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Determinants of Islamic Banking Profitability
M. Kabir Hassan, Ph.D.
Professor of Finance
Department of Economics and Finance
University of New Orleans
New Orleans, LA 70148
Phone: 504-280-6163
Fax: 504-280-6397
Email: mhassan@uno.edu
Abdel-Hameed M. Bashir, Ph.D.
Senior Economist
Economic Policy and Strategic Planning Division
Islamic Development Bank
P.O. Box 9201, Jeddah 21413, Saudi Arabia
Phone: 966-2-646-7468
Email: ambashir@isdb.org
ERF Paper
2
Determinants of Islamic Banking Profitability
Abstract
The paper analyzes how bank characteristics and the overall financial environment affect the
performance of Islamic banks. Utilizing bank level data, the study examines the performance
indicators of Islamic banks’ worldwide during 1994-2001. A variety of internal and external
banking characteristics were used to predict profitability and efficiency. In general, our analysis
of determinants of Islamic bank profitability confirms previous findings. Controlling for
macroeconomic environment, financial market structure, and taxation, the results indicate that
high capital and loan-to-asset ratios lead to higher profitability. Everything remaining equal, the
regression results show that implicit and explicit taxes affect the bank performance measures
negatively while favorable macroeconomic conditions impact performance measures positively.
Surprisingly, the results indicate a strong positive correlation between profitability and overhead.
3
Determinants of Islamic Banking Profitability
1. Introduction
The steady expansion of Islamic banks has been the hallmark of the Muslim world
financial landscape in the 1980s and 1990s. With a network that spans more than 60 countries
and an asset base of more than $166 billion, Islamic banks are now playing an increasingly
significant role in their respective economies. Based on their charters, Islamic banks have the
flexibility of becoming shareholders and creditors of firms, as well as the advantage of providing
investment-banking services. In this respect, Islamic banks are rapidly gaining market shares in
their domestic economies1. In retrospect, the presence of Islamic banks exemplifies the empirical
success and the viability of eliminating fixed interest payments from financial transactions.
Indeed, consolidation among banks, rising competition and continuous innovation to
provide financial services, all contribute to a growing interest in a detailed critical evaluation of
Islamic banks. In fact, evaluating the performance of Islamic banks is essential for managerial as
well as regulatory purposes. While managers are keen to determine the outcomes of previous
management decisions, bank regulators concerned about the safety and soundness of the banking
system and with preserving public confidence, monitor banks’ performance to identify banks that
are experiencing severe problems. Without persistent monitoring of performance, existing
problems can remain unnoticed and could lead to financial failure in the future. Depositors may
also be interested in characterizing the performance of their bank(s) since they are not entitled to
fixed returns and the nominal values of their deposits are not guaranteed. Most importantly,
performance evaluation is needed to provide answers to key policy questions such as: should
Islamic banks be held to the same set of regulations as conventional banks? Are they relics of a
bygone era, propped up by subsidies and distorting financial-sector competition? Or, are they
efficient and focused financial institutions that could, if unleashed, eventually dominate the retail
financial landscape?
Previous attempts to study Islamic Banks (Ahmed 1981, Karsen 1982) focused primarily
on the conceptual issues underlying interest-free financing. The issues of viability of Islamic
banks and their ability to mobilize saving, pool risks and facilitate transactions did not get enough
coverage in the existing literature. Fewer studies, however, have focused on the policy
implications of eliminating interest payments [Khan (1986), Khan and Mirakhor (1987), and
Bashir (1996)]. In fact, the lack of complete data impeded any comprehensive analysis of the
1 Their market share has grown from around two percent in the 1970s to around fifteen percent in the
1990s, see Aggarwal and Yousef (2000).
4
experience of the last three decades. For example, the empirical work done so far has yielded
inconclusive results [see, Bashir, Darrat and Suliman (1993), Bashir (1999), Zaher and Hassan
(2001) and Hassan (1999)]. Meanwhile, the recent trends of financial liberalization and
deregulation have created new challenges and new realities for Islamic banks. The integration of
global financial markets has put Islamic banks in a fierce competition with traditional banks. To
compete in local and global deposit markets, Islamic banks have to design and innovate
Islamically acceptable instruments that can cope with the continuous innovations in financial
markets. In addition, Islamic banks should find investment opportunities (for fund mobilization
and utilization) that offer competitive rates of return at acceptable degrees of risk. Equally,
banks’ management must carefully consider interactions between different performance measures
in order to maximize the value of the bank.
This paper intends to characterize some financial and policy indicators that impact the
overall performance of Islamic banks. Specifically, the purpose of the study is to closely examine
the relationship between profitability and the banking characteristics, after controlling for
economic and financial structure indicators. The intention is to decide which, among the potential
determinants of performance, appears to be important. By studying the connection between
Islamic banks’ performance and the efficiency indicators, this paper contributes to the on going
discussion on the effects of deregulation and liberalization on the performance of the banking
sector. In the meantime, the paper also attempts to add to the existing literature in several ways.
First, utilizing bank level data, the paper provides summary statistics pertaining to Islamic banks’
sizes and profitability. Second, the paper uses regression analysis to determine the underlying
determinants of Islamic bank’s performance2. To this end, a comprehensive set of internal
characteristics is examined as determinants of banks’ net margins and profitability3. These
internal characteristics include bank size, leverage, loans, short term funding, and overhead.
Third, while studying the relationship between banks’ internal characteristics and performance,
the paper controls the impact of external factors, such as macroeconomic, regulatory and financial
market environment. Among the external factors controlled, reserve taxes, and the market
capitalization were not included in previous studies of Islamic banks. Moreover, some of the
determinants were also interacted with the country’s GDP per capita to check whether their
impacts on bank performance differ with levels of income. Finally, the results show that it is
2 Since both shareholders and depositors in Islamic banks are the residual claimants to the bank’s profits,
bank profitability is the designated measure of bank performance.
3 The literature divides bank profitability determinants to internal and external measures. Internal factors
are areas of bank management that the officers and staff of the bank have under their immediate control.
By contrast, external factors are environmental aspects of the bank’s market over which management has
no direct control (Bourke, 1989, Molyneux and Thronton, 1992, Fraser, Gup and Kolari, 2001).
5
possible to conduct a meaningful analysis in spite of the substantial differences in regulations and
financial development between the countries in the sample. The rest of the paper is organized in
four sections. Section 2 identifies the data sources, defines and highlights the variables
benchmarking Islamic banking performances. In section 3, we formulate the predicted model and
discuss the possible links between bank performance and the set of internal and external
indicators. Section 4 represents the empirical results while the conclusions are stated in section 5.
2. The Data and Variables
The data used in this study are cross-country bank-level data, compiled from income
statements and balance sheets of Islamic banks in 21 countries for each year in the 1994-2001
period. Table 1 gives the country-wise and year-wise breakdown of these Islamic banks. The
main data source is BankScope database compiled by IBCA. In so far as possible, the BankScope
database converts the data to common international standards to facilitate comparisons. Other
data sources include International Monetary Fund’s International Financial Statistics (IFS), World
Development Indicators (2001), and Global Development Finance (2001).
Let us begin our review with an initial assessment of the banking sector of the selected
Islamic countries by analyzing some accounting ratios as given in Table 2 without controlling for
the other variables that are also important. We will move into deeper analysis gradually.
In column 1 through 4 of Table 2, presents the averages of four macro-economic
variables, which are Gdp/Cap, Growth, Inflation and Real Interest. Per capita GDP measured in
1995 USD is highest in Qatar (19.907 USD in 1995 dollars) followed by UA Emirates 19,988)
and then by Brunei (17,657). Sudan has the lowest per capita GDP (284). Bangladesh, Gambia
and Yemen all have per capita GDP within the range of 300 to 350. Growth rates of GDP vary
within the sample countries from a highest of 5.77 percent per year in Sudan and a lowest of .98
percent in Indonesia. Inflation is highest in Sudan (49.44 percent per year) followed by Indonesia
(24.40) and Iran (23.15). It is lowest in Jordan at 1.82 followed by 1.98 in UA Emirates. Real
interest is highest in Gambia (20.80) and lowest in Algeria (-1.66). Therefore, the Islamic Banks
that we are about the study operate different countries around the world at different levels of
development. Economic structure, historical backgrounds, social norms and cultural values of
these countries are also diverse in many ways.
