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Bank lending, deposits and risk-taking in times of crisis: A panel
analysis of islamic and conventional banks
Mansor H. Ibrahim, Syed Aun R. Rizvi
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BANK LENDING, DEPOSITS AND RISK-TAKING IN TIMES OF CRISIS: A PANEL
ANALYSIS OF ISLAMIC AND CONVENTIONAL BANKS
Mansor H. Ibrahim
School of Graduate Studies
International Centre for Education in Islamic Finance (INCEIF)
Lorong Universiti A
59100 Kuala Lumpur MALAYSIA
Email: mansorhi@hotmail.com and mansorhi@inceif.org
Syed Aun R. Rizvi
Suleman Dawood School of Business
Lahore University of Management Sciences (LUMS)
Opp Sector U, DHA IV,
Lahore, Pakistan
Email: aun@rizvis.net and aun.raza@lums.edu.pk
Acknowledgement: We would like to thank the two anonymous referees for helpful comments. We also
would like to thank participants at the 2nd Applied Modelling Conference held at Deakin University, 2-3
February 2017. The usual disclaimer applies.
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BANK LENDING, DEPOSITS AND RISK-TAKING IN TIMES OF CRISIS: A PANEL
ANALYSIS OF ISLAMIC AND CONVENTIONAL BANKS
ABSTRACT
In this study, we conduct a panel analysis of Islamic and conventional banks to ascertain whether
Islamic banks are able to sustain financing supply and whether its growth is higher than
conventional bank lending growth in times of stress. For concreteness, we also assess whether
the sustained financing supply of Islamic banks is justified by a concomitant increase in Islamic
deposit growth and is not linked to excessive risk taking. Utilizing a panel sample of 25 Islamic
banks and 114 conventional banks from 10 dual-banking countries, we observe sustained
financing supply by Islamic banks but significant reduction in the lending growth by
conventional banks during the crisis period. The results further suggest that the financing growth
of Islamic banks is higher than the lending growth of conventional banks during the crisis period.
However, we find no clear evidence that the deposit growth of Islamic banks behaves differently
during the period. Finally, there is no indication to suggest that Islamic banks exhibit excessive
risk taking in times of stress. Our results contribute to the evidence supporting the contributive
role of the Islamic banking system to financial and economic stability.
Keywords: Dual-Banking System, Credit Growth, Deposit Growth, Credit Risk, Crisis
JEL Classification: G21, G01, C33
1. INTRODUCTION
Credit provision is essential in ensuring sustained real production and in stimulating
economic activities. Thus, it is not surprising that, facing crises, policymakers resort to various
mechanisms so that banks are able to maintain their credit supply. These include nationalization
of distressed banks, deposit guarantees, fund injections and revision of capital requirements
(Chen et al., 2016). In parallel, scholarly studies have searched for factors that allow banks to
perform and function reasonably well in times of stress to inform policymakers what might be
appropriate policy actions to stabilize bank credit. They have brought into their focus various
bank-specific characteristics, notable among which are bank ownership (Brei and Schclarek,
2013; Cull and Martinez-Peria, 2013; Chen et al., 2016), bank types or business orientation
(Chiaramonte et al., 2015; Merilainen, 2016) and bank capital (Kosak et al., 2015; Altunbas et
al., 2016).
In search for solutions to the problem of bank-originated financial instability, some studies
have turned their attention to the Islamic banking business model. Drawn by its rapid
development even during the recent global financial crisis, they make comparative assessments
of Islamic banks and conventional banks using a variety of bank performance metrics. As
regards Islamic bank financing, few studies are available (Hasan and Dridi, 2011; Beck et al.,
2013; and Ibrahim 2016). In general, they paint a bright picture of Islamic banks for their stable
or even increasing supply of credit during crisis episodes. Still, despite the alleged resiliency of
Islamic banks, whether the Islamic banking sector is relatively more stable than its conventional
counterparts remains contentious (Mejia et al., 2014; Kabir and Worthington, 2017).
Accordingly, it is still an open empirical question whether Islamic banking can be a stabilizing
force in times of stress.
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This paper builds upon these studies by providing further empirical assessments of Islamic
and conventional banks in times of crisis. In this paper, we contend that a look at
lending/financing per se would not lead to a reliable conclusion about the ability of a bank to be
a stabilizing force in times of crisis. Expansion of credit, especially during crisis episodes,
necessarily requires funding and entails additional risks. Further, with intensified asymmetric
information problem, credit expansion during the crisis period may signify excessive risk taking.
