients on the pre-crisis, crisis, and aftercrisis dummy variables for differences between IBs and CBs are insignificant. That is, 23
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individual bank characteristics determine profitability. To summarize the ROA results from Table 4, more profitable banks have greater capital strength and loan specialization ratios, but lower inefficiency ratios. Hence, issuing loans,
ip t
controlling costs, and maintaining adequate equity capitalization are important for all banks, regardless of whether they are Islamic or commercial banks. Similar variables have been
cr
identified in previous studies as the primary determinants of individual bank profitability. For
us
example, Kosmidou et al (2007) finds that larger bank size is a major determinant of profitability. Berger and Bouwman (2013) show that having adequate bank capital (CAPSTR) is
an
an important factor for bank survival during financial crises. Finally, the banking literature has focused substantially on cost efficiency and the inefficiency ratio appears to be perhaps the major
d
significant determinant of profitability.
M
determinant of profitability for our sample of IBs and CBs. For our data set, risk is not a
te
The dummy variable UNLISTED is negatively related to profitability, meaning that stock market listing contributes to profitability. Stock market listing is another measure of size, age,
Ac ce p
and the reputation of the bank. The significantly positive country dummies for Bangladesh, Indonesia, Sudan, Syria, and the United Arab Emirates show above average profitability for banks in those countries, even after adjusting for the impact of individual bank characteristics. None of the countries show distinctly lower ROA than the reference country of Malaysia, but the lower ROA for Saudi Arabia would be significant if a country such as the United Arab Emirates Turkey, or Indonesia were used as the reference country instead of Malaysia. Similar to Beck et al. (2013) once individual bank results have been adjusted for variables like capital strength and the inefficiency ratios, there is no significant difference 24
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between the performance of IBs and CBs. The determinants of ROA are essentially the same for both IBs and CBs—it is primarily individual differences in bank characteristics that explain performance differences and not whether a bank is commercial or Islamic.
ip t
Equation (1) can be used to examine the determinants of ROE and the results are shown
cr
in Table 4. Explanatory variables are similar to those for ROA with one exception. Capital strength is negatively related to ROE. The reason is that higher values of equity in the
us
denominator of the ROE ratio reduce its value—apparently by more than higher equity increases net income. As was the case for ROA the inefficiency ratio is negatively related to profitability
an
and appears to be the most significant determinant of ROE. The securitization ratio is positively
M
related to ROE, while the loan specialization ratio is not a significant determinant of ROE. The deposit to assets ratio is also positively related to ROE. It is also positively related to ROA, but
d
loses significance once other variables are included in the ROA equation. UNLISTED is
te
significantly negatively related to ROE, as it is to ROA. Once again the PRE-Islamic, CrisisIslamic, and After-Islamic dummy variables are insignificant in explaining profitability
Ac ce p
differences between banks. ROE in Bangladesh is significantly higher than in the reference country and ROE is significantly lower in Brunei. Once again profitability would be significantly lower in Saudi Arabia than other countries if a country such as Turkey had been used as a reference point, instead of Malaysia. The determinants of ROE are similar to those for ROA and serve as a robustness check for measures of profitability. Basically, bank profitability is determined by individual bank-specific variables such as the inefficiency ratio, capital strength, asset specialization ratios, and whether a bank is listed or unlisted. The banking models may be different, but profitability does not seem to be determined by whether a bank is an 25
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Islamic or a commercial bank. 6. Convergence and speed of adjustment The graphical results presented in Figure 1 as well as the general lack of significance in
ip t
differentials between IBs and CBs after the GFC in Table 3 might suggest that the two types of
cr
banks are becoming more similar over time. To more formally test this hypothesis, we refer to the growing literature on economic convergence that is used to test whether a variable or ratio is
us
converging toward some common current or historical mean value. This issue has been addressed by four different approaches: beta convergence, sigma convergence, panel unit root
an
tests, and log t convergence tests. However, for the sake of brevity, we do not present results for
M
panel unit root tests because studies such as Kim and Rous (2012), Aspergis et al. (2014), and Caporale et al. (2014) indicate that such tests are generally not as precise as the Phillips and Sul
te
6.1 Beta convergence
d
(2007a) log t-test of convergence.
We denote yit (for bank i in year t) as the value of any of the ratios or bank variables
Ac ce p
listed in Table 2. Then, following procedures developed by Barro and Sala-i-Martin (1992), beta convergence is then estimated from a fixed effects panel estimator of the following equation:
ln (yit / yit 1 ) ln ( yit 1 ) Islamic ln ( yit 1 ) Islamici it ,
(2)
where β and βIslamic are the coefficients to be estimated for each financial variable, ln is the natural log function, and εit is an error term. Beta convergence occurs if the coefficient for β based on the initial beginning of period level of the variable or ratio is negative.11 In Equation
11
For ratios that can take on negative values (e.g., ROA, ROE, NNIM, etc.), Equation (2) is estimated using the log of (1 + yit) with the ratio expressed in decimal format. These variables could instead be
26
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(2), the beta coefficient represents speed of adjustment and it is further divided so that β represents commercial banks and βIslamic shows the differential speed of adjustment for IBs relative to CBs. As shown in Table 5, all of the beta coefficients (labeled βCommercial) are negative
ip t
and statistically for all 21 financial variables. This indicates that all banks are converging toward some common value for each ratio over time. The βIslamic coefficients are positive and
cr
statistically significant for 7 of 21 ratios: ROA, ROD, NIM, NIBA, INEFF, LCI and IMPAIR.
us
IBs adjust faster than CBs only for the case of the loan to deposits ratio. In general, IBs adjust to changes slower than CBs across several variables; however, given the small magnitude of the
an
Islamic differentials, IBs still display convergence toward some overall mean for each ratio. Moving to the After-Crisis period of 2010-2014, all 21 variables display convergence toward a
M
common value. For many of the ratios, convergence is faster than over the entire period, but the
d
positive Islamic differential for slower convergence is still observed for 7 of 21 financial
te
variables. Focusing on ROA, IBs react slower than CBs to profitability changes, both over the entire period and in the post-crisis years. However, both IBs and CBs appear to be converging
Ac ce p
toward the mean for all banks in the MENASA region. 6.2 Sigma convergence
We denote the difference between the value of yit for an individual bank and the cross n
y
sectional mean of that variable for each year as: Wit ln (yit ) (1/ n)
i 1
it
. Then sigma
convergence is estimated from a fixed effects panel estimator of the following equation: estimated using raw, untransformed data. For all variables, there was little difference between using log or non-log data in testing for convergence. Some studies, such as Casu and Girardone (2010) include lagged values of the dependent variable in equations (1) and (2). This formulation for beta and sigma convergence tests had little impact on other coefficients and we adopt the formulation in the paper for greater simplicity.
27
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ln (Wit / Wit 1 ) ln ( yit 1 ) Islamic ln ( Wit 1 ) Islamici it ,
(3)
where Wit replaces the yit values in Equation (2), and sigma convergence requires that β < 0. Sigma convergence measures whether there is a lessening in the cross sectional dispersion in
ip t
each variable over time. It is a more stringent condition than beta convergence because beta
cr
convergence is a necessary, but not sufficient condition for sigma convergence. Nevertheless, the results for sigma conversion in Table 6 are nearly identical to those for beta convergence in
us
Table 5. Sigma convergence across all 21 variables is indicated by the negative coefficients on βCommercial over the whole period. Similarly, sigma convergence is observed for all 21 ratios over
an
the period 2010-2014. Once again IBs display slower convergence than CBs for 7 of 21
M
variables over the whole period and for the post-crisis period. Overall, sigma convergence is observed across all banks; with CBs generally shower faster convergence than IBs—particularly
te
6.3 Gamma (Log t) convergence
d
for variables such as ROA.