Column 5 to 7 of Table 2 shows reserve to deposit ratios, bank to GDP ratios, and tax
ratios. These ratios are indicators of financial market structure. Reserve to deposit ratio is highest
in Jordan 46.69 percent followed by Iran at 31.64 and then by Sudan at 26.03. The ratio is lowest
in Algeria (1.97). Bank to GDP ratios, which is the ratio of the deposit money bank divided by
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GDP, is highest in Lebanon (127.40) followed by Malaysia (117.47) and then by Kuwait (100).
Sudan has the lowest bank to GDP ratio of .01 and is preceded by Yemen (8.82) and then
Mauritania (11.35). Tax ratio is highest in Iran (75 percent). It is followed by Sudan and
Malaysia, both just over 68.00. It is lowest in Bahrain -7.87 percent.
In column 8 of Table 2 we present the Deposit Insurance dummy variable. It takes a
value of 1 if such an insurance scheme is present otherwise it takes 0. We can observe that only
Bahrain, Bangladesh and Lebanon have such schemes. In column 9, we present the concentration
ratios defined as the ratio of the three largest banks’ asset to total banking sector assets. We can
see that the concentration ratio is very high in Mauritania (97 percent) followed by Bahrain (84
percent) and then by Qatar (79). It is lowest in Bangladesh (9.13). For other countries included in
the sample this ratio ranges from 66 to 26 percent. This is high indeed. This indicated lack of
competition within the banking sector. The last column of Table 2, presents the credit variables
which are defined as the domestic credit of the private sector over total assets of the banking
system. Mauritania has the highest credit ratio of 88 percent followed by Saudi Arabia (83) and
then by Tunisia (73).
Financial institutions in general and banks in particular are exposed to a variety of risks,
whereby the extent of these risks depends on the portfolio characteristics of individual banks
(IMF, 2001). The variety of risks to which banks are exposed justifies looking at aspects of bank
operations that can be categorized under the CAMEL framework4. Recent studies have attempted
to deepen our understanding of the financial soundness indicators that are more relevant for the
analysis of financial stability. The recent studies have focused on the contemporaneous indicators
of financial health. No consensus has yet emerged, however, on a set of indicators that is more
relevant to assessing financial soundness or to building effective early warning systems.
Nonetheless, the literature provides some empirical justifications for the use of most of the
variables that have been identified as prudential indicators of financial soundness (IMF, 2000).
Table 3 presents comparative performance indicators of Islamic Banks and commercial
banks operating in the same market in countries where Islamic Banks operate side by side with
conventional banks. The importance of the indicators listed in Table 3 stems from the fact that
they help bank regulators assess bank performance. To facilitate comparison, the commercial
banks and Islamic Banks selected are similar in size, where size is measured in terms of total
assets. Specifically, we select all commercial banks that are in the third quartile, in terms of size,
in each country. Table 3 also summarizes the time averages of some important ratios. The value
4 The variety of risks to which banks are exposed justifies looking at certain aspects of bank operations.
Most bank supervisors have broadly adopted the U.S. CAMEL method of assessing bank performance:
capital adequacy, asset quality, management quality, earnings, and liquidity.
7
of each ratio represents the average over the period 1994-2001. All ratio definitions are given in
the appendix.
To analyze the performance measures presented in Table 3, we start with assets quality
ratios. Monitoring asset quality indicators is important since risks to the solvency of financial
institutions often derive from impairment of assets. The most useful asset quality indicator is the
financial leverage ratio (measured by the ratio of asset to capital). Poor asset quality is perceived
to cause capital erosion and increase credit and capital risks. Asset quality depends on the quality
of credit evaluation, monitoring and collection within each bank, and could be improved by
collateralizing the loans, having adequate provisions against potential losses, or avoiding asset
concentration on one geographical or economic sector5. Meanwhile, any analysis of asset quality
needs to take into account indicators of the likelihood of borrowers to repay their loans. It is
particularly important to monitor whether the increase in indebtedness in the economy is
concentrated in sectors that are vulnerable to shifts in economic activity. Loan concentration in a
specific economic sector or activity (measured as a share of total loans) makes banks vulnerable
to adverse developments in that sector or activity. Hence, the quality of financial institutions, loan
portfolios is closely related to the financial health and profitability of the institutions’ borrowers,
especially the nonfinancial enterprise sector (IMF, 2001). In this context, monitoring the level of
household and corporate indebtedness is useful.
In comparing the asset quality ratios for equal-sized commercial and Islamic Banks, we
observe a significant difference in the ratio of loan-loss reserve to gross loans. Commercial banks
in our sample tend to have more loan loss reserve – relative to the total loans – than Islamic
Banks. Since high-performing banks tend to restrain their credit risk, they tend to have lower loan
loss provision ratio. The comparison between the two groups of banks indicates that Islamic Bank
have a better quality of the loan portfolio. Another significant difference exists when comparing
the ratio of impaired loan over total loans. As in the previous case, Islamic banks have better
assets quality compared to commercial banks. Finally, a significant difference exists when
comparing the percentage of net charge-off (NCO) to gross loans. The net noncharge-off
indicates the percentage of loans written off the books. With a zero percent, Islamic Banks are
out-performing their peers in the sample. In summary, when compared to commercial banks with
similar size, Islamic banks seems to have better asset quality than their counterparts.
5 A large concentration of aggregate credit in a specific economic sector or activity, especially commercial property,
may signal an important vulnerability of the financial system to developments in the sector or activity. Many financial
crises in the past (including the Asian crises) have been caused or amplified by downturns in particular sectors of the
economy spilling over into the financial system via concentrated loan books of financial institutions (IMF, OP 192,
April 2000).
8
The second entry in Table 3 includes the capital adequacy ratios. Capital adequacy and
availability ratios indicate the robustness of financial institutions to shocks to their balance sheets.
Usually actual capital adequacy ratios are lagged indicators (historic) of the already existing
banking problems. Yet, an adverse trend in these ratios may signal increased risk exposure and
possible capital adequacy problems. According to the Basle Committee on Banking Supervision,
the most commonly used indicator in this group is the risk-based capital ratio (measured as the
ratio of capital to risk-adjusted assets). Simple leverage ratios (ratio of assets to capital) usually
complement this ratio6. In addition to capital adequacy, it is important also to monitor other
capital quality indicators, which may reflect the bank’s capability of absorbing losses.
When capital ratios are compared for the banks in our sample, several systematic
variations between Islamic Banks and commercial banks were observed. One noticeable
difference is the variation in capital-asset ratios. Although both type of banks (on average)
maintain the Basle Committee’s uniform standard of capital adequacy of 8 percent, Islamic Banks
tend to maintain much higher capital-asset ratios than their commercial peers. Except for one
ratio (subordinated debt over capital funds), Islamic Banks seem to be better capitalized than
commercial banks with similar size. The subordinated debt ratio indicates the percentage of total
capital provided in the form of subordinated debt. The lower this ratio is the better. In summary,
Islamic banks have better capital adequacy ratios than Commercial banks with similar size.
The third group of ratios presented in Table 3 is operation ratios. Generally, banks are
increasingly involved in diversified operations that involve some aspect of market risks. The
most important components of market risk, which significantly impact assets and liabilities of
financial institutions are interest and exchange rate risks7. Virtually, all financial institutions are
subject to interest rate risk and, therefore, it is considered as a market indicator.
Most of the operation ratios presented in Table 3 are significantly bigger for the
commercial banks compared to those of Islamic Banks in our sample. These include, net interest
income or revenue over total (average) assets, other operating income over total assets, non-
operating item and tax over total assets, non-operating items over net income and recurring
earning power. Usually, better performing banks have larger operations ratios. In our case,
commercial banks have significantly bigger operations ratios.
The last group of indicators in Table 3, are the liquidity ratios. Liquidity is generally not a
major problem for sound banks in a reasonably competitive banking system. However, liquidity
6 Financial institutions’ leverage increases when bank assets grow at a faster rate than capital, and is particularly useful as an indicator
for institutions that are primarily involved in lending.
7 Large open foreign exchange positions (including foreign exchange maturity mismatches) and a high reliance on foreign borrowing
(particularly short-term maturity) may signal a high vulnerability of financial institutions to exchange rate swings and capital flow
reversals (IMF, OP#192, April 2000).
9
can change rapidly, requiring frequent updating of relevant indicators. The recent banking crises
suggest that in many cases, liquidity crises have their roots in solvency problems. It is, therefore,
extremely important to monitor liquidity indicators because poor management of short-term
liquidity may force solvent banks toward closure8. An important indicator of liquidity is interbank
credit, whereby a high dispersion in interbank rate signals high risk. Banks may control their
interbank positions by using quantitative controls.