Thus, we would be more confident of a bank’s ability to be a stabilizing force if (i) it has the
ability to expand credit in times of crisis and (ii) its credit expansion does not lead to excessive
risk in the future. The present study considers these aspects in the analysis to arrive at a more
concrete answer to the potential role of Islamic banks in the economy.
Our contribution to the literature is threefold. First, we add to the strand of research on bank
behaviour in times of stress by bringing into the fore the potential role of the Islamic banking
sector. Second, we contribute to the on-going debate on the relative stability of Islamic banks
vis-a-vis conventional banks. In the literature, there are two competing views - the stability view
and the skeptic view (Ibrahim, 2016). The stability view posits that the Islamic banking sector
is amiable to banking stability, which is rooted in their distinct features of being Shariahcompliant (Farooq and Zaheer, 2015)1. By contrast, emphasizing no distinct differences between
Islamic banks and conventional banks in practice, the “skeptic” view doubts the Islamic banks
would make a difference (Chong and Liu, 2009; Khan, 2010). We bring into the picture the
financing decisions by banks to ascertain whether Islamic banks are able to stabilize credit
during crisis episodes.
And third, from the analytical perspective, we provide a more comprehensive evaluation of
bank financing by synthesizing various aspects of banking studies to bring about concrete
inferences as to whether the Islamic banking system contributes favourably to stability. We take
lead from the recent contribution by Chen et al. (2016) that not only looks at loan growth during
the global financial crisis but also assesses how loan increases during the crisis affect bank
performance and aggregate activities. We broaden our analysis to include deposit growth and
credit risk. The former is to assess, if Islamic banks did have higher credit growth, whether
higher credit growth was accompanied by higher deposit growth as an indicator of their ability
to expand credit. Meanwhile, the latter is to verify whether credit expansion was accompanied
by higher future risk and whether it can be construed as excessive risk taking by Islamic banks.
The rest of this paper is structured as follows. Section 2 provides background information
and develops empirical hypotheses. Section 3 details model specifications. Section 4 presents
the data and discusses estimation results. Finally, section 5 contains a summary of the main
findings and concluding remarks.
2. BACKGROUND AND HYPOTHESES DEVELOPMENT
To place the present study in its context, we first provide background information of the
Islamic banking. The emphasis is on the (theoretical) differences of the Islamic banks from their
conventional counterparts and the theory-practice divide in Islamic banking and resulting
arguments whether the Islamic banking system contributes to stability. Then, on the bases of
According to Farooq and Zaheer (2015), the distinct features of the Islamic banking include prohibition of interest
rate, speculative activities and of excessive uncertainty and Islamic banking transactions being linked
fundamentally to the real sector.
1
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various contentious views and of existing literature, we develop hypotheses to be tested.
2.1 Islamic Banking
The Islamic banking industry has evolved to be a material part of the global financial
industry. While figures differ according to sources, the growth of Islamic banking assets has
outpaced that of conventional banking assets even during the global financial crisis. Indeed, as
noted by Hasan and Dridi (2011), the asset growth of Islamic banks was more than double that
of conventional banks during 2007-2009, i.e. the global financial crisis period. Starting in only
few Muslim-majority countries in the 1960s and 1970s, the Islamic banking presently operates
in more than 75 countries. Such multinational conventional banks as Chase Manhattan,
Citibank, and HSBC have also participated in the provisions of the Islamic banking services by
establishing Islamic banking windows or Islamic bank subsidiaries.
Islamic banking differentiates itself from the existing conventional banking through its
adherence to the Islamic laws (Shari’ah). A key feature of the Islamic banking operations is the
prohibition of interest rate (riba). According to Islam, charging interest rate is a form of injustice
since it allows financiers to gain benefits simply by transferring their money to others without
being involved in real activities. In other words, it is unlawful for money to beget money without
real sector contributions (Ariff, 2014). In addition, Islamic banks must not be involved in
transactions characterized by extreme uncertainties (gharar) and gambling (maysir). Thus, they
must not expose themselves to such toxic assets as collateralized debt obligation (CDO) and
mortgage-back security (MBS) and derivative products (Mollah et al., 2017). Moreover, the
parties to Islamic financial transactions must honour the sanctity of contracts to their best ability
such that asymmetric information and morally hazardous behaviour are contained. Needless to
state, the Islamic banks must confine their activities to those allowable by Islam. Accordingly,
financing related to the production and sales of alcoholic beverages, gambling and prostitution
is prohibited.