Since its introduction in 2007, the Phillips and Sul (2007a) log t-test has become the
Ac ce p
preferred test for measuring convergence of economic or financial variables. It offers better discriminatory power than tests for beta convergence, sigma convergence, or various unit root and cointegration tests for stationarity or co-movement between variables. Since the now standard methodology for relative transition parameters and log t-tests of convergence has been extensively presented by Phillips and Sul (2007a, 2007b, and 2009) and summarized in Matousek et al. (2015), we only briefly discuss how the test is developed for our data set. We define the relative transition parameter hit as:
28
Page 27 of 54
hit
yit
(4)
n
(1/ n) yit i 1
ip t
It describes the transition path for each bank relative to the panel or cross section average in each year. The average value for hit must be one, so the cross sectional dispersion of the relative
n
ln ( H1 / Ht ) (1/ n) (hit 1)2 .
(5)
an
i 1
us
cr
transition parameters relative to their average in any year may be defined as:
Then, Phillips and Sul (2007a) perform an ordinary least squares regression on the
M
dispersion of the relative transition parameters (Ht) as follows:
(6)
d
ln(H1/ Ht ) 2ln[ln(t1)] a ln(t) et
te
This notation follows Phillips and Sul (2009), where et is the error term and the
Ac ce p
regression equation is adjusted so that the parameters are corrected for heteroskedasticity and autocorrelation. Since the γ parameter on log t indicates speed of adjustment, Equation (6) can be referred to as a test for gamma convergence. However, it is similar to sigma convergence since it involves the dispersion of cross sectional variance over time. Values of γ ≥ 2 imply convergence in levels for a variable or ratio, while values of 2 > γ ≥ 0 imply conditional convergence or that growth rates converge over time. Convergence requires that the cross sectional dispersion in a variable decreases over time. Since H1 represents dispersion in the first year and Ht is the dispersion in any year, the ratio (H1/Ht) will increase over time if convergence is occurring (because Ht will decline relative to H1). Hence, convergence requires a positive 29
Page 28 of 54
gamma coefficient. Phillips and Sul (2007a) have set up the log t test so that the null hypothesis is convergence over time. If the t-statistic for the γ coefficient is t < -1.65, the null hypothesis of convergence can be rejected with a one-sided t-test at the 5% confidence level. If t > -1.65 the
ip t
null hypothesis of convergence is accepted. The -2 ln [ln(t+1)] term in our Equation (6) behaves like a penalty function for the regression and has been defined as -2 ln [ln(t)] in some empirical
cr
applications of the log t-test. Finally, many studies de-trend the data using a Hodrick-Prescott
us
filter. We experimented with this filter and various smoothing techniques for the ROA variable and found that the results were about the same as winsorizing at the 2nd and 98th percentile levels.
an
Hence, we adopted this rather simple winsorizing technique for all 21 financial ratios when implementing the log t-test.
M
To implement the log t regressions, Phillips and Sul (2007a) recommend discarding from
d
20% to 30% of the observations after calculating ln (H1/Ht). For the full sample, H1 is 1996, but
te
we discard the first five of nineteen years (about 26% of the data) to estimate the log t regressions. To examine convergence in the more recent 2008 – 2014 period, H1 becomes 2008.
Ac ce p
We then discard the first two of seven years (about 29% of the data) and convergence is examined for 2010-2014.
Table 7 shows the results of the log t convergence test over the entire period from 1996 – 2014, as well as for 2010-2014. The second column (for γAll) shows significantly large negative gamma coefficients for 7 of 21 variables—meaning that the log-t tests rejects convergence across all banks for these seven ratios. The gamma coefficients are insignificant for 10 of 21 variables (including ROA) —meaning that the log t results are inconclusive regarding convergence. Finally, the γAll coefficients are positive and statistically significant for 4 of 21 ratios (NIM, 30
Page 29 of 54
SIZE, CTA, and CAPSTR) and suggest convergence in rates for these ratios. The coefficients for γ Islamic and γ Commercial are obtained from separate log-t regressions following the procedures in equations (4) – (6), except that yit represents only Islamic or only
ip t
commercial banks in equation (4) and the cross sectional mean is the mean ratio for all banks (IBs and CBs) in a given year. Note that the signs and magnitudes of the γ Islamic and γ Commercial
cr
coefficients are similar for most variables. These coefficients indicate whether IB financial
us
variables are converging toward the values for those of all banks and whether CB financial variables are converging toward the values for those of all banks. The results provide only weak
an
support for convergence, but they show that there is little difference in convergence characteristics between IBs and CBs in the MENASA region.
M
The right-most three columns in Table 7 examine convergence in the post-crisis period of
d
2010-2014. Positive statistically significant gamma coefficients indicate convergence across 7 of
te
21 variables. Ratios have been converging to a greater extent in the aftermath of the Global Financial Crisis than was seen in earlier years. In particular, we note that convergence in rates
Ac ce p
across all banks has occurred for our two main measures of profitability—ROA and ROE. Results are inconclusive for 9 ratios and convergence is rejected for only 5 of 21 ratios (NIM, FATA, IMPAIR, CRISK, and Z-score). Signs and magnitudes of gamma coefficients are similar for both IBs and CBs—once again indicating little difference between IBs and CBs. Note that convergence is rejected across all banks for the net interest margin—a variable where one might expect differences between IBs and CBs given the prohibition of riba in Islamic banking. Also, three of the non-converging variables--credit risk, the impairment ratio, and Z-score—are all measures of risk in banking. Since IBs and CBs operate under different principles, the risks
31
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facing them may be different so that convergence across risk ratios does not occur. This is consistent with findings of Baele et al. (2014) that default rates on Islamic loans in Pakistan were only about one-half that for loans issued by commercial banks. To summarize, competitive
ip t
pressures in the aftermath of the GFC may have led to converge in profitability measures across MENASA banks. However, since IBs and CBs operate differently, they have reached the
cr
profitability results in different ways so that there has not been a convergence in all asset
us
composition and risk ratios. 6.4 Gamma (Log t) club convergence
an
Studies that find little evidence of overall convergence often test for club convergence
M
across a subset of countries or firms where convergence still may occur. Since we generally support convergence in profitability ratios across all banks, further testing for club convergence
d
should only strengthen our results. Nevertheless, we follow the example of Phillips and Sul
te
(2007a, 2007b, 2009) and test for club convergence with various subsets of our data. We limit our tests to the profitability ratios ROA, ROE, NIM, and NNIM based on clustering using only
Ac ce p
the ROA variable. Based on our previous results, we expect to discover stronger convergence in clubs for ROA and ROE, but lack of convergence in NIM. The results for NNIM are unclear a priori.