In comparing the liquidity ratios for our sample banks, the two ratios that are
significantly different between commercial banks and Islamic Banks are net loan over customer
and short term funding, and liquid assets over customer’s short term funding. These ratios tend to
be higher for high-performing banks. The liquidity ratios show that commercial banks are more
liquid than Islamic banks.
We find almost similar results when we compare the Islamic Bank performance ratios
with those of conventional commercial banks according to deposit base. These results are
reported in Table 4.
3. Determinants of Islamic Banks Profitability and Spread
In this section, we formulate the model used to examine the relationship between the
performance of Islamic banks and the set of internal and external banking characteristics. Since
the ultimate objective of management is to maximize the value of the shareholder’s equity, an
optimal mix of returns and risk exposure should be pursued in order to increase the profitability
of the bank. Hence, a comprehensive plan to identify objectives, goals, budgets, and strategies
should be developed. The planning should encompass both internal and external performance
dimensions. Because of increasing innovation and deregulation in the financial service industry,
internal and external competitiveness is becoming a critical factor in evaluating performance.
While internal performance is evaluated by analyzing financial ratios, external performance is
best measured by evaluating the bank’s market share, regulatory compliance, and public
confidence.
The operating efficiency and profitability measures used as criterion for performance are
specified below. Whereas capital, leverage, overhead, loan and liquidity ratios were used as
proxies for the bank’s internal measures, macroeconomic indicators, taxation, financial structure,
and country dummies were used to represent the external measures. A linear equation, relating
8 Acute liquidity problems could potentially lead to widespread solvency problems if banks are forced to liquidate their assets at a
significant loss. These effects would have grave consequences to borrowers, lenders and the economy at large.
10
the performance measures to a variety of financial indicators is specified9. The subsequent
regression analysis starts from estimating the following basic equation:
tjtjitiijt XBI
γ
β
α
α
+++= 0 tj
M+ijtjj C
ε
δ
+
(1)
where, Iijt = is the measure of performance (either non-interest margin or before tax profit margin)
for bank i in country j at time t; Bit are bank variables for bank i at time t; jt
Xare country
variables for country j at time t; tj
Mare the financial structure variables in country j at time t,
and j
Care country dummy variables10. 0
α
is a constant, and tji
γ
β
α
,, and j
δ
are coefficients,
while ijt
ε
is an error term. Although the primary focus of this paper is the relationship between
performance and bank internal variables, the inclusion of macroeconomic variables, financial
structure variables, and the country dummies is meant to control for cyclical factors that might
affect bank performance. Several specifications of equation (1) are estimated.
3.1. Measures of Performance
Evaluating bank performance is a complex process that involves assessing interaction
between the environment, internal operations and external activities. In general, a number of
financial ratios are usually used to assess the performance of financial intermediaries. The
primary method of evaluating internal performance is by analyzing accounting data. Financial
ratios usually provide a broader understanding of the bank’s financial condition since they are
constructed from accounting data contained on the bank’s balance sheet and financial statement.
Another key management element that many studies have found to be a primary factor in
assessing bank performance is operating efficiency. In measuring efficiency, both ex ante and ex
post spreads can be used to provide information on cost control. Generally speaking, ex ante
spreads are calculated from the contractual rates charged on loans and rates paid on deposits. In
contrast, however, the spread for the Islamic banks can be calculated from the rates of return
generated from various non-interest banking activities, including participation in direct
investment. As an efficiency indicator, we use the ex post spreads consisting of revenues
generated from Islamic banking operations such as mark-up (Murabaha), rent-to-own (Ijara),
9 No specification test is used here to support using the linear function. However, the linear functional form
is widely used in the literature and produces good results(see Short, 1979, and Bourke, 1989).
10 We run Hauseman specification tests for both fixed and random coefficient effect within pooled cross-
section time-series model. We report the correctly specified panel data model.
11
deferred sale (Bai Mu’jal), and service charges, minus the expenses of carrying such activities11.
Accounting values from the bank’s financial statement were used to compute the ex post spread
and profitability measures employed in this study.
Four measures of performance are used in this study: the net non-interest margin (NIM),
profit margin (BTP/TA), returns on assets (ROA), and returns on equity (ROE). The NIM is
defined as the net income accruing to the bank from non-interest activities (including fees, service
charges, foreign exchange, and direct investment) divided by total assets. Non-interest income is
growing in importance as a source of revenue for conventional banks in the 1990s. Some of the
fastest growing non-interest income items include ATM surcharges, credit-card fees, and fees
from the sale of mutual funds and annuities (see Kidwell, Peterson and Blackwell, 2000). For
Islamic banks, non-interest income, NIM, makes up the lion’s share of total operating income and
captures the bank’s ability to reduce the risk of insolvency. Moreover, since the returns on
Islamic banks’ deposits are contingent on the outcomes of the projects that banks finance, then
NIM reflects the management‘s ability to generate positive returns on deposits. If banks were
able to engage in successful non-loan activities and offer new services, non-interest income will
increase overtime (Madura, 2000). Goldberg and Rai (1996) used the net non-interest return as a
rough proxy of bank efficiency12.
The bank’s before-tax profit over total assets (BTP/TA) is used as a measure of the bank’s
profit margin. This measure is computed from the bank’s income statement as the sum of non-
interest income over total assets minus overhead over total assets minus loan loss provision over
total assets minus other operating income. BTP/TA reflects the banks’ ability to generate higher
profits by diversifying their portfolios. Since large size (scale) enables banks to offer a large
menu of financial services at lower costs, then positive relationships between BTP/TA and the
explanatory variables in equation (1) will give support to the efficient-structure hypothesis
(Smirlock, 1985).
Other alternative measures of overall performance are ROA and ROE. Both measures
are closely tied to the key item in the income statement; net income. ROA and ROE have been
used in most structure-performance studies and are included here to reflect the bank’s ability to
generate income from nontraditional services. ROA shows the profit earned per dollar of assets
and most importantly, reflects the management ability to utilize the bank’s financial and real
investment resources to generate profits. For any bank, ROA depends on the bank’s policy
11 The ex post spreads on conventional banks consist of the difference between banks’ interest revenues
and their actual interest expenses.
12 Since the operations of Islamic banks are generally risky, any change in the perceived risks faced by the
bank will necessarily be reflected on this margin.
12
decisions as well as uncontrollable factors relating to the economy and government regulations.
Many regulators believe return on assets is the best measure of bank efficiency. ROE, on the
other hand, reflects how effectively a bank management is using shareholders funds. A bank’s
ROE is affected by its ROA as well as by the bank’s degree of financial leverage (equity/ asset).
Since returns on assets tend to be lower for financial intermediaries, most banks utilize financial
leverage heavily to increase return on equity to a competitive level.
3.2. Bank Characteristics
To assess the relationship between performance and internal bank characteristics, our
analysis utilizes several bank ratios. These supplemental measures are particularly useful for
comprehensive understanding of the factors underlying a bank’s net margins and the quality of
bank management. The set of ratios used comprises fund source management (CSTFTA), funds
use management (OVRHD/TA and NIEATA), leverage and liquidity ratios (EQTA and
LOANTA). Each one of these determinants was also interacted with per capita GDP to capture
the effects of GDP on bank performance. The capital ratios have long been a valuable tool for
assessing safety and soundness of banks. Bank supervisors use capital ratios as rules of thumb to
gauge the adequacy of an institution’s level of capital. Since capital management is related to
dividend policy, banks generally prefer to hold the amount of capital that is just sufficient to
support bank operations. Starting 1988, the Basel Accord has imposed uniform capital ratio
standards on banks internationally.
Previous studies of the determinants of bank profitability in the United States found a
strong and statistically significant positive relationship between EQTA and profitability. This
supports the view that profitable banks remain well capitalized; or the view that well capitalized
banks enjoy access to cheaper (less risky) sources of funds with subsequent improvement in profit
rates (see Bourke, 1989). A positive relationship between the ratio of bank loans to total assets,
LOANTA, and profitability was also found from using international database (Demirguc-Kunt
and Huizinga, 1997). Bank loans are expected to be the main source of revenue, and are expected
to impact profits positively. However, since most of the Islamic banks’ loans are on the form of
profit and loss sharing (loans with equity features), the loan-performance relationship depends
significantly on the expected change of the economy. During a strong economy, only a small
percentage of the PLS loans will default, and the bank’s profit will rise. On the other hand, the
bank could be severely damaged during a weak economy, because several borrowers are likely to
default on their loans. Ideally, banks should capitalize on favorable economic conditions and
insulate themselves during adverse conditions.