As a manifestation of their conformance to the Shari’ah, Islamic banks arrange their
financial transactions at both ends of the balance sheet on the basis of profit-and-loss sharing
and real-sector linkages. The profit-and-loss sharing arrangement either through mudarabah or
musharakah contract identifies depositors on the liability side and banks on the asset side as
investors. Under these contracts, they share ex post in the outcomes of business transactions on
the basis of a pre-agreed ratio. Thus, Islamic banking departs completely from its interestbased
conventional counterpart where, in conventional banking, financiers are lenders deriving
rewards from taking purely financial risks and not real-sector risks. Islamic banks also offer
financing on the basis of sales or leasing contracts, known respectively as Murabahah and
Ijarah. Under these contracts, the banks specify instalment payments over a specified period of
the goods they sell or lease to clients after taking into consideration the costs and mark-up
profits. Thus, in Islamic banking, interest rate is completely absent and real-sector linkages are
foundational.
These features of Islamic banking serve as theoretical arguments for its relative stability as
compared to the conventional banking system. At the bank level, Khan (1987) demonstrates
theoretically the relative soundness of an Islamic bank as compared to a conventional bank.
According to Khan (1987), the real sector linkages of the Islamic bank immediately align its
assets and liabilities across business cycle phases. By contrast, the separation of assets and
liabilities under the conventional banking system makes it harder for the conventional bank to
recover its balance sheet during adverse shocks. Accordingly, the Islamic bank would be
relatively more stable. The absence of speculative and toxic financial products further firms up
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the resiliency of the Islamic banking system during financial shocks, as manifested during the
recent global financial crisis. Ariff (2014) further mentions inter-connectivity between the real
sector and financial sector, with the latter provides a supporting role for real activities, to be
central to economic stability and growth. In other words, the real-sector linkages of the Islamic
banking system fosters economic stability.
While few would argue against the relative stability of the (theoretical) Islamic banking
system, some raise doubts whether the Islamic banking is in practice markedly different from
the conventional banking on three grounds. These are (i) concentration on Murabahah
financing; (ii) pricing of Islamic banking products; and (iii) lack of truly distinct Shari’ah- based
products.
While portrayed to be a distinct feature of the Islamic banking business model, Mudarabah
and Musharakah financings make up only a small percentage of Islamic banking assets. Islamic
financing comprises predominantly Murabahah or sale-based financing, which is estimated to
be more than 80% since the early years of Islamic banking operations in the 1970s (Al-Harran,
1995; Chong and Liu, 2009; and Khan, 2010). Moreover, the pricing of especially Murabahahbased products is also an issue since Islamic banks resort to the interest rate benchmark. Finally,
some view Islamic products merely as imitation of prevailing conventional products but adapted
and modified to be Shari’ah compliant. As examples, as noted by Ariff (2014), murabahah
financing, bai’ bithaman ajil (BBA), al-ijarah thumma al- bai’ (AITAB) and tawarruq
munazzam offered by Islamic banks are mirror images of respectively conventional fixed-rate
home loans, conventional floating-rate home loans, hirepurchase, and conventional personal
loans. According to Ariff (2015), to be truly distinct, the Islamic banks must leap away from
Shari’ah-compliant mode to Shari’ah-based mode in the development of Islamic products.
The theory and practice divide in Islamic banking is at the centre of the debate whether the
adoption of the Islamic banking system can help stabilizing the prevailing fragile system. Based
on the Islamic banking practices, the skeptic view sees no differences in Islamic banks.
However, counter-arguments to the skeptic view are also available. The criticisms of the Islamic
banking practices notwithstanding, the Islamic banking financial products possess real sector
linkages and hence the real - financial decoupling is unlikely. In addition, Islamic banking
differentiates itself not only on the basis of the prohibition of interest rates but also on the
importance of the sanctity of contracts. The presence of Shari’ah board in addition to the
standard board of governance plays a crucial role in ensuring conformity to the principles of the
Shari’ah and hence curtailing Islamic banks’ excessive risk taking.