Club convergence results for three geographical regions are shown in Table 8. First is the Gulf Cooperation Council (the six GCC countries) consisting of Bahrain, Kuwait, Oman, Qatar, United Arab Emirates and Saudi Arabia. The second region is Southeast Asia, consisting of Bangladesh, Brunei, Indonesia, Malaysia and Pakistan. Third is the Africa region consisting of banks in Egypt, Mauritania, Sudan and Tunisia. Other regions could be considered, but our 32
Page 31 of 54
results are illustrative of what could be expected based on geographical regions. For the GCC, convergence results for the entire period 1996-2014 are about the same as for all countries in Table 7. For 2010-2014, we observe much stronger club convergence across
ip t
all banks for both ROA and ROE. In fact, the results suggest convergence in levels, which is a
cr
stronger result than convergence in rates observed in Table 7. There is no evidence of
convergence across all banks for NIM or NNIM, but we observe convergence in rates for Islamic
us
banks relative to all banks in the GCC region for the variable NIM.
an
Results for Southeast Asia are mixed with convergence in rates across all banks for the variables NIM and NNIM over the whole period 1996-2014. However, for 2010-2014
M
convergence is rejected for ROA, ROE, and NIM. Such results probably have arisen because of the heterogeneous nature of the countries placed in this group.
d
For Africa, results are inconclusive for 1996-2014 except for the rejection of convergence
te
in NIM. For 2010-2014, results are as hypothesized. There is convergence in levels across
Ac ce p
African banks for ROA and ROE. Convergence is rejected across all banks for NIM and NNIM. Once again the signs and magnitudes of the gamma coefficients are similar for IBs and CBs. The club convergence results are more decisive than seen for all MENASA countries in Table 7. Another way to select clubs is to adopt a clustering algorithm, as in Phillips and Sul (2007a, 2007b, 2009). We place countries identified as having high historical ROA in one group and countries with low historical ROA in another group. The high ROA cluster consists of Iraq, Qatar, Saudi Arabia, Sudan, and the United Arab Emirates. This group is similar to the GCC in composition and not surprisingly the results for 2010-2014 support convergence in levels across all banks in the cluster for the profitability variables ROA and ROE. Convergence is rejected 33
Page 32 of 54
across all banks for the variables NIM and NNIM. The low ROA cluster consists of the countries Brunei, Egypt, Lebanon, Malaysia, Pakistan, Syria, and Tunisia, and is a combination of banks from the Africa and Southeast Asia
ip t
regions. For the period 2010-2014, convergence in levels across all banks is found for ROA and
cr
ROE. Convergence is rejected for NIM and NNIM. A third club of middle ROA countries
us
could be formed, but not surprisingly, most of the results for this cluster were inconclusive. 7. Summary and conclusion
an
This paper has examined the profitability of commercial and Islamic banks before, during and after the financial crisis of 2008-2009. Using the largest sample to date of Islamic and
M
commercial banks operating in Africa, Asia, and the Middle East, we confirm that Islamic banks were more profitable and more financially stable than their commercial counterparts prior to the
d
Global Financial Crisis. Islamic banks weathered the initial onslaught (financial phase) of the
te
crisis better than their commercial counterparts in 2008. However, as the crisis spread to the real
Ac ce p
economy (economic phase) in 2009, Islamic banks noticeably underperformed commercial banks. Islamic banks have been catching up, but at a slower rate than commercial banks, and they have still not returned to pre-crisis levels of profitability. Convergence tests indicate that all banks are converging toward similar levels of profitability as measured by return on assets and return on equity, but convergence is not occurring across all financial ratios. In particular, banks are not converging across net income margins or risk characteristics. This non-convergence results hold for the whole period from 1996 to 2014 and in the post-crash period of 2010-2014. A possible explanation is that competitive pressures force banks toward convergence in profitability metrics. However, because Islamic and commercial 34
Page 33 of 54
banks have different operating philosophies, the profitability results are obtained in different ways so that convergence does not occur across risk measures and some asset composition ratios. Our findings have several policy implications: Although there have been periods where one
ip t
type of bank outperformed the other, we expect them to perform rather similarly in the future. Since Islamic banks and commercial banks maintain different asset composition characteristics
cr
and risk profiles, regulators need not apply all of the same regulations to both types of banks.
us
Even though IBs are governed by Islamic principles, profitability of both types of banks is determined more by the characteristics of the individual bank than the type of bank (IBs vs.
an
CBs). Hence, the performance of bank’s management can be assessed on the basis of profitability measures for both IBs and CBs. Yet, there are several noticeable differences in
M
financial ratios between bank types. Thus, regulators should not treat the two types of banks
d
identically when setting up and implementing bank regulations. Islamic banks still need to find
te
ways to enhance non-interest based revenues, while commercial banks should note that a more conservative philosophy toward banking as evidenced by Islamic principles may serve them to
Ac ce p
avoid losses caused by the so called “toxic assets” that may have contributed to the Global Financial Crisis.
35
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References Abedifar, P., Hasan, I., Tarazi, A. 2016. Finance-growth nexus and dual-banking systems: Relative importance of Islamic banks. Journal of Economic Behavior & Organization, forthcoming.
ip t
Abedifar, P., Molyneux, P., Tarazi, A., 2013. Risk in Islamic banking. Review of Finance 17, 2035-2096.
cr
Alqahtani, F., Mayes, D., Brown, K. 2016. Economic turmoil and Islamic banking: Evidence from the Gulf Cooperation Council. Pacific-Basin Finance Journal 39, 44-56.
us
Ariff, M., Badar, M., Shamser, M., Hassan, T., 2011. Performance of Islamic and conventional banks. In Foundations of Islamic Banking, Ariff, M., Iqbal, M. (eds.)
an
Aspergis, N. Christou, C. Miller, S. 2014. Country and industry convergence of equity markets: International evidence from club convergence and clustering. North American Journal of Economics and Finance 29, 36-58.
M
Baele, L., Farooq, M., Ongena, S., 2014. Of religion and redemption: Evidence from default on Islamic loans. Journal of Banking and Finance 44, 141-159.
d
Barro, R., 1991. A Cross-Country Study of Growth, Saving, and Government.
te
Barro, R., Sala-i-Martin, X. 1992. Convergence. Journal of Political Economy 100, 223-251.
Ac ce p
Beck, T., Demirgüç-Kunt, A., Merrouche, O., 2013. Islamic vs. conventional banking: Business model, efficiency and stability. Journal of Banking and Finance 27, 433-447. Berger, A., Bouwman, C. 2013. How does capital affect bank performance during financial crises? Journal of Financial Economics, 109, 146-176. Bourkhis, K., Nabi, M., 2013. Islamic and conventional banks’ soundness during the 2007-2008 financial crisis. Review of Financial Economics 22, 68-77. Caporale, G., Erdogan, B., Kuzin, V. 2014. Testing stock market convergence: A non-linear factor approach. Empirica, published online. Casu, B., Girardone, C. 2010, Integration and efficiency convergence in EU banking markets. Omega 38, 260-267. Čihák, M., Hesse, H., 2010. Islamic banks and financial stability: An empirical analysis. Journal of Financial Services Research 38 (2-3), 95-113.
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Chong, B., Lui, M. 2009. Islamic banking: Interest-free of interest-based? Pacific Basin Finance Journal 17(1), 125-144.
ip t
Dahduli, M., 2010. Islamic banking and the credit crunch. Working paper, Bangor University, UK.
cr
Daher, H., Masih, M., Ibrahim, M. 2015. The unique risk exposures of Islamic banks’ capital buffers: A dynamic panel data analysis. Journal of International Financial Markets, Institutions and Money 36, 36-52.
us
Demirgüç-Kunt, A., Huizinga, H., 1999. Determinants of commercial bank interest margins and profitability: Some international evidence. The World Bank Economic Review 13, 379-408.
an
Escribano, A., Stucchi, R., 2014. Does recession drive convergence in firms’ productivity? Evidence from Spanish manufacturing firms. Journal of Productivity Analysis 41, 339-349.