13
Since the bulk of the earnings of Islamic banks come from non-interest activities, the
ratio of non-interest earning assets to total assets, NIEATA, is expected to impact profitability
positively. The ratio of consumer and short-term funding to total assets, CSTFTA, is a liquidity
ratio that comes from the liability side. It consists of current deposits, saving deposits and
investment deposits. Since liquidity holding represents an expense to the bank, the coefficient of
this variable is expected to be negative.
The ratio of overhead to total assets, OVRHD, is used to provide information on variation
in operation costs across the banking system. It reflects employment, total amount of wages and
salaries as well as the cost of running branch office facilities. A high OVRHD ratio is expected
to impact performance negatively because efficient banks are expected to operate at lower costs.
On the other hand, the usage of new electronic technology, like ATMs and other automated
means of delivering services, has caused the wage expenses to fall (as capital is substituted for
labor). Therefore, a lower OVRHD ratio may impact performance positively. Meanwhile, the
interaction variable OVRGDP captures the effects of both overhead and GDP on the performance
measures. The sign of the coefficient of this variable is not restricted.
3.3. The Control Variables
To isolate the effects of bank characteristics on performance, it is necessary to control for
other factors that have been proposed in the literature as possible determinants of profitability.
Four sets of variables are expected to be external to the bank: the macroeconomic environment,
the financial market structure, and the taxation indicator variables. The economic conditions and
the specific market environment would obviously affect the bank’s mixture of assets and
liabilities. We introduce these indicators in order to see how they interact with each other and
how they affect bank performance. Three indicators are used as proxies for macroeconomic
conditions: GDP per capita, GDPPC, the real interest rate (RI) and real interest rate*GDPPC. The
GDP per capita variable, GDPPC, is expected to have an effect on numerous factors related to the
supply and demand for loans and deposits. It is hypothesized in this paper that GDPPC affects
performance measures positively. Since most of the countries in the sample are characterized as
low or middle income, banks in these countries are expected to operate less competitively and
are, therefore, expected to generate higher profit margins.
Previous studies have also revealed a positive relationship between real interest rate (RI)
and bank profitability (Bourke, 1989). For conventional banks, high real interest rate generally
leads to higher loan rates, and hence higher revenues. However, in the case of Islamic banks, real
interest rate may impact performance positively if a larger portion of Islamic banks’ profits
14
accrues from direct investment, shareholding and/or other trading activities (Murabaha). Yet, real
interest rate may have a negative effect on bank profitability if higher real interest rates loweer
the demand for loan.
One of the most important industry characteristics that can affect a commercial bank’s
profitability is regulation. If regulators reduce the constraints imposed on banks, banks may take
on more risk. If banks taking on the higher degree of risk are profitable, then depositors and
shareholders gain. If, on the other hand, the banks fail, depositors lose. To incorporate the impact
of prudential surveillance and supervision, we used the required reserves of the banking system
(RES), and its interactions with GDP, RESGDP, as proxies for financial regulation. Although
prudential supervision of Islamic bank is just as necessary and desirable as it is in conventional
banks, the traditional regulatory measures are not always applicable to Islamic banks. Many
Islamic economists argue that Islamic banks should not be subject to reserve requirements
because required reserves do not generate any income to the bank. Nonetheless, we use reserve
requirements as proxies for regulation because almost all Islamic banks operate in an
environment where these traditional supervisory measures are used. Both implicit and explicit
taxes are expected to impact profits negatively13.
Studies of financial structure for the banking industry relate bank performance to several
market constraints. Competition from other providers of financial services and from the stock
market may influence bank’s operations (Fraser et al, 2000). In this study, we use the ratio of total
bank deposits to GDP (BNK) to measure the influence of the financial market, despite the fact
that financial and capital markets are still at the initial stages of development in the countries in
our sample. The size of the banking system (BNK), comprising the ratio of total assets of the
deposit money bank to GDP, is used to measure the importance of other financial competitors in
the economy. Both variables are expected to impact performance negatively. Furthermore, BNK
is also interacted with GDP and with each other. Moreover, the number of banks (BANKS) is
used to show the impact of competition on Islamic bank profitability. Finally, the total assets
(ASST), is used to control for cost differences related to bank size and for the greater ability of
larger banks to diversify. The first factor may lead to positive effects if there are significant
economies of scale while the second may have negative effects, if increased diversification leads
to higher risks and lower returns.
13 Theoretically, Islamic banks’ deposits are not supposed to be subject to reserve requirement. Therefore,
the direction of effect of RES on profitability is unclear.
15
4. Empirical Results
This section analyzes and presents the regression results. The data from the sample of 43
Islamic banks are pooled for all eight years (1994-2001) and used to replicate and extend earlier
research. Different specifications of equation (1) were estimated. As stated above, in addition to
bank-level variables, the explanatory variables used include control variables like financial
structure variables, taxation variables, and macroeconomic indicators. The estimation technique
used is panel data methods and the White (1980) procedure is used to ensure that the coefficients
are heteroskedastic14.
Tables 5 through 8 report the estimated coefficients of the panel regressions for ROA,
ROE, Net Profit before Taxes (NPBT) and Net non-Interest Margin, respectively. The results
reported are for two sets of models: the fixed effects (FE) model and/or the random effect (RE)
model, depending on the result of the Hausman specification test at the 5 percent level. The tables
show the estimated coefficients for bank characteristics, macroeconomic indicators, taxation and
financial structure. Four possible econometric specifications (for each performance measure)
were estimated. We denote them specification 1, 2, 3, and 4, respectively. The first regression in
each table is a benchmark, including the bank characteristics indicators only and excluding all
other explanatory variables. In the second regression we add the macroeconomic indicators while
the third regression adds the taxation variables. Finally, the fourth specification includes all the
above variables plus the financial structure variables. The estimation technique is robust-
covariance matrix in generalized least squares (GLS).
The first bank characteristic variable is book-to-value equity divided by total assets
lagged one period (Equity/TA (t-1)). As with Demirguc-Kunt (1997) and Berger (1995), we find
a statistically significant positive relationship between Equity/TA (t-1) and Net non-Interest
Margin. Unlike the above-mentioned studies, we find a statistically significant inverse
relationship between the equity variable and ROE, indicating that high capital ratio reduces the
returns on equity of Islamic Banks. Further, our results show an almost lack of correspondence
between the capital ratio variable and the return on assets (ROA). However, when controlling for
macro variable, taxation and finance variables, we find a significant negative relation when the
dependent variable is profitability (Table 7, specification 4).
In the regressions, the Equity/TA (t-1) variable is also interacted with per capita GDP
(measured in thousand of dollars of 1995) to see the effect of the capital ratios on bank
performance in countries with different levels of income. The results indicate that the interaction
14 The use of panel data has a number of advantages. First, it provides an increased number of data points
and generates additional degrees of freedom. Second, incorporating information relating to both cross-
section and time-series variables can substantially diminish the problems that arise from omitted variables.
16
variable has negative and statistically significant effects on net interest margin alone, indicating
that the Equity/TA (t-1) variable does not have a strong impact on bank performances in countries
with different levels of income. The effect of the interaction variable on profit before tax, ROE
and ROA are all statistically insignificant.
Next, there is an inverse and statistically significant relationship between Non-interest
earning assets variable (NIETA) and the performance measures. Note that the coefficient of non-
interest earning variable interacted with GDP is positive and statistically significant in the NIM
(specification 1), PBT (specification 1 and 3), all columns of both ROA and ROE specifications.
The coefficient of Loan/TA variable is negative and statistically significant for ROE, ROA and
profitability and negative, but insignificant, for non-Net Interest Margin only. When the
Loan/TA is interacted with GDP per capita, we find significant positive impact in specification 1
for NIM; and specification 2 and 3 in ROA and ROE.
Our results also show that the coefficients of Customer & Short-Term Funding over total
assets (CSTFTA) on Net Interest Margin (all specifications) and profitability (specification 2 and
4 only) are negative and significantly different from zero. It goes not have any impact on ROE
and ROA. The interaction of CSTFTA with GDP has no meaningful relationship with bank’s
performance measures. The next characteristic variable considered in these regressions is
overhead. Our results show that overhead (OVRHD) is directly and significantly related to non-
Interest Margin. But it does not have any significant coefficients in ROA, ROE and profitability
specifications. When Overhead is interacted with GDP per capita, the results show a significant
positive relationship in only specification 3 and 4 of profit before taxes. Therefore, conclusion
remains ambiguous.