The debate on whether Islamic banking is distinguishable from conventional banking and
whether it is relatively more stable especially during the crisis episodes has spilled into an
empirical front, with available evidence ranging from no significant difference to some key
differences between them. Employing a non-parametric test, Bourkhis and Nabi (2013) find no
differences in bank capital, profitability, asset quality, costs and lending activities of Islamic and
conventional banks. Beck et al. (2013) and Alqahtani et al. (2016) find some differences
between Islamic and conventional banks, particularly in terms of intermediation ratio and cost
inefficiency. The recent study by Jawadi et al. (2016) also documents a few significant
differences between the two types of banks in terms of financial risk. Evaluating convergence
in performance of Islamic and conventional banks, Olson and Zoubi (2017) document evidence
indicating convergence of their profitability. Still, as they demonstrate, the convergence does
not apply to other financial ratios, namely net income margins and risk characteristics. Thus,
Islamic banks are different. In light of these findings, it seems that the issues remain open to
further empirical investigation.
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2.2 Hypotheses Development
In the aftermath of the recent global financial crisis, many studies have emerged to identify
bank characteristics that would limit the tendency of the banking system to amplify financial
shocks in a pro-cyclical manner. One interesting aspect that has been highlighted in the literature
is the importance of banks’ objectives and principles in constraining bank lending behaviour.
Some have argued and forwarded evidence that government-owned banks and cooperative
banks are more likely willing and able to maintain their lending when facing shocks. Unlike
commercial banks, these banks are not strictly profit-oriented. According to Chen et al. (2016),
government-owned banks have social-welfare agendas and their liabilities are implicitly
guaranteed. Accordingly, they would be more willing to continuously extend loans during crises
even if they may not be profitable and would be able to do so since they have liquidity strength.
Being stakeholders’ banks, cooperative banks are oriented towards long term relationships with
their members or clients and, consequently, are likely to behave in a less cyclical manner
(Merilainen, 2016).
Islamic banks are essentially commercial banks. However, while they are profit-oriented,
they may behave differently from the conventional commercial banks when facing adverse
shocks. The Shari’ah principles that these banks adhere to means that the social-welfare agendas
would assume priority during crunch times. The capital and asset strength of the Islamic banks,
as a result of the above-mentioned Shari’ah principles, may enable them to extend financing
even during crisis periods. Religion and profit-loss sharing arrangements are other potential
reasons that Islamic banks would be less pro-cyclical (Mollah et al., 2017). Given the alignment
between bank assets and liabilities as demonstrated by Khan (1987) and religious convictions
of Islamic bank depositors (Farooq and Zaheer, 2015), Islamic banks are less immune to deposit
withdrawals and hence are in a better position to maintain financing supply in the wake of
adverse shocks.
Several studies find support for higher financing or intermediation of Islamic banks during
the global financial crisis and economic downturns. Assessing the effects of the recent global
financial crisis on Islamic and conventional banks using a sample of 120 conventional and
Islamic banks from 8 countries, Hasan and Dridi (2011) find the credit growth of Islamic banks
to be higher. Beck et al. (2013) document higher intermediation ratio of Islamic banks as
compared to that of conventional banks and the difference is even more significant during local
crises. Alqahtani et al. (2016) further reaffirm that Islamic banks intermediate more. Finally,
according to Ibrahim (2016) in his analysis of Malaysia’s dual banking system, Islamic
financing is less pro-cyclical or even counter-cyclical as compared to conventional lending.
Despite these favourable findings, it remains uncertain whether Islamic banks can maintain their
credit supply in times of stress given the noted arguments that the Islamic banking is not
practically different from conventional banking, which is empirically supported by Bourkhis
and Nabi (2013). Due to their distinct features, Islamic banks face unique risks, e.g. Shariah
compliant risk, equity investment risk, rate of return risk, and displaced commercial risk, and
hence may be exposed to higher risk as compared to conventional banks (Mejia et al., 2014).
Kabir et al. (2015) offer empirical evidence suggesting that Islamic banks have higher nonperforming loans while Alandejani et al. (2017) indicate that Islamic banks have a higher
incidence rate of failure. These risks, coupled with the underdeveloped Islamic money markets,
may affect their function as financial intermediaries during the crisis period. In views of these,
we test the following hypotheses related to the financing growth of Islamic banks:
H01: There is no significant reduction in the financing growth of Islamic banks during the crisis
period.
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H02: There is no difference in the lending/financing growth of Islamic banks and conventional
banks during the crisis period.
Based on the above hypotheses, a strong case for the ability of Islamic banks to stabilize credit
can be made if they can sustain financing supply in times of stress, i.e. non-rejection of H01, and
the financing growth of Islamic banks is higher than the lending growth of conventional banks
during the period, i.e. rejection of H02.