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Grassa, R., 2012. Islamic banks' income structure and risk: evidence from GCC countries. Accounting Research Journal 25, 227-241.
d
Grira, J., Hassan, M., Soumare, I. 2016. Pricing beliefs: Empirical evidence from the implied cost of deposit insurance for Islamic banks. Economic Modelling 55, 152-168.
te
Guetat, I., Serranito, F., 2007. Income convergence within the MENA countries: A panel unit root approach. Quarterly Review of Economics and Finance 46, 685-706.
Ac ce p
Hasan, M. & J. Dridi J., 2010. The effects of the global crisis on Islamic and conventional banks: A comparative Study. IMF working paper 10/201. Jutasompakor, P., Brooks, R., Brown, C., Treepongkaruna, S., 2014. Banking crises: Identifying dates and determinants. Journal of International Financial Markets, Institutions and Money 32, 150-166. Kim, Y., Rous, J. 2012. House price convergence: Evidence from U.S. state and metropolitan area panels, working paper. Khan, F., 2010. How “Islamic” is Islamic banking? Journal of Economic Behavior & Organization 76, 805-820. Khan, B., Crowne-Mohammed, E.A., 2009-2010. The value of Islamic banking in the current financial crisis. Review of Banking and Financial Law 29, 441-464.
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Kosmidou, K., Pasiouras, F., Tsaklanganos, A., 2007. Domestic and multinational determinants of foreign bank profits: The case of Greek banks operating abroad. Journal of Multinational Financial Management 17, 1-15.
ip t
Lin, P., Huang, H, 2012. Inequality convergence revisited: Evidence from stationarity panel tests with breaks and cross correlation. Economic Modelling 29, 316-325.
cr
Matousek, R., Rughoo, A., Sarantis, N., Assaf, A., 2015. Bank performance and convergence during the financial crisis: Evidence from the ‘old’ European Union and Eurozone. Journal of Banking and Finance 52, 208-216.
us
Mobarek, A., Kalonov, A. 2014. Comparative performance analysis between conventional and Islamic banks: empirical evidence from OIC countries. Applied Economics 46, 253-270.
an
Mollah, S., Zaman, M. 2015. Shari’ah supervision, corporate governance and performance: conventional vs. Islamic banks. Journal of Banking and Finance 58, 418-435. Mollah, S., Hassan, M., Farooque, O., Mobarek, A. 2016. The governance, risk-taking, and performance of Islamic banks. Journal of Financial Services Research (forthcoming).
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Olson, D., Zoubi, T., 2008. Using accounting ratios to distinguish between Islamic and conventional banks in the GCC region. International Journal of Accounting 43, 45-65.
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d
Olson, D., Zoubi, T., 2011. Efficiency and bank profitability in MENA countries. Emerging Markets Review 12, 94-110.
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Phillips, P., Sul, D. 2007a. Transition modelling and econometric convergence tests. Econometrica 75, 1771-1855. Phillips, P., Sul, D. 2007b. Some empirics on economic growth under heterogeneous technology. Journal of Macroeconomics 29, 455-469. Phillips, P., Sul, D. 2009. Economic transition and growth. Journal of Applied Econometrics 24, 1153-1185. Rajhi, W., 2013. Islamic banks and financial stability: A comparative empirical analysis between MENA and Southeast Asian Countries. Région et Développement 37, forthcoming. Rashwan, M., 2010. A comparison between Islamic and traditional banks: Pre and post the 2008 financial crisis. Working paper, British University in Egypt. Rughoo, A., Sarantis, N. 2012. Integration in European retail banking: Evidence from savings and lending rates to non-financial corporations. Journal of International Financial Markets, Institutions and Money 22, 1307-1327. 38
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Van Horen, N., 2007. Foreign banks in developing countries: Origin matters. Emerging Markets Review 8, 81-105.
Ac ce p
te
d
M
an
us
cr
ip t
Weill, L., 2009. Convergence in banking efficiency across European countries. Journal of International Financial Markets, Institutions & Money 19, 818-833.
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Appendix In general, there are two types of Mudarabah: restricted and unrestricted. Under the restricted Mudarabah, the IB receives the money and the customer specifies a particular business for which
ip t
the IB shall invest the money in. IB acts as a fund manager and normally the funds are
accounted for off-balance sheet. In Unrestricted Mudarabah, an IB receives funds from
cr
depositors that it is subsequently allowed to use the funds in any activity that the management
us
feels appropriate, so long as the activities are not forbidden by Islamic Sharia. In doing so, the IB makes the funds available to entrepreneurs who have ideas and expertise to use the funds in
an
productive activities. The bank and its client share profits but the client does not share in losses. IB usually pools all profits and losses from different Mudarabah investments (entrepreneurs) and
M
shares the profit with depositors of funds based on a pre-agreed ratio. IBs are thus looked upon
d
as partners with both depositors and entrepreneurs, and they share risk with both.
te
In Musharka, IB provides part of the needed funds to an entrepreneur who has a specific project. The partners’ contributions do not have to be equal and contributions may be in the form
Ac ce p
of physical or intangible capital, such as machinery, labor or management. IB may or may not participate in the management of the business. Both parties share profits on a pre-agreed ratio, but the losses are shared based on the ratio of capital provided by each party. This is a joint venture between the bank and the client designed for a certain project and ending within an agreed period of time.
Another type of contract is Ijarah (leasing). It is designed for financing mainly tangible assets such as aircrafts vehicles, equipment and machinery. The IB buys the asset and then leases the asset to an entrepreneur for a mutually agreed charge. IB receives periodic payments
40
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from the lessee so long as the leased-property can be used by the lessee and it is carried as an asset in the balance sheet of the IB (lessor). At the end of the lease term, the ownership (title) of the leased-property can be transferred to the entrepreneur (Ijarah Muntahia Bittamlek). Thus,
ip t
the IB is an ‘investor’ when providing financing for asset services using Ijarah, and perhaps when providing financing for asset acquisitions using Ijarah Muntahia Bittamlek.
cr
Murabaha, not profit-sharing contract, is one of the most widely utilized forms of
us
financing contract by IBs where the bank purchases for a client certain commodities based on her request. The client promises to buy the goods from the bank on a pre-agreed profit basis. The IB
an
may briefly bear some asset price risk in respect of the item being sold, but may mitigate this by requiring a deposit from the customer (e.g., car financed through a sale to the customer after the
M
bank purchases the car). The bank justifies the profit on the grounds that it takes some risks
d
because the client has no legal obligation to fulfill his initial promise. Murabaha in general is
not an investor.
te
used to provide sale on credit resulting in account receivable. In this case, the bank is a creditor
Ac ce p
IBs replace loans with investments that are generally riskier than secured interest bearing loans. The account receivable resulting from the sale is no more risky than an unsecured loan to a customer with a similar credit standing, and may be collateralized in which case it is no more risky than a loan with similar security.