The final bank characteristics variable, the total liabilities over total assets (LATA) has
significant positive correlation on NIM and specification 1 of ROE and ROA. However, its
impact on the other performance variables and other specifications are not significant. Its
interaction term with GDP enters the NIM equations significantly and negatively. It does not have
other significant variables.
We now discuss the effects of macroeconomic variables. Per capita GDP has significant
positive coefficient in NIM (specification 2 and 3). It does not have significant coefficient in
profitability, ROE and ROA. Next we discuss the growth rate of GDP (GDPGR) variable. It has
significant positive relation with NIM (specification 3 and 4), in all specification of profitability,
ROA and specification 1 or ROE. As regards inflation rate (INF) and its interaction term with
GDP the only significant variable is observed in specification 3 of ROA. Therefore, the impact of
these variables on the profitability measures in not conclusive.
17
Next we present the effect of taxation variables. We observe that reserve variable (RES)
and its interaction term with per capita GDP (RESGDP) have no significant relation with any of
the performance measures. Our results also show that taxation (TAX) has meaningful positive
impact on all the specifications of NIM. Its coefficient is significant only in specification 4 of
profitability and ROA. In the rest of the specifications the impact is not statistically significant.
We can cautiously conclude that, there is some statistically meaningful relationship between
taxation and profitability in Islamic Banks.
For the Financial Structure variables, our results indicate that the total assets of the
deposit money bank divided by GDP, its interaction term with GDP and number of banks does
not have a significant coefficient in any of the specifications. Concentration has significant
impact on profitability, ROE and ROA. Credit has significant and negative correlation on
profitability, ROE and ROA. Banks total assets (ASST) has negative and significant and non-zero
correlation on profitability and ROA. This implies a negative association. This negative
correlation imply that – to some extent – big size tends to be associated with less profitability in
Islamic Banks. Although it affects the other two profitability measures (ROA and Before Tax
Profit) positively, the impact is not significantly different from zero.
5. Conclusion
The preceding empirical analysis allows us to shed some light on the relationship
between banking characteristics and performance measures in Islamic Banks. First, the Islamic
banks’ profitability measures respond positively to the increases in capital and negatively to loan
ratios. The results revealed that larger equity to total asset ratio leads to more profit margins.
This finding is intuitive and consistent with previous studies. It indicates that adequate capital
ratios play an weak empirical role in explaining the performance of Islamic banks. Islamic Banks’
loan portfolio is heavily biased towards short-term trade financing. As such, their loans are low
risk and only contribute modestly to the bank profits. Bank regulators may use this as an evidence
for prompt supervisory action. Second, the results also indicate the importance of consumer and
short-term funding, non-interest earning assets, and overhead in promoting banks’ profits. A high
CSTF to total asset ratio is shown to lead to low non-interest margins. The counter intuitive
finding about the association between NNIM (net not interest margin) and overhead suggests that
high profits earned by banks may be appropriated in terms of higher wages and salaries. It
appears that the expense preference behavior appears to be holding in the Islamic banking market.
Third, the results suggest that the regulatory tax factors are important in the determination of bank
18
performance. However, our findings seem to suggest that reserve requirement does not have a
strong impact on the profitability measures. Fourth, favorable macroeconomic environment seems
to stimulate higher profits. Higher growth rate of GDP seem to have a strong positive impact on
the performance measures. However, per capita GDP seem to have limited effect on performance.
Inflation rate and its interaction term with GDP do not seem to have a significant impact on
performance. Finally, the size of the banking system has negative impact on the profitability
except net non interest margin.
19
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21
Table 1 Number of Banks by Country and By Year
Country \ Year 1994 1995 1996 1997 1998 1999 2000 2001
ALGERIA 1 1 1 1 1 1 1
BAHAMAS 1 1 1 1
BAHRAIN 3 3 3 4 5 5 4 4
BANGLADESH 1 1 1 1 1 1 1 2
BRUNEI DARUSSALAM 2 2 2 3 3 3 3
EGYPT 1 2 2 2 2 2 2 1
GAMBIA 1 1 1 1
INDONESIA 1 1 1 1 1
IRAN 1 1 3 3 3 3
JORDAN 1 1 1 1 2 2 2 2
KUWAIT 1 1 1 1 1 1 1 1
LEBANON 1 1 1 1 1 1
MALAYSIA 2 2 2 3 3 3 3
MAURITANIA 1 1 1
QATAR 1 2 2 2 2 2 2 2
SAUDI ARABIA 1 1 1 1 1 1 1
SUDAN 2 2 3 3 3 3 1 1
TUNISIA 1 1 1 1 1 1 1
UNITED ARAB EMIRATES 1 1 1 1 2 2 2 2
UNITED KINGDOM 1 1 1 1 1 1 1
YEMEN 1 1 2 2 2 2
Total 18 23 25 31 39 39 34 22
Source: Bank Scope (2002)
Table 2. Economics and Institutional Indicators (Countries where there are Islamic Banks)
All variables, but deposit insurance, are averaged over the period 1994-2001 (or the most available years). The deposit insurance variable takes value 1 if the
country has explicit insurance deposit coverage (as of 2001) and zero otherwise. Number of banks is the number of bank with at least three years of complete
information in a given country.
Country Gdp
/Cap
(US $ 1995)
Growth Inflation Real interest Reserves
/deposit
Bank
/GDP
tax Deposit
Insurance
Concen-
tration
Number of
banks(a)
Credit(b)
A
LGERIA 1,536 2.69 16.88 -1.66 1.97 29.48 14.12 0 65.92 4.86 10.84
BAHRAIN 10,175 3.57 0.36 11.62 7.58 54.97 -7.87 1 83.84 13.71 2.69
BANGLADESH 344 5.03 3.98 10.16 12.21 30.48 57.23 1 9.13 21.25 64.90
BRUNEI 17,675 n.a. n.a. n.a. n.a. n.a. 35.17 0 n.a. 3.00 n.a.
EGYPT 1,134 4.96 5.63 8.18 19.72 77.61 3.65 0 47.77 30.07 59.95
GAMBIA 357 5.45 3.27 20.80 16.44 22.63 3.27 0 40.14 2.25 49.02
INDONESIA 1,034 0.98 24.40 2.31 15.88 56.71 45.28 0 38.36 69.20 63.02
IRAN 1,574 3.20 23.15 n.a. 31.64 20.91 75.81 0 49.23 6.94 56.71
JORDAN 1,613 3.75 1.82 9.74 46.69 78.43 44.74 0 75.41 11.00 17.60
KUWAIT 15,056 1.55 6.49 4.09 1.76 100.52 0.27 0 26.25 9.57 18.44
LEBANON 2,840 4.42 6.67 14.84 16.38 127.40 14.72 1 31.92 63.33 29.68
MALAYSIA 4,600 4.94 3.04 5.09 16.79 117.47 68.17 0 27.58 45.43 34.58
MAURITANIA 489 4.27 7.42 n.a. 11.73 11.35 n.a. 0 96.68 3.67 87.63
QATAR 19,907 n.a. n.a. n.a. 4.93 72.14 n.a 0 79.44 6.71 37.28
SAUDI ARABIA 6,836 1.55 5.15 5.35 6.05 46.37 n.a 0 50.26 13.86 82.95
SUDAN 284 5.77 49.44 n.a. 26.03 0.01 68.19 0 59.83 6.05 45.20
TUNISIA 2,254 4.88 3.76 n.a. 6.22 57.09 5.04 0 38.98 16.13 73.02
UA Emiratos 17,988 1.95 1.98 n.a. 16.25 61.69 n.a. 0 43.29 20.16 52.68
YEMEN 306 4.47 13.95 6.79 25.40 8.72 n.a. 0 60.44 6.40 44.20
(a) Number of banks includes Commercial Banks, Islamic Banks and Non-banking credit institutions
(b) Credit is Domestics Credit to Privates Sector / Total Assets Banking System
Table 3: Benchmark Performance Measures of Islamic Banks vis-à-vis Conventional Banks
Average of Commercial Banks with similar asset size in the countries where Islamic Banks are present.
Commercial banks are selected in a way they are similar to Islamic Banks in size, measured in total assets.
All commercial banks are selected the third quartile by size in each country in 2001. The value of each ratio
represents the average in the period 1994-2001. All ratio definitions are given in the appendix.