The ability of Islamic banks to maintain financing supply during the crisis period depends
crucially on the stability of their deposits. However, whether Islamic bank deposits are resilient
to the crisis has not been much investigated. On one hand, the presence of displaced commercial
risk, which is unique to the Islamic banking sector, means that deposit withdrawals would be
highly likely in the face of adverse shocks (Khan and Ahmed, 2001; Mejia et al., 2014). On the
other hand, the “religious branding” and “asset-liability” alignments of Islamic banks would
mean that Islamic deposits would be less sensitive to shocks (Khan, 1987; Farooq and Zaheer,
2015).
In perhaps the only study on the resiliency of Islamic deposits during a financial panic.
Farooq and Zaheer (2015) document evidence supporting fewer deposit withdrawals from
Islamic banks as compared to conventional banks. Indeed, some Islamic banks even attracted
more deposits during the period. The findings, however, relate only to the case of Pakistan and,
as a result, there is a need to look at the subject from a wider perspective. The present study
draws a sample from 10 major dual-banking countries to evaluate deposit growth of Islamic and
conventional banks as stated in the following hypotheses.
H03: There is no significant reduction in the deposit growth of Islamic banks during the crisis
period.
H04: There is no difference in the deposit growth of Islamic banks and conventional banks during
the crisis period.
A non-rejection of H03 and rejection of H04 and findings that the deposit growth of Islamic
banks is higher would further strengthen the ability of Islamic banks to stabilize credit. However,
if the deposit growth of Islamic banks is more severely affected by the crisis, then there would
be a concern that the Islamic banks overstretch its credit expansion, a behaviour that may likely
breed future risk.
From the lens of finance - growth nexus, credit expansion is applauded as a driver for
economic growth. Moreover, continuous expansion of credit during crisis or recessionary
periods is viewed necessary for speedy economic recovery. However, the recent global financial
crisis serves as a reminder that overexpansion of credit can be disastrous. The rapid growth of
bank credit is likely to worsen moral hazard and adverse selection problems and, consequently,
increases the probability of a banking crisis (Schularick and Taylor, 2012). According to
Corsetti et al. (1999) and Goldstein (1998), credit growth in excess of economic growth
characterizes crisis-hit Asian countries during years leading up the Asian financial crisis in 1997.
Following the eruption of the global financial crisis, various studies have documented a
significant link between a banking crisis and rapid credit growth, see Davis et al., (2016) and
references therein. Schularick and Taylor (2012), for instance, estimate the marginal effect of a
1 percentage point increase in the credit to GDP ratio to be 0.3 percentage point increase in the
probability of the crisis. At the individual bank level, Foos et al. (2010) indicate the importance
of loan growth in driving bank riskiness through higher loan loss provisions, lower risk-adjusted
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interest income and lower capital ratios.
While these studies link the crises to overexpansion of credit during good times prior to
crisis occurrence, Chen et al. (2016) hypothesize potential link between credit growth and bank
performance during the global financial crisis. They focus on whether government banks have
higher loan growth rates than private banks during the global financial crisis and how the higher
loan growth by government banks is associated with their performance. Using bank-level data
from 56 countries, they find significantly higher loan growth rates of government banks during
the crisis period. Whether their higher loan growth rates impact positively or negatively on bank
performance, however, depends on the level of corruption of the countries under study. More
precisely, the bright side of government is uncovered only in the countries with low corruption
level in that government banks are able to stabilize credit to speed up economic recovery without
sacrificing performance. In high corruption countries, it seems that lending decisions by
government banks are distorted, resulting in underperformance relative to private banks.
In addition, facing heightened credit risk, banks may take risk by lending in excess of the
optimal level. As suggested by Jensen and Meckling (1976), a managerial rent-seeking and a
conflict of interest between shareholders and creditors may encourage managers or shareholders
to make risky loans and shift the risk to depositors. This behaviour is highly likely when banks
are engulfed with immense credit problems (Bernanke and Gertler, 1986; Koudstaal and
Wijnbergen, 2012). In addition, during the crisis episode, the adverse selection problem is likely
at work, where firms that are highly risky are the ones that actively seek financing and secure
financing from banks. In a recent paper, Zhang et al. (2016) demonstrate that the lending
decisions by banks do exhibit moral hazard in their examination of the Chinese commercial
banking system.