41
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Totals
ip t
1999 7 27 3 23 33 2 11 8 38 23 2 5 15 1 6 8 6 1 15 19 17
2000 7 29 3 22 44 2 1 8 39 21 4 5 13 1 6 9 6 1 17 17 17
2001 8 33 3 22 38 2 13 8 30 24 4 5 15 1 7 9 8 1 17 21 18
2002 7 33 3 23 36 1 13 8 30 26 6 5 13 1 7 9 9 0 16 24 19
4
5
240
251
2003 10 33 3 23 38 1 13 8 29 26 7 5 18 2 7 9 6 0 14 25 19
2004 10 32 3 23 38 1 14 8 29 26 7 5 19 2 7 10 6 0 14 26 19
2005 15 32 3 23 50 4 13 10 31 30 7 5 21 2 7 10 8 5 14 25 20
2006 15 13 2 22 50 4 14 11 29 34 5 5 25 2 7 10 10 6 14 26 20
2007 19 33 2 24 53 5 1413 10 31 37 5 6 26 2 7 11 13 7 15 28 20
2008 20 33 2 25 53 7 13 9 30 42 4 6 28 3 8 11 14 11 16 29 21
2009 16 33 2 24 58 5 13 9 37 42 6 6 27 4 8 12 14 10 16 31 21
2010 18 37 2 25 60 7 13 9 35 43 8 6 26 4 9 12 14 11 15 31 20
2011 16 38 2 25 64 9 14 10 34 46 7 6 27 4 10 12 15 10 15 30 22
2012 18 38 2 25 72 11 14 11 34 45 5 6 28 4 10 12 14 10 14 31 25
2013 18 45 2 25 85 10 14 11 32 47 5 8 28 4 10 12 12 11 11 32 25
2014 17 43 2 24 86 9 14 11 29 47 3 8 28 4 10 12 9 8 11 34 25
Totals 238 607 46 450 950 85 246 173 633 628 96 106 401 43 144 195 177 95 274 439 378
5
5
5
6
6
7
7
7
7
7
8
8
7
6
8
7
1
116
249
275
288
293
295
302
306
342
349
375
393
402
412
422
437
454
435
6520
M an
1998 6 20 3 24 27 2 11 8 39 23 3 5 15 1 6 9 5 1 14 4 18
ce pt
1997 5 19 3 24 35 2 11 8 39 23 4 5 14 1 6 9 5 1 13 3 16
Ac
Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Malaysia Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
Year 1996 6 18 1 24 30 1 11 8 39 23 4 5 15 0 6 9 3 1 13 3 16
ed
Country
us
Panel A: Number of banks in sample by country for the years 1996 - 2014
cr
Table 1—Description of Data Sample
43
Page 41 of 54
44 Table 1—(continued) Description of Data Sample
Totals
cr us
an
132 9 18 63 88
45.0 17.8 59.0 11.1 5.9 22.4 19.5 45.7 1.6 27.4 14.6 3.8 18.2 41.9 32.6 22.6
M
45 86 256 376 290
te
107 108 27 50 56 19 48 79 10 172 14 4 73 18 47 44
% Islamic
Ac ce p
Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Malaysia Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
Total Observations Islamic
Commercial 131 499 19 400 894 66 198 94 623 456 82 102 328 25 97 151
d
Country
ip t
Panel B: Number of banks in sample by type (commercial or Islamic) for the years 1996 - 2014
74.6 9.5 6.6 14.4 23.3
78
38
32.8
5296
1224
18.8
44
Page 42 of 54
45
Table 2--Definitions of variables and ratios
Ac ce p
te
d
M
an
us
cr
ip t
Profitability and performance ratios 1. ROA = return on assets = net income / total assets 2. ROE = return on equity = net income / stockholders’ equity 3. ROD = return on deposits = net income / total deposits 4. NIM = net interest margin = (total interest income – total interest cost) / total assets 5. NNIM = net non-interest margin = (total noninterest income –total noninterest expense) / total assets Asset composition variables 6. SIZE = natural log of total assets 7. LSPEC = loan specialization ratio = total net loans / total assets 8. SECUR = security specialization ratio = other interest bearing assets (non-loans)/ total assets 9. DEPA = deposit specialization ratio = total deposits/ total assets 10. LOANDEP = total net loans to total deposits 11. CTA = cash to assets ratio = cash and cash equivalents / total assets 12. FATA = fixed asset ratio = fixed assets / total assets 13. NIBA = ratio of non-interest bearing assets to total assets Efficiency ratios 14. INEFF = inefficiency ratio = operating expenses/ gross income 15. LCI = labor cost to income = personnel expenses/gross income 16. OOEI = other operating expenses / gross income 17. IEI = interest expense to income = income expenses/ gross income Risk Measures 18. CRISK = credit risk = loan loss provisions/ net loans 19. IMPAIR = impairment ratio = impaired loans / net loans 20. CAPSTR = capital strength = total equity/ total assets 21. Z-score =stability measure = Sum of ROA and CAPSTR / standard deviation of each bank’s ROA Dummy Variables 22. ISLAMIC = dummy variable equal to one if the bank is Islamic, zero for commercial banks 23. UNLISTED = dummy variable equal to one if bank shares are not traded on a stock exchange, nor delisted 24. Country dummies for 21 of 22 countries. Malaysia is the intercept term because it has the most Islamic banks.
Bankscope definitions and conventions used to obtain various Islamic equivalent variables are as follows: Net income = commercial net income before taxes, plus Zakat. Loans = asset value of Mutajata+Murabaha+Istisna+Ijarah+Salam+Musharka+Mudarabah+Wakalat. Customer Deposits = Unrestricted Mudarabah deposits + Murabaha deposits + Medium Term Wakala Financing + savings accounts + current accounts. Interest income = income from Murabaha + Musharka + Istisna + Ijarah + Salam + Mudarabah + Wakalat. Interest expense = distributions (expense) on customer deposits. Non-net interest income = income from commission and fees + portfolio management and Sukuk management fees received. Non-interest expense = portfolio management fees paid + professional and licenses fees paid. Loan loss provisions = net provisions for Islamic financing and investing activities (Murabaha, Musharka, Istisna, Ijarah, Salam, Mudarabah, and Wakalat).
45
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46 Impairment = impaired Murabaha, Musharka, Mudarabah, Istisna, Ijarah, Salam
ip t
Table 3--Means of Variables for Commercial Banks and the differential value for Islamic banks before, during and after the financial crisis of 2008-2009
Variable
cr
Islamic Bank differentials Commercial Banks Before During After Before During After 1996-2007 2008-2009 2010-2014 1996-2007 2008-2009 2010-2014 1.30 12.47 2.30 3.15 -1.41
1.25 11.62 2.22 3.21 -1.59
1.24 10.53 2.28 3.15 -1.56
0.35*** -0.83 1.55*** -0.04 0.21**
-0.08 -3.29*** 1.14** 0.03 -0.22
SIZE LSPEC SECUR CTA FATA NIBA DEPA LOANDEP
13.88 46.67 39.47 8.86 1.77 2.39 69.40 77.27
14.63 50.25 33.68 10.68 1.75 2.43 69.67 80.21
14.84 51.18 33.26 10.67 1.60 2.41 69.34 86.56
-0.52*** 4.45*** -7.07*** 0.11 0.47*** 0.37*** -5.86*** 28.63***
-0.38*** 0.60 -6.92*** 4.05*** 0.56** 0.33** -9.14*** 42.48***
-0.31*** 1.17 -6.33*** 3.43*** 0.54*** 0.23*** -6.61*** 29.38***
INEFF LCI OOEI IEI
81.13 14.64 15.31 53.03
80.49 16.58 16.22 48.26
77.77 18.37 16.92 42.64
-2.28* 2.85*** 5.38*** -10.91***
5.60* 2.17** 8.53*** -4.64**
4.63*** 1.77*** 6.86*** -4.73***
CRISK IMPAIR CAPSTR Z-Score
2.01 9.22 11.25 18.47
1.79 7.21 12.58 19.46
1.47 6.50 13.36 21.93
-0.48*** -3.37*** 5.37*** 0.88
0.46 -3.12*** 3.81*** -1.04
0.18 -1.22** 2.49*** -1.60*
an
M
d
te
Ac ce p
-0.14 -1.57** 0.49 -0.26** 0.03
us
ROA ROE ROD NIM NNIM
All ratios are expressed in percent and mean values are obtained from a regression of each accounting ratio on six dummy variables—one for each of the three time periods across both commercial and Islamic banks. The t-statistics used to judge significance levels are adjusted for heteroskedasticity. Due to missing data only 5977 observations are available for LCI and 5405 for CRISK. Means for all other variables are based on 6520 observations. ***, **, * indicates a ratio for Islamic banks that is significantly different from the same ratio for commercial banks at the 1%, 5%, and 10% levels. Black boldfacing indicates a mean value for an Islamic bank that is statistically larger than the same coefficient for a commercial bank, while red boldfacing shows a coefficient that is statistically smaller. (The results are qualitatively similar using the t-test of means based on unequal variances between bank samples.)