Mean
Assets Quality Commercial Islamic Difference P-value
Loan Loss Res / Gross Loans 5.31 2.19 3.12 0.02
Loan Loss Prov / Net Int Rev 58.44 16.44 42.00 0.13
Loan Loss Res / Impaired Loans 236.48 379.65 -143.17 0.25
Impaired Loans / Gross Loans 4.84 0.76 4.08 0.03
NCO / Average Gross Loans 0.96 0.00 0.96 0.03
NCO / Net Inc Bef Ln Lss Prov 54.44 0.30 54.15 0.16
Capital
Equity / Tot Assets 7.89 12.22 -4.33 0.03
Equity / Net Loans 15.13 25.13 -10.00 0.04
Equity / Cust & ST Funding 9.83 19.79 -9.96 0.01
Equity / Liabilities 8.62 14.20 -5.58 0.04
Cap Funds / Tot Assets 8.18 12.23 -4.05 0.04
Cap Funds / Net Loans 15.68 25.16 -9.48 0.05
Cap Funds / Cust & ST Funding 10.20 19.81 -9.61 0.02
Cap Funds / Liabilities 8.94 14.21 -5.28 0.05
Subord Debt / Cap Funds 3.65 0.17 3.48 0.00
Operations
Net Interest Margin 3.31 2.51 0.80 0.14
Net Int Rev / Avg Assets 2.92 2.00 0.92 0.06
Oth Op Inc / Avg Assets 1.93 0.88 1.04 0.01
Non Int Exp / Avg Assets 4.11 2.00 2.10 0.01
Pre-Tax Op Inc / Avg Assets 0.96 0.60 0.36 0.64
Non Op Items & Taxes / Avg Ast 0.38 -0.02 0.40 0.00
Return On Avg Assets (ROAA) 0.58 0.62 -0.04 0.95
Return On Avg Equity (ROAE) 5.93 5.26 0.68 0.95
Dividend Pay-Out 29.61 32.65 -3.04 0.84
Inc Net Of Dist / Avg Equity -3.30 3.76 -7.06 0.42
Non Op Items / Net Income 7.53 -34.45 41.97 0.03
Cost To Income Ratio 56.87 56.39 0.48 0.93
Recurring Earning Power 2.40 0.93 1.47 0.02
Liquidity
Interbank Ratio 191.96 426.72 -234.76 0.15
Net Loans / Tot Assets 53.24 49.91 3.33 0.29
Net Loans / Cust & ST Funding 66.25 79.13 -12.89 0.01
Net Loans / Tot Dep & Bor 62.91 66.42 -3.51 0.35
Liquid Assets / Cust & ST Funding 30.61 41.45 -10.85 0.01
Liquid Assets / Tot Dep & Bor 29.07 34.53 -5.46 0.10
24
Table 4: Benchmark Performance Measures of Islamic Banks vis-à-vis Conventional Banks
Average of Commercial Banks with similar level of deposits in the countries where Islamic Banks are
present. Commercial banks are selected in a way they are similar to Islamic Banks in deposits. All
commercial banks are selected by the third quartile by deposit in each country in 2001. The value of each
ratio represents the average in the period 1994-2001. All ratio definitions are given in the appendix.
Mean
Assets Quality Commercial Islamic Difference P-value
Loan Loss Res / Gross Loans 7.53 2.19 5.34 0.03
Loan Loss Prov / Net Int Rev 71.63 16.44 55.19 0.25
Loan Loss Res / Impaired Loans 215.38 379.65 -164.27 0.09
Impaired Loans / Gross Loans 5.59 0.76 4.83 0.02
NCO / Average Gross Loans 1.20 0.00 1.20 0.08
NCO / Net Inc Bef Ln Lss Prov 15.74 0.30 15.45 0.01
Capital
Equity / Tot Assets 6.39 12.22 -5.83 0.02
Equity / Net Loans 12.76 25.13 -12.37 0.03
Equity / Cust & ST Funding 7.79 19.79 -12.00 0.01
Equity / Liabilities 7.03 14.20 -7.17 0.02
Cap Funds / Tot Assets 6.71 12.23 -5.52 0.03
Cap Funds / Net Loans 13.38 25.16 -11.78 0.04
Cap Funds / Cust & ST Funding 8.18 19.81 -11.64 0.01
Cap Funds / Liabilities 7.38 14.21 -6.84 0.02
Subord Debt / Cap Funds 2.51 0.17 2.34 0.06
Operations
Net Interest Margin 2.77 2.51 0.26 0.75
Net Int Rev / Avg Assets 2.52 2.00 0.52 0.48
Oth Op Inc / Avg Assets 1.57 0.88 0.69 0.00
Non Int Exp / Avg Assets 4.69 2.00 2.69 0.05
Pre-Tax Op Inc / Avg Assets -0.43 0.60 -1.03 0.55
Non Op Items & Taxes / Avg Ast 0.31 -0.02 0.33 0.09
Return On Avg Assets (ROAA) -0.75 0.62 -1.36 0.39
Return On Avg Equity (ROAE) -85.65 5.26 -90.90 0.21
Dividend Pay-Out 23.87 32.65 -8.78 0.41
Inc Net Of Dist / Avg Equity -99.15 3.76 -102.91 0.20
Non Op Items / Net Income 7.76 -34.45 42.20 0.03
Cost To Income Ratio 79.86 56.39 23.47 0.31
Recurring Earning Power 1.75 0.93 0.83 0.23
Liquidity
Interbank Ratio 191.56 426.72 -235.16 0.15
Net Loans / Tot Assets 50.87 49.91 0.97 0.81
Net Loans / Cust & ST Funding 61.45 79.13 -17.68 0.00
Net Loans / Tot Dep & Bor 58.25 66.42 -8.17 0.11
Liquid Assets / Cust & ST Funding 31.90 41.45 -9.56 0.06
Liquid Assets / Tot Dep & Bor 30.16 34.53 -4.37 0.30
25
Table 5: Determinants of Return on Assets (ROA)
The regression is estimated using GLS estimation pooling bank level across 21 countries where there are Islamic Banks for the 1994-
2001 period. Regression also includes countries dummies, which are not reported. Dependent variable is return on assets, which is
defined as net income (profit after taxes) over total earning assets. Detailed variable definitions and data sources are given in the
appendix. Standard errors are given in parenthesis.
1 2 3 4
Bank C haracteristics
EQTA(-1) 0.022
(0.014)
-0.029
(0.057)
-0.023
(0.051)
-0.031
(0.051)
EQAGDP(-1) 0.001
(0.006)
0.004
(0.010)
0.004
(0.010)
0.004
(0.010)
LOANTA -0.015**
(0.008)
-0.022***
(0.008)
-0.024***
(0.008)
-0.018**
(0.007)
LONGDP 0.004
(0.003)
0.007*
(0.004)
0.007*
(0.004)
0.005
(0.004)
NIEATA -0.039***
(0.015)
-0.056***
(0.020)
-0.056***
(0.018)
-0.058***
(0.015)
NIEAGDP 0.019***
(0.006)
0.019*
(0.011)
0.022*
(0.011)
0.021*
(0.012)
CSTFTA -0.006
(0.013)
-0.019
(0.016)
-0.018
(0.016)
-0.020
(0.016)
CSTFGDP -0.001
(0.003)
-0.001
(0.003)
0.000
(0.003)
0.000
(0.003)
OVRHD -0.090
(0.179)
0.016
(0.124)
0.008
(0.129)
-0.033
(0.138)
OVRGDP 0.031
(0.045)
0.030
(0.045)
0.054*
(0.030)
0.051*
(0.029)
LATA 0.052*
(0.031)
0.020
(0.097)
0.027
(0.087)
0.042
(0.085)
LATAGDP -0.006
(0.007)
-0.004
(0.016)
-0.004
(0.015)
-0.006
(0.015)
Macro variables
GDPPC -0.012
(0.020)
-0.008
(0.021)
-0.005
(0.022)
GDPGR 0.247*
(0.143)
0.397**
(0.166)
0.397***
(0.128)
INF -0.017
(0.015)
-0.010
(0.014)
0.013
(0.016)
INFGDP 0.004
(0.003)
0.005**
(0.003)
0.006
(0.005)
Financial Structure
RES 0.051
(0.106)
0.050
(0.109)
RESGDP -0.051
(0.040)
-0.045
(0.037)
TAX -0.005
(0.003)
-0.005*
(0.003)
TAXGDP 0.002***
(0.001)
0.002***
(0.000)
BANK 0.041
(0.036)
BANKGDP 0.002
(0.007)
NUMBER 0.000
(0.000)
CONCEN 0.056**
(0.024)
CREDIT -0.042**
(0.020)
ASST 0.000*
(0.000)
C -0.002
(0.020)
0.047
(0.096)
0.023
(0.097)
-0.008
(0.100)
Adjusted R2 0.28 0.33 0.40 0.46
N 157 143 143 143
*, ** and ***indicate significance level of 10, 5 and 1 percent respectively.