In the context of a dual banking system, the risk implications of Islamic financing has not
been closely scrutinized. This is surprising given the rapidly growing Islamic financing, which
has been far exceeding the growth of conventional loans. Moreover, by expanding financing
during a period when asymmetric information is surmountable, Islamic banks may expose
themselves to excessive risk. This means that the higher expansion of financing during the crisis
period by Islamic banks as compared to conventional banks, if it is true, must be assessed. On
the basis of the literature reviewed above, we state the following hypotheses:
H05: There is no effect of financing/lending growth of Islamic and conventional banks on future
bank risk.
H06: There is no significant difference in the effect of financing/lending growth of Islamic and
conventional banks on future bank risk.
H07: Financing growth of Islamic banks during the crisis period is not related to excessive risk.
We test these hypotheses to offer a more concrete answer to whether the Islamic banking sector
plays a stabilizing role during the crisis period. Hypothesis 5 and hypothesis 6 are to establish
the link between lending/financing growth and bank risk. Meanwhile, Hypothesis 7 is to test
specifically whether Islamic banks behave in a morally hazardous manner in their expansion of
credit during the crisis.
3. MODEL SPECIFICATIONS
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3.1 Lending and Deposit Growth
To evaluate whether Islamic banks are able to sustain loan (deposit) growth and have higher
loan growth (deposit growth) than conventional banks especially during the global financial
crisis, i.e. Hypothesis 1 to Hypothesis 4, we specify the following model for loan growth
(deposit growth):
ALft(ADft) =
+ PiIBit + faGFCt + ^3(lBtt
+ sit (1)
Po
x GFCt~)
+ $Controlit
+ gf
where AL is the growth rate of gross loans/financing (AD is the growth rate of deposit), IB is the
Islamic bank dummy, GFC is the global financial crisis dummy, Control is a vector of controlled
variables comprising bank-specific, macroeconomic and regulation variables, pi- is a bankspecific effect, and st is the standard error term. In line with existing literature, the bank-specific
variables include bank size, capitalization, liquidity, profitability, funding ratio, cost efficiency
and credit risk. These variables are lagged once to address the endogeneity issue. As for the
macroeconomic variables, we include economic growth, inflation and financial development.
Finally, as for the regulation, we control for activity restrictions, capital requirement,
supervisory power and private monitoring2.
Since GFC is key in our analysis, its definition requires deliberation. While the span of recent
US recession identified by the National Bureau of Economic Research (NBER) is December
2007 to June 2009, some view the financial crisis to start much earlier (Iley and Lewis, 2013).
Indeed, in the literature, the global financial crisis has been referred as the 20072009 financial
crisis (Akbar et al., 2017; Flannery et al., 2013) as well as the 2008-2009 financial crisis
(Coleman and Feler, 2015; Cull and Martinez-Peria, 2013; and Feldkircher, 2014). According
to Berglof et al. (2009), the effects of the crisis that started mid-2007 was confined mainly to
advanced economies during the first year of the crisis. In a similar vein, Mishkin (2011) relates
the first phase of the crisis from August 2007 to August 2008 to the losses in one relatively small
segment of the US financial market before it turned global. This means that, for the analysis of
countries other than the US and advanced economies, it is more appropriate to designate 20082009 as the crisis years. Accordingly, GFC takes the value of 1 for years 2008-2009 and 0
otherwise, in line with various studies comparing Islamic and conventional banks (Abedifar et
al., 2013; Louchini and Boujelbene, 2016; Olson and Zoubi, 2017)3.
The key parameters of the model are P1,P2, and P3. To clarify their interpretation, Table 1
summarizes the specification of (1) conditional on whether the bank is Islamic or conventional
and whether the period is a normal or a crisis period (error terms omitted). It is clear from Table
1 that fi2 captures the difference of expected lending (deposit) growth by conventional banks
during the crisis period as compared to the normal period, all else equal. If it is negative and
significant, we may conclude that the expected lending (deposit) growth of conventional banks
We also consider the oil price as a controlled variable given that most of the countries in our sample are oil-rich
economies, as suggested by a referee. Following Alqahtani et al. (2016), we include the natural logarithm of the
annual average oil price in all regressions that we perform. While Alqahtani et a (2016) find strong evidence for
the significant effects of oil price on bank performance, the oil price is significant in less than half of the regressions
that we perform. This is not surprising given that the countries in our sample are not confined only to oil-rich
countries. More importantly, the main conclusions that we make are not materially affected by the inclusion of oil
price. These results are not reported but are available upon request.