46
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47
Table 4—Determinants of ROA and ROE
(-3.76)*** (0.47) (1.97)* (-0.70) (0.88) (2.23)** (1.11) (0.74) (1.08) (1.32) (-0.23) (0.06) (1.30) (0.03) (0.77) (-1.57) (3.21)*** (2.83)*** (0.69) (1.56) (1.94)* (1.37)
te
Ac ce p
t-statistic (12.49)*** (-0.99) (12.09)*** (-0.57) (11.48) (1.26)
us
70.65% 169.52***
Coefficient 34.12 -0.74 33.22 -0.51 31.41 0.91
an
Adjusted R2 Hausman test
M
-0.159 0.146 0.491 -0.300 0.233 0.523 0.402 0.218 0.330 0.334 -0.105 0.024 0.368 0.012 0.290 -0.529 1.026 0.964 0.214 0.436 0.566 0.575
ROE t-statistic (12.63)*** (1.28) (10.62)*** (1.20) (10.92)*** ( 1.29) (2.12)** (-2.00)** (-87.95)*** (21.88)*** (3.29)***
d
Coefficient 3.644 0.449 3.478 0.448 3.292 0.488 0.029 -0.052 -0.043 0.036 0.003
cr
ROA Independent Variable Constant (PRE-Malaysia) PRE-Islamic (1996-2007) Crisis (2008-2009) Crisis-Islamic After (2010-2014) After-Islamic SIZE SIZE-Islamic INEFF CAPSTR LSPEC SECUR DEPA UNLISTED Bahrain Bangladesh Brunei Egypt Indonesia Iraq Jordan Kuwait Lebanon Mauritania Oman Pakistan Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen
ip t
Random Effects panel estimation of the determinants of ROA and ROE. The final model is selected from an exhaustive search of the independent (non-profitability) variables in Table 2. A 5% cutoff criterion was used to maximize the log of the likelihood function. The t-statistics are shown in parentheses. Coefficients on continuous independent variables are qualitatively similar for models without dummies included.
-0.290 -0.222
0.021 0.030 -1.093 -3.034 6.305 -12.439 -2.879 5.174 -6.754 -4.946 -3.050 2.847 -1.741 7.531 1.904 -2.267 -1.392 -6.825 5.713 4.939 1.509 3.551 0.074 0.156
(-70.95)*** (-11.02)*** (3.13)*** (3.68)*** (-3.02)*** (-0.75) (1.84)* (-2.40)** (-0.82) (1.58) (-1.63) (-1.29) (-0.79) (0.80) (-0.40) (1.43) (0.51) (-0.51) (-031) (-1.61) (1.41) (1.18) (0.37) (0.95) (0.02) (0.03) 54.96% 104.46***
47
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48 ***, **, and * indicate significance at the 1%, 5% and 10%levels, respectively.
Table 5—Beta Convergence of Financial Ratios
NIM NNIM SIZE LSPEC SECUR DEPA
CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
Ac ce p
LOANDEP
us
-0.720 (-25.66) -0.652 (-27.87) -0.542 (-19.77) -0.492 (-20.98) -0.735 (-27.75) -0.189 (-12.84) -0.454 (-25.44) -0.400 (-21.26) -0.374 (-19.91) -0.400 (-21.96) -0.620 (-28.45) -0.385 (-19.51) -0.347 (-17.35) -0.679 (-30.93) -0.879 (-40.07) -0.549 (-27.37) -0.493 (-25.53) -0.811 (-24.48) -0.546 (-21.67)
an
ROD
0.110 (4.43) 0.028 (0.96) 0.088 (3.87) 0.071 (3.24) 0.044 (1.68) -0.007 (-1.35) -0.009 (-0.96) -0.034 (-1.64) 0.009 (1.21) -0.030 (-3.27) 0.033 (-1.70) 0.063 (3.20) -.033 (-1.57) 0.013 (2.67) 0.045 (2.45) 0.022 (1.87) -0.013 (-1.24) 0.054 (1.42) 0.059 (2.27)
M
ROE
-0.639 (-44.31) -0.640 (-47.34) -0.540 (-38.02) -0.518 (-40.07) -0.613 (-44.37) -0.273 (-30.66) -0.416 (-38.14) -0.433 (-36.91) -0.432 (-39.03) -0.425 (-38.43) -0.590 (-53.79) -0.439 (-36.36) -0.349 (-29.17) -0.554 (-44.69) -0.742 (-65.42) -0.476 (-41.01) -0.440 (-39.31) -0.722 (-43.46) -0.521 (-38.98)
Post-Crisis (2010-2014) β Commercial β Islamic
d
ROA
Entire period (1996-2014) β Commercial β Islamic
te
Variable
cr
ip t
Beta coefficients are from fixed effects panel regressions for the whole period and the post-crisis period. T-statistics are in parentheses. Black boldfacing for the β Commercial coefficients indicates convergence in levels for commercial banks that is significant at the 5% level. The β Islamic coefficients show the differential speed of adjustment for Islamic banks relative to commercial banks. Black (red) boldfacing means significantly faster (slower) at the 5% significance level for Islamic banks relative to commercial banks. Random effect models with listing and country dummies are similar, but the Hausman test rejects the random effects model at the 1% level for all independent variables.
0.088 (2.04) 0.086 (1.80) 0.018 (0.45) 0.009 (0.26) -0.006 (-0.13) -0.011 (-1.76) 0.024 (1.72) -0.119 (-3.59) 0.005 (0.42) -0.005 (-0.38) 0.173 (4.60) -0.009 (-0.32) 0.174 (5.94) 0.001 (0.06) 0.216 (6.49) 0.019 (1.08) -0.035 (-1.83) 0.159 (2.42) 0.181 (4.12)
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49 CAPSTR
-0.402 (-37.31) -0.343 (-31.36)
Z-score
0.023 (1.94) 0.032 (1.77)
-0.473 (-23.84) -0.285 (-13.71)
0.039 (2.25) -0.078 (-2.40)
ip t
Table 6-- Sigma Convergence of Financial Ratios
NIM NNIM SIZE LSPEC SECUR
LOANDEP CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
us
-0.014 (-0.56) -0.063 (-2.88) -0.123 (-5.77) 0.011 (0.53) 0.062 (2.69) 0.085 (4.17) 0.100 (3.75) 0.019 (0.93) 0.019 (2.42) 0.062 (2.37) 0.063 (1.70) -0.140 (-4.90)
Ac ce p
DEPA
-0.419 (-34.35) -0.387 (-30.79) -0.406 (-28.12) -0.595 (-52.75) -0.486 (-36.47) -0.414 (-32.82) -0.708 (-47.48) -0.752 (-63.99) -0.610 (-40.71) -0.554 (-41.97) -0.731 (-43.95) -0.416 (-35.94)
-0.726 (-25.39) -0.647 (-23.72) -0.545 (-19.29) -0.491 (-19.09) -0.742 (-27.60) -0.191 (-11.37) -0.367 (-22.03)
an
ROD
0.139 (5.32) 0.052 (1.70) 0.100 (4.20) 0.108 (4.38) 0.045 (1.65) 0.029 (1.35) -0.029 (-1.29)
M
ROE
-0.653 (-44.71) -0.650 (-47.43) -0.544 (-38.06) -0.518 (-38.95) -0.640 (-44.22) -0.271 (-28.69) -0.370 (-30.78)
Post-Crisis (2010-2014) σ Commercial σ Islamic
d
ROA
Entire period (1996-2014) σ Commercial σ Islamic
te
Variable
cr
Sigma coefficients are from fixed effects panel regressions. T-statistics are in parentheses. Black boldfacing for the β Commercial coefficients indicates convergence in levels for commercial banks that is significant at the 5% level. The β Islamic coefficients show the differential speed of adjustment for Islamic banks relative to commercial banks. Black (red) boldfacing means significantly faster (slower) at the 5% significance level for Islamic banks relative to commercial banks. Random effect models with listing and country dummies are similar, but the Hausman test rejects the random effects model at the 1% level for all independent variables.