26
Table 6: Determinants of Return on Equity (ROE)
The regression is estimated using GLS estimation pooling bank level across 21 countries where there are Islamic Banks for the 1994-
2001 period. Regression also includes countries dummies, which are not reported. Dependent variable is return on assets, which is
defined as net income (profit after taxes) over equity. Detailed variable definitions and data sources are given in the appendix.
Standard errors are given in parenthesis.
1 2 3 4
Bank C haracteristics
EQTA(-1) -0.611*
(0.338)
-0.696
(0.861)
-0.753
(0.780)
-0.923
(0.715)
EQAGDP(-1) 0.059
(0.093)
0.060
(0.150)
0.065
(0.148)
0.053
(0.146)
LOANTA -0.316**
(0.139)
-0.352**
(0.135)
-0.353***
(0.124)
-0.265**
(0.114)
LONGDP 0.079
(0.058)
0.092**
(0.046)
0.086*
(0.046)
0.061
(0.044)
NIEATA -0.985***
(0.301)
-0.909***
(0.329)
-0.862***
(0.302)
-0.880***
(0.278)
NIEAGDP 0.337**
(0.139)
0.347*
(0.193)
0.372*
(0.205)
0.347*
(0.206)
CSTFTA -0.056
(0.233)
0.061
(0.244)
-0.023
(0.241)
-0.080
(0.229)
CSTFGDP -0.059
(0.049)
-0.061
(0.044)
-0.045
(0.040)
-0.041
(0.037)
OVRHD -0.440
(1.772)
0.475
(1.781)
0.767
(1.747)
0.465
(1.670)
OVRGDP -0.299
(0.453)
-0.304
(0.380)
-0.376
(0.416)
-0.477
(0.409)
LATA 1.184**
(0.534)
1.806
(1.977)
1.683
(1.717)
1.759
(1.588)
LATAGDP -0.088
(0.124)
-0.149
(0.287)
-0.137
(0.273)
-0.176
(0.265)
Macro variables
GDPPC -0.075
(0.313)
-0.052
(0.336)
-0.064
(0.331)
GDPGR 1.075
(1.434)
1.609
(1.526)
2.143*
(1.150)
INF 0.074
(0.173)
0.069
(0.160)
0.275
(0.185)
INFGDP 0.034
(0.037)
0.055
(0.039)
0.133
(0.092)
Financial Structure
RES -0.876
(1.336)
-1.001
(1.403)
RESGDP -0.350
(0.565)
-0.261
(0.531)
TAX -0.026
(0.046)
-0.023
(0.044)
TAXGDP 0.006
(0.007)
0.005
(0.007)
BANK 0.235
(0.436)
BANKGDP 0.118
(0.103)
NUMBER -0.006
(0.006)
CONCEN 0.576**
(0.225)
CREDIT -0.430**
(0.191)
ASST -0.004*
(0.002)
C -0.268
(0.370)
-0.912
(1.838)
-0.564
(1.724)
-0.715
(1.647)
Adjusted R2 0.28 0.29 0.29 0.35
N 157 143 143 143
*, ** and ***indicate significance level of 10, 5 and 1 percent respectively.
27
Table 7: Determinants of Profit before taxes (PBT)
The regression is estimated using GLS estimation pooling bank level across 21 countries where there are Islamic Banks for the 1994-
2001 period. Regression also includes countries dummies, which are not reported. Dependent variable is the profit before taxes, which
is defined as profit before taxes over total assets. Detailed variable definitions and data sources are given in the appendix. Standard
errors are given in parenthesis.
1 2 3 4
Bank C haracteristics
EQTA(-1) 0.000
(0.015)
-0.108
(0.072)
-0.096
(0.059)
-0.109*
(0.056)
EQAGDP(-1) 0.002
(0.006)
0.008
(0.009)
0.007
(0.009)
0.007
(0.009)
LOANTA -0.015*
(0.009)
-0.024**
(0.009)
-0.027***
(0.009)
-0.019**
(0.008)
LONGDP 0.002
(0.004)
0.004
(0.004)
0.005
(0.004)
0.003
(0.003)
NIEATA -0.053***
(0.019)
-0.080***
(0.026)
-0.080***
(0.022)
-0.081***
(0.017)
NIEAGDP 0.018***
(0.006)
0.016
(0.011)
0.019*
(0.011)
0.018
(0.011)
CSTFTA -0.008
(0.017)
-0.036*
(0.021)
-0.032
(0.020)
-0.033*
(0.020)
CSTFGDP -0.002
(0.003)
-0.002
(0.003)
-0.001
(0.003)
-0.001
(0.003)
OVRHD -0.152
(0.259)
0.043
(0.156)
0.026
(0.155)
-0.022
(0.157)
OVRGDP 0.033
(0.056)
0.023
(0.056)
0.060**
(0.028)
0.055**
(0.027)
LATA 0.044
(0.035)
-0.017
(0.104)
-0.002
(0.093)
0.009
(0.093)
LATAGDP -0.004
(0.007)
-0.002
(0.016)
-0.003
(0.015)
-0.005
(0.015)
Macro variables
GDPPC -0.013
(0.021)
-0.009
(0.020)
-0.004
(0.022)
GDPGR 0.381*
(0.214)
0.600**
(0.255)
0.599***
(0.199)
INF -0.026
(0.022)
-0.015
(0.019)
0.018
(0.021)
INFGDP 0.004
(0.003)
0.006
(0.004)
0.007
(0.006)
Financial Structure
RES 0.097
(0.115)
0.103
(0.113)
RESGDP -0.070
(0.048)
-0.065
(0.042)
TAX -0.006
(0.005)
-0.006*
(0.004)
TAXGDP 0.003***
(0.001)
0.003***
(0.001)
BANK 0.065
(0.045)
BANKGDP 0.000
(0.007)
NUMBER -0.001
(0.001)
CONCEN 0.076**
(0.036)
CREDIT -0.060*
(0.031)
ASST -0.0005*
(0.0003)
C 0.012
(0.022)
0.106
(0.108)
0.060
(0.101)
0.025
(0.105)
Adjusted R2 0.21 0.31 0.44 0.53
N 157 143 143 143
*, ** and ***indicate significance level of 10, 5 and 1 percent respectively.
28
Table 8: Determinants of Net Non Interest Margin (NNIM)
The regression is estimated using GLS estimation pooling bank level across 20 countries where there are Islamic Banks for the 1994-
2002 period. Regression also includes countries dummies, which are not reported. Dependent variable is the net interest margin, which
is defined as net interest income over total earning assets. Detailed variable definitions and data sources are given in the appendix.
Standard errors are given in parenthesis.