Defining the global financial crisis years to be 2007-2009 as in Kabir et al. (2015) leads to insignificant effect of
the crisis on bank behaviour, which may be due to considering 2007 to be the beginning of the crisis year when,
as noted in the text, the effect of the crisis was confined mainly to the advanced economies during 2007.
2
3
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drops during the crisis period. Meanwhile, fi2 + fl3 provides a corresponding figure for Islamic
banks and hence serves a basis for testing hypothesis 1 and hypothesis 3. The comparison
between Islamic banks and conventional banks is given by fl1 and fl1 + ^3, respectively for
normal time and crisis time. The significance of these coefficients would mean the financing
(deposit) growth of Islamic banks is different from that of conventional banks during
respectively normal and crisis times, the latter of which pertains to hypothesis 2 and hypothesis
4.
Specification (1) considers years surrounding the global financial crisis to be normal years.
One may argue that the post-crisis period is different from the pre-crisis period. The global
economy has witnessed a much volatile oil market during the post global financial crisis. This
might have affected bank performance and behaviour as the dual-banking countries under study
are mostly oil-rich countries. In addition, the global economy is still wary of the consequences
of the European sovereign crisis that followed the global financial crisis. Accordingly, for
robustness, we modify (1) by allowing the lending (deposit) growth to be potentially different
during the pre-crisis, crisis, and post-crisis periods as:
ALft(ADft) = fa + PiIBit + faGFCt + p3(lBit x GFCt) + 04PFCt +
P5(IBit x PFCt) + $Controlit + ^ + sit
(2)
where PFC is the post financial crisis dummy variable taking the value of 1 after 2009 and 0
otherwise.
In the empirical implementation, we estimate equations (1) and (2) using a combined
sample of conventional and Islamic banks as well as separately for the two types of banks.
Obviously, estimating the equations separately for conventional banks and Islamic banks means
that the Islamic bank dummy and Islamic bank - crisis interaction are dropped from the
equations. In this case, the coefficient of the GFC captures the lending/financing (deposit)
growth during the crisis period as compared to its growth during normal time or pre-crisis period
for the respective types of banks. If it is negative, we can infer that the lending/financing
(deposit) growth drops during the crisis period. The results from these separate regressions
would serve as a confirmatory evidence on the relative credit (deposit) growth of Islamic banks
vis-a-vis conventional banks.
Take note that we opt for static panel specifications of the loan and deposit growth
equations. While some studies have allowed dynamics in the lending/deposit growth, our static
specification follows Bhaumik et al. (2011), Cull and Martinez-Peria (2013), Kosak et al.
(2015) and others. Our choice is justifiable on the basis that we find no persistence in the lending
or deposit growth, i.e. the lagged dependent variables incorporated in (1) and (2) carry
insignificant coefficients4. Moreover, since the loan level normally exhibits a unit root property,
we have no reason to believe that its first difference would be persistent. We estimate the
equations using the random-effect panel estimator with robust standard errors clustered at the
bank level. With the presence of the Islamic bank dummy, the fixed-effect panel estimator would
not be appropriate. Still, we experiment with the fixed-effect panel estimator when we estimate
the equations separately for conventional and Islamic banks. Since the results are largely similar,
they are not reported to conserve space.
4
We estimate the dynamic specifications of (1) and (2) using the system GMM estimator.
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3.2 Bank Risk
Next, we intend to assess whether lending growth is associated with future risk for both
Islamic banks and conventional banks and whether there is difference between the two, i.e.
Hypothesis 5 and Hypothesis 6. Finally, observing higher financing growth of Islamic banks
during the crisis, we examine whether it is related to excessive risk, i.e. Hypothesis 7.