-0.380 (-18.95) -0.321 (14.49) -0.207 (-8.64) -0.639 (-28.25) -0.427 (-18.98) -0.443 (-20.96) -0.821 (-31.53) -0.25 (-3.82) -0.665 (-24.97) -0.641 (-27.67) -0.811 (-24.42) -0.301 (-14.79)
0.109 (2.32) 0.108 (2.11) 0.026 (1.00) 0.009 (0.21) 0.042 (0.87) 0.108 (3.37) -0.035 (-1.06)
-0.081 (-2.04) -0.104 (-3.12) -0.198 (-6.06) 0.315 (6.06) 0.047 (1.29) 0.174 (5.72) 0.136 (3.03) 0.022 (2.089) 0.133 (3.37) 0.040 (0.93) 0.144 (2.22) -0.294 (-6.04)
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50 CAPSTR
-0.513 (-37.01) -0.345 (-29.82)
Z-score
0.109 (4.80) -.007 (-0.32)
-0.549 (-23.80) -0.287 (-14.50)
0.171 (4.46) -0.109 (-2.99)
ip t
Table 7—Gamma (Log t) Convergence of Financial ratios
NIM NNIM SIZE LSPEC SECUR DEPA
CTA NIBA FATA INEFF LCI OOEI IEI CRISK IMPAIR
Ac ce p
LOANDEP
1.174 (2.98) 1.657 (5.98) -0.038 (-0.25) -0.706 -2.09) 0.153 (0.41) -0.134 (-0.95) 1.176 (2.20) -0.226 (-1.05) -0.032 (-0.38) 0.796 (3.24) 0.826 (0.95) 0.468 (2.76) -2.120 (-14.13) 0.400 (0.95) 0.561 (2.89) 0.278 (1.28) 0.432 (2.55) -1.100 (-3.35) -1.964 (-15.28)
2010 - 2014 γ Islamic γ Commercial
us
0.426 (0.93) 0.348 (0.74) -0.192 (-0.41) 0.650 (4.18) 0.225 (1.31) 0.182 (3.75) 0.037 (0.39) -0.415 (-5.14) -0.585 (-7.28) 0.132 (2.42) 0.730 (4.12) 0.142 (0.81) -0.242 (-2.36) -0.197 (-1.26) 0.688 (4.58) 0.305 (2.77) -0.888 (-3.81) -0.235 (-2.38) -1.194 (-12.93)
γ All
1.081 (2.06) 4.849 (3.25) 0.600 (2.70) -1.019 (-1.53) 0.141 (0.73) -0.723 (-2.29) 1.320 (3.09) -0.267 (-0.61) 0.537 (0.99) 1.479 (9.22) 3.088 (49.49) -0.534 (-2.28) -2.469 (-5.40) -0.274 (-0.51) 0.116 (1.80) -0.087 (-0.76) 0.128 (0.76) -0.484 (-0.58) -1.354 (-6.75)
an
ROD
-0.301 (-0.73) -0.445 (-1.13) -0.086 (-0.81) 0.327 (2.51) 0.266 (0.92) 0.051 (0.48) 0.234 (1.34) -0.207 (-1.58) -1.126 (-4.06) 0.011 (0.07) 1.078 (4.09) 0.044 (0.54) -0.540 (-2.30) -0.955 (-3.96) -1.167 (-4.46) -0.104 (-0.78) -0.733 (-4.13) -0.614 (-1.31) -0.708) (-4.80)
γ Commercial
M
ROE
0.175 (0.29) 0.190 (0.44) -0.181 (-0.62) 0.523 (4.07) 0.235 (1.22) 0.172 (2.99) 0.061 (0.60) -0.404 (-7.31) -0.788 (-8.56) 0.070 (1.45) 0.773 (4.41) 0.094 (0.63) -0.321 (-2.57) -0.426 (-2.82) 0.118 (1.04) 0.151 (1.34) -0.875 (-4.52) -0.296 (-2.58) -1.109 (-14.16)
1996 – 2014 γ Islamic
d
ROA
γ All
te
Variable
cr
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
1.359 (3.86) 0.734 (1.62) -0.371 (-1.16) -0.535 (-1.80) 0.143 (0.33) 0.011 (0.09) 1.146 (2.00) .0.119 (-0.87) -0.269 (-0.81) 0.551 (1.38) 0.084 (0.07) 0.879 (6.13) -1.888 (-10.80) 0.854 (2.22) 1.196 (4.66) 0.544 (2.07) 0.743 (3.81) -1.156 (-2.43) -2.137 (-14.49)
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51 CAPSTR
0.625 (12.71) 0.061 (0.49)
Z-score
0.675 (10.33) 0.589 (4.50)
-0.057 (-0.17) -1.034 (-10.58)
0.612 (11.56) -0.060 (-0.46)
0.158 (0.36) -1.215 (-6.64)
-0.448 (-3.69) -0.509 (-1.73)
ip t
Table 8—Gamma (Log t) Club convergence results for profitability ratios by region
NIM NNIM
Southeast Asia region γ All Variable ROA ROE
NNIM
Africa region Variable ROA ROE NIM NNIM
γ All
-0.435 (-1.18) -0.234 (-1.06) -0.621 (-3.32) -0.018 (-0.17)
-0.766 (-1.99) -0.160 (-0.32) -.001 (-.00) 0.505 (2.14)
0.041 (0.10) -0.230 (-0.47) 0.446 (5.69) 0.478 (3.05)
1996 – 2014 γ Islamic γ Commercial
-0.544 (-1.50) -0.797 (-0.82) -0.280 (-0.48) 0.003 (0.01)
us
3.001 (2.15) 6.329 (3.57) 0.352 (1.27) 0.263 (0.33)
1996 – 2014 γ Islamic γ Commercial
Ac ce p
NIM
-0.078 (-0.21) -0.190 (-0.48) 0.253 (2.22) 0.469 (2.84)
-1.801 (-3.51) -0.859 (-1.75) -0.680 (-6.35) 0.881 (0.96)
2010 - 2014 γ Islamic 2.680 (1.65) 29.66 (3.84) 1.091 (2.06) 0.273 (0.34)
an
ROE
-1.51 (-3.38) -4.581 (-1.90) 1.410 (3.75) 1.844 (1.56)
γ All
M
-1.756 (-3.97) -1.994 (-3.38) 0.347 (1.51) 1.186 (1.14)
1996 – 2014 γ Islamic γ Commercial
d
ROA
γ All
te
GCC region Variable
cr
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
-0.225 (-0.65 -0.158 (-0.61) -0.453 (-4.45) -0.069 (-.72)
γ All -0.786 (-1.77) -0.887 (-1.67) -0.644 (-1.71) 1.243 (2.78)
2010 - 2014 γ Islamic -1.391 (-5.49) -1.456 (-0.92) -1.740 (-1.88) 0.696 (2.02)
γ All
2010 - 2014 γ Islamic
3.439 (83.92) 2.785 (2.90) -1.578 (-16.18) -1.189 (-2.09)
4.034 (18.45) 6.940 (8.65) -1.173 (-11.56) -1.149 (-1.71)
γ Commercial 4.108 (2.90) 2.189 (0.83) -0.175 (-0.39) 0.200 (0.23)
γ Commercial -0.576 (-1.12) -0.770 (-1.16) -0.190 (-0.64) 1.375 (2.53)
γ Commercial 3.096 (25.29) 1.882 (1.38) -1.951 (-57.62) -1.184 (-2.16)