1 2 3 4
Bank C haracteristics
EQTA(-1) 0.073**
(0.036)
0.182**
(0.076)
0.254***
(0.091)
0.238**
(0.095)
EQAGDP(-1) -0.001
(0.005)
-0.012*
(0.007)
-0.016**
(0.007)
-0.015**
(0.008)
LOANTA -0.014
(0.015)
-0.025
(0.016)
-0.024
(0.016)
-0.019
(0.015)
LONGDP 0.008*
(0.004)
0.008
(0.005)
0.008
(0.006)
0.006
(0.006)
NIEATA -0.105*
(0.057)
-0.108**
(0.053)
-0.074
(0.049)
-0.084*
(0.049)
NIEAGDP 0.017***
(0.006)
0.005
(0.008)
-0.005
(0.009)
-0.006
(0.009)
CSTFTA -0.169***
(0.062)
-0.153**
(0.063)
-0.111**
(0.054)
-0.119**
(0.055)
CSTFGDP 0.011**
(0.004)
0.007
(0.005)
0.002
(0.004)
0.004
(0.005)
OVRHD 2.665***
(0.769)
2.907***
(0.826)
2.906***
(0.732)
2.959***
(0.763)
OVRGDP -0.126
(0.094)
-0.140
(0.103)
-0.124
(0.094)
-0.130
(0.094)
LATA 0.241***
(0.085)
0.554***
(0.178)
0.655***
(0.183)
0.622***
(0.179)
LATAGDP -0.018***
(0.007)
-0.049***
(0.016)
-0.055***
(0.016)
-0.054***
(0.016)
Macro variables
GDPPC 0.035**
(0.015)
0.044**
(0.017)
0.034
(0.022)
GDPGR 0.250
(0.164)
0.512***
(0.177)
0.471**
(0.195)
INF -0.015
(0.042)
-0.017
(0.040)
-0.014
(0.052)
INFGDP 0.006
(0.005)
0.005
(0.004)
0.008
(0.008)
Financial Structure
RES 0.095
(0.254)
0.009
(0.293)
RESGDP 0.004
(0.058)
0.022
(0.062)
TAX 0.026**
(0.013)
0.026**
(0.013)
TAXGDP -0.002
(0.002)
-0.002
(0.002)
BANK -0.091
(0.105)
BANKGDP 0.011
(0.011)
NUMBER 0.001
(0.001)
CONCEN 0.025
(0.050)
CREDIT -0.020
(0.043)
ASST -0.001
(0.001)
C -0.087*
(0.047)
-0.398***
(0.149)
-0.573***
(0.183)
-0.517**
(0.199)
Adjusted R2 0.56 0.58 0.63 0.62
N 157 143 143 143
*, ** and ***indicate significance level of 10, 5 and 1 percent respectively.
29
Appendix A:
Bank Characteristics
• Net Interest Margin: interest income minus interest expenses over total assets
• Net Profit/TA: before tax profit over total assets
• Equity/TA: book value of equities (assets minus liabilities) over total assets
• Loan/TA: total loans over total assets
• Non-interest earning assets/TA: cash, non-interest earning deposit at other banks, and
other non-interest assets
• Customer & Short term funding/TA: all short term and long term deposits plus other
non-deposit short term funding over total assets
• OVERHEAD/TA: personnel expenses and some other non-interest expenses over total
assets
All bank level data and variables are obtained from BankScope database.
Macro Indicators
• GDP/CAP: real GDP per capital in constant 1995 US$
• Real Interest: the nominal interest rate minus rate of inflation. Where available, nominal
rate is the lending rate. Otherwise, deposit rate is used
• Data onGDP, population and interest rate are from International Financial Statistic (IFS).
Taxation
• Reserves: Reserve of the banking system over deposit of the banking system (time,
saving and demand deposit) multiplied by Customer & Short Term Funding/TA for each
bank.
Financial Structure
• BANK/GDP: Total deposit (time, demand and saving) of banking system divided by
GDP.
• Number of banks: Number of banks with complete data in the BankScope database
• Total Assets (TA): Total assets of each bank in a given year in US million $ (from
BankScope database)
Asset Quality15
• Loan Loss Res / Gross Loans: The loan loss reserve over gross loan ratio indicats how
much of the total portfolio has been provided for but not charged off. It is a reserve for
losses expressed as percentage of total loans. Given a similar charge-off policy, the
higher the ratio the poorer will be the quality of the loan portfolio.
• Loan Loss Prov / Net Int Rev: Loan loss provision over net interest revenue presents the
relationship between provisions in the profit and loss account and the interest income
over the same period. Ideally this ratio should be as low as possible. In a well-run bank, if
the lending book is higher in risk, this would be reflected by higher interest margins. If
the ratio deteriorates this means that risk is not being properly remunerated by margins
• Loan Loss Res / Impaired Loans: The loan loss reserve over impaired loans (non-
performing loans) ratio relates loan loss reserves to non-performing or impaired loans.
The higher this ratio is the better provided the bank is and the more comfortable we will
feel about the assets quality.
• Impaired Loans / Gross Loans: This is a measure of the amount of total loans which are
doubtful. The lower this figure is the better the assets quality
• NCO / Average Gross Loans: Net charge off or the amount written-off from loan loss
reserves less recoveries is measured as a percentage of the gross loans. It indicates what
15 All definitions of Asset Quality, Capital, Operations and Liquidity were obtained from the BankScope database
30
percentage of today’s loans have been finally been written off the books. The lower this
figure the better as long as the write off policy is consistent across comparable bank
• NCO / Net Inc Bef Ln Lss Prov: Net charge-off over net income before loan loss
provision ratio is measured similar to charge-offs but against income generated in the
year. The lower this ratio is the better, other things being equal.
Capital
• Equity / Tot Assets: This ratio measures the ability of the bank to withstand losses. A
declining trend in this ratio may signal increased risk exposure and possibly capital
adequacy problem.
• Equity / Net Loans: this ratio measures the equity cushion available to absorb losses on
the loan book
• Equity / Cust & ST Funding: This ratio measures the amount of permanent funding
relative to short term potentially volatile funding. The higher this ratio is the better.
• Equity / Liabilities: This leverage ratio is simply another way of looking at the equity
funding of the balance sheet and is another way of looking at capital adequacy.
• Cap Funds / Tot Assets:
• Cap Funds / Net Loans:
• Cap Funds / Cust & ST Funding:
• Cap Funds / Liabilities:
• Subord Debt / Cap Funds: this ratio indicates what percentage of total capital funds is
provided in the form of subordinated debt.
Operations
• Net Interest Margin: This ratio is the net interest income expressed as a percentage of
earning assets. The higher this ratio, the cheaper the funding or the higher the margin the
bank is commanding. Higher margins and profitability are desirable as long as the asset
quality is being maintained
• Net Int Rev / Avg Assets16: Net Interest Income over average assets indicates that the
item is averaged using the net income expressed as a percentage of the total balance sheet
• Oth Op Inc / Avg Assets: Other operating income over average assets. When compared
to the above ratio, this indicates to what extent fees and other income represent a greater
percentage of earnings of the bank. As long as this is not volatile trading income it can be
seen as a lower risk form of income. The higher this figure is the better
• Non Int Exp / Avg Assets: Non interest expenses or overheads plus provisions give a
measure of the cost side of the banks performance relative to the assets invested.
• Pre-Tax Op Inc / Avg Assets: This is a measure of the operating performance of the
bank before tax and unusual items. This is a good measure of profitability unaffected by
one off non trading activities.
• Non Op Items & Taxes / Avg Ast: This ratio measures costs and tax as a percentage of
assets.
• Return On Avg Assets (ROAA)
• Return On Avg Equity (ROAE)
• Dividend Pay-Out: This is a measure of the amount of post tax profits paid out to
shareholders. In general the higher the ratio the better but not if it is at the cost of
restricting reinvestment in the bank and its ability to grow its business.
• Inc Net Of Dist / Avg Equity: This ratio is effectively the return on equity after
deducting the dividend from the return and it shows by what percentage the equity has
increased from internally generated funds. The higher the better.
• Non Op Items / Net Income: This denotes what percentage of total net income consists
of unusual items.
• Cost to Income Ratio: This is one of the most focused on ratios currently and measures
the overheads or costs of running the bank, the major element of which is normally
salaries, as percentage of income generated before provisions. It is a measure of
efficiency although if the lending margins in a particuar country are very high then the
16 The acronym "AVG" stands for the arithmetic mean of the value at the end of year t and t-1
31
ratio will improve as a result. It can be distorted by high net income from associates or
volatile trading income.
• Recurring Earning Power: This ratio is a measure of after tax profits adding back
provisions for bad debts as a percentage of Total Assets. Effectively this is a return on
assets performance measurement without deducting provisions.
Liquidity
• Interbank Ratio: this is money lent to other banks divided by money borrowed from
other banks. If this ratio is greater than 100 then it indicates the bank is net placer rather
than a borrower of funds in the market place, and therefore more liquid.
• Net Loans / Tot Assets: This liquidity ratio indicates what percentage of the assets of the
bank are tied up in loans. The higher this ratio the less liquid the bank will be.
• Net Loans / Cust & ST Funding: This loans to deposit ratio is a measure of liquidity in
as much as high figures denotes lower liquidity.
• Net Loans / Tot Dep & Bor: This similar ratio has as its denominator deposits and
borrowings with the exception of capital instruments.
• Liquid Assets / Cust & ST Funding: This is a deposit run off ratio and looks at what
percentage of customer and short term funds could be met if they were withdrawn
suddenly, the higher this percentage the more liquid the bank is and less vulnerable to a
classic run on the bank.
• Liquid Assets / Tot Dep & Bor: This ratio is similar to the mentioned above but looks at
the amount of liquid assets available to borrower as well as depositors.