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52
Table 9—Gamma (Log t) Club convergence results for profitability ratios—Clustering by high, middle and low historical return on asset groups of countries
NIM NNIM
ROA ROE NIM NNIM
γ All -0.435 (-1.18) -0.233 (-1.06) -0.621 (-3.32) -0.008 (-0.17)
-0.544 (-1.50) -0.797 (-0.81) -0.281 (-0.48) 0.003 (0.01)
2010 - 2014 γ Islamic
us
3.145 (5.23) 4.021 (12.83) -0.646 (-3.31) -8.864 (-3.21)
1996 – 2014 γ Islamic γ Commercial
Ac ce p
Low ROA cluster Variable
-0.532 (-1.11) 0.053 (0.09) -1.435 (-6.71) -1.114 (-1.21)
-1.137 (-3.51) -0.618 (-1.51) 0.180 (1.38) -1.023 (-0.98)
γ All
3.561 (5.91) 5.276 (13.29) 0.515 (1.16) -8.756 (-3.35)
an
ROE
γ Commercial
M
-0.837 (-2.06) -0.344 (-0.69) -0.740 (-15.93 -1.076 (-1.12)
1996 – 2014 γ Islamic
d
ROA
γ All
-0.225 (-0.65) -0.128 (-0.60) -0.453 (-4.45) 0.069 (0.72)
te
High ROA cluster Variable
cr
ip t
Obtained from six separate OLS log-t regressions for convergence of gamma toward the cross sectional mean for all banks, Islamic banks and commercial banks over two timeframes. T-statistics are in parentheses, but regression constants are not presented. Coefficients of γ ≥ 2 imply convergence in levels, 2 > γ ≥ 0 implies convergence in rates and γ < 0 suggests possible divergence. The null hypothesis of convergence is rejected at the 5% significance level when the t-statistic is t < -1.65 and shown by red boldfacing. Black boldfacing implies significant values for acceptance of convergence.
γ All 3.439 (83.92) 2.785 (2.90) -1.578 (-16.18) -1.189 (-2.09)
γ Commercial 2.792 (3.64) 2.941 (4.07) -1.439 (-6.41) -8.952 (-3.12)
2010 - 2014 γ Islamic γ Commercial 4.074 (18.45) 6.940 (8.65) -1.173 (-11.56) -1.145 (-1.71)
3.096 (25.29) 1.881 (1.38) -1.951 (57.62 -1.184 (-2.16)
52
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53
Figure 1
2.8
16
ROE
ROA 2.4
ip t
Panel A: Profitability Ratios
cr
14
2.0 12
us
1.6 10 1.2 8
0.8
6 1998
2000
2002
2004 ROAI
2006
2008
2010
2012
2014
1996
1998
ROAC
2000
2002
2004
ROEI
7
4.0 ROD
2006
2008
2010
2012
2014
ROEC
M
1996
an
0.4
NIM
6
3.6
5
d
3.2
4
2.8
te
3
2.4
1 1996
1998
Ac ce p
2
2000
2002
2004
RODI
-0.6
2006
2008
2010
2012
2014
2.0
1996
1998
RODC
2000
2002
2004 NIMI
2006
2008
2010
2012
2014
NIMC
NNIM
-0.8 -1.0 -1.2 -1.4 -1.6 -1.8 -2.0 -2.2 1996
1998
2000
2002
2004 NNIMI
2006
2008
2010
2012
2014
NNIMC
53
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54
Figure 1 (continued)
ip t
Panel B: Asset Composition Ratios 70 LSPEC
15.2 SIZE
65 14.8
60
cr
14.4
55
14.0
45
13.2
40
12.8 1998
2000
2002
2004 SIZEI
2006
2008
2010
2012
2014
1996
1998
2000
2002
2004
48
72
DEPA
Securitization Ratio 70
44
68 40
66 64 62
32
60 28
58 56 1998
2000
2002
2004
SECRATI
2006
2008
2010
2012
2014
1996
SECRATC
130
16
2000
te
LOANDEP
1998
120
2002
d
24
2008
LSPECC
2010
2012
2014
M
36
1996
2006
LSPECI
SIZEC
an
1996
us
50
13.6
2004 DEPAI
2006
2008
2010
2012
2014
DEPAC
CTA
14
110
12
100
Ac ce p
10
90
8
80
6
70 60
4
1996
1998
2000
2002
2004
LOANDEPI
3.6
2006
2008
2010
2012
2014
1996
1998
2000
2002
2004
LOANDEPC
CTAI
2006
2008
2010
2012
2014
2010
2012
2014
CTAC
2.8
FATA
NIBA
3.4
2.6
3.2
2.4
3.0
2.2
2.8
2.0
2.6
1.8
2.4
1.6
2.2
1.4
2.0
1.2 1996
1998
2000
2002
2004 NIBAI
2006
2008
NIBAC
2010
2012
2014
1996
1998
2000
2002
2004 FATAI
2006
2008
FATAC
54
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55
Figure 1 (continued)
24
Inefficiency Ratio
22
84
20
80
18
76
16
72
14
68
cr
LCI
88
LCII
us
92
ip t
Panel C: Efficiency Ratios
2004
2006
12 1998
2000
2002
2004 INEFFI
2006
2008
2010
2012
2014
1996
1998
2000
INEFFC
28
2002
2004
2006
2008
2010
2012
2014
LCIC
an
1996
65 OOEI
IEI
26
60
24
20
M
55
22
50
18
45
16
12
d
40
14
35
2000
2002
2004 OOEII
2006
2008
OOEIC
2010
2012
2014
1996
1998
2000
te
1998
2002
IEII
2008
2010
2012
2014
IEIC
Ac ce p
1996
55
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56
Panel D: Risk Measures 4.0
ip t
CRISK 3.5
3.0
cr
2.5
2.0
us
1.5
1.0 1996
1998
2000
2002
2004 CRISKI
2006
2008
2010
2012
2014
CRISKC
11
22 Impairment Ratio
CAPSTR
10 9
an
20 18
8 16 7 14
M
6 12
5
10
4 3
8 1998
2000
2002
2004 IMPAIRI
2006
2008
2010
2012
2014
1996
1998
2000
d
1996
IMPAIRC
24
2004
CAPSTRI
2006
2008
2010
2012
2014
CAPSTRC
te
Z-Score
2002
22
18
16
14 1996
1998
Ac ce p
20
2000
2002
2004
ZEDI
2006
2008
2010
2012
2014
ZEDC
56
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