To test hypotheses 5 and 6, we follow the dynamic specification as in Foos et al. (2010)
but with some adjustments to fit our research objective, written as:
NPLit =Po+ PiNPLit-i + 27=1 faj^it-j + $Controlit + gf + eit
m
(3)
m
NPLit = fa + faNPLit—i + £ P2ij^Lit—j I(IBt = 0) + £ &2jALit-j I(IBt = 1) j=1
+$Controlit + ^i+ eit
j=1
(4)
where NPL is the ratio of non-performing loans to gross loans, I(.) is a bank type indicator and
it equals 1 when the argument is true, and other variables are as defined before. In line with Foos
et al. (2010), we expect credit growth to affect bank credit risk or non-performing loans with
time lags since it is unlikely that borrowers will default immediately in the first year of the loans
they receive. Equation (3) restricts risk - loan growth relations to be the same for conventional
banks and Islamic banks while equation (4) relaxes the restriction. Based on (3) and (4), we test
the null hypotheses (i) 'E'lj=1faj = 0, (ii) 2jj=i P21j = 0 and (iii) 2'lj=iP22j = 0 that there is no future
risk implications of financing/lending growth respectively for all banks, conventional banks and
Islamic banks. In addition, we also test whether there is significant difference in the risk - credit
growth relations of conventional and Islamic banks, i.e. 2j=i faj = 27jl=1P22j. We include a larger
set of controlled variables than the one used by Foos et al. (2010). These include bank-specific
variables (bank size, capitalization, liquidity), macroeconomic variables (GDP growth,
inflation, financial development, and crisis dummy), and regulation (activity restrictions, private
monitoring, supervisory power, and capital stringency).
For Hypothesis 7, i.e. financing growth of Islamic banks during the crisis period is not
related to excessive risk, we focus specifically on Islamic banks. Taking insight from Zhang et
al. (2016) and Vithessonthi (2016), we specify the model as:
m
NPLit
m
= Yo + YiNPLit-i + £ Y2jMit-j + £ Y3j(Mit-j X GFCt—j)
=1
+®Controlit + ^ + Eit
=1
(5).
In line with Zhang et al. (2016), we can infer from the link between loan growth and nonperforming loans whether Islamic financing growth during the crisis constitutes excessive risk
taking. As we have noted, asymmetric information abounds in times of crisis. Prudential banks
would be more cautious in their lending through, for example, more stringent lending standards.
This, according to Vithessonthi (2016), would weaken the link between credit growth and credit
risk. If we accept this argument, we may also state that the credit growth - risk relation would
be stronger if banks take excessive risk in times of crisis. Moreover, in the presence of
asymmetric information and resulting adverse selection problem, lending growth during the
crisis period may likely be granted to low quality borrowers, since they are the ones actively
seeking loans, or to those rejected by other banks. As a result, loan growth would be translated
to higher risk in the future. On the basis of these arguments, we may infer whether Islamic banks
ACCEPTED MANUSCRIPT
behave prudentially or undertake excessive risk from the significance and sign of Yl>jl=1 Y3j. If l5
6 7 8jl=1 Y3j is significantly positive, then financing growth of Islamic banks during the crisis is
construed as excessive risk taking.
The dynamic specification of risk equations (3) to (5) renders the traditional panel
estimators inappropriate. Even we can assume exogeneity of all explanatory variables, the
lagged dependent variable is correlated with the bank-specific effect by construction. The
standard and widely-adopted approach in dealing with this endogeneity issue is to apply the
GMM estimators developed by Arellano and Bond (1991), Arellano and Bover (1995) and
Blundell and Bond (1988). In line with the literature, we utilize the GMM estimators for our
purpose. We also take note that the GMM estimators may suffer from instrument proliferation.
In addition, they have poor finite sample properties and hence are less suitable when the number
of cross-sectional unit is small (Meschi and Vivarelli, 2009). Since the estimation of equation
(5) involves only 25 Islamic banks, we follow Roodman(2009) by limiting the number of
instruments such that it is less than the number of cross-section units. We also apply the biasedcorrected LSDV (LSDVC) as proposed by Kiviet (1995), Judson and Owen (1999), Bun and
Kiviet (1995) and Bruno (2005) to deal with the small micro panel of Islamic banks.
included in the analysis. This leads the exclusion of Yemen due to the unavailability of
regulation variables. By applying these sample selection criteria, we arrive at a sample of 114
conventional banks and 25 Islamic banks from 10 countries. These countries are Bangladesh,
Bahrain, Indonesia, Jordan, Kuwait, Malaysia, Qatar, Saudi Arabia, Turkey and the United Arab
Emirates. The data are unbalanced covering the period 2000 to 2014. We admit that other studies
tend to have larger sample size. However, their sample selection criteria are less stringent. For
instance, in their analysis of the 6 GCC countries, Alqahtani et al. (2016) requires that, to be
included in the sample, a bank must have observations on all variables for at least one year.
Meanwhile, Kabir et al. (2015) requires data availability of a least 3 consecutive years. Still, it
should be noted that a small sample than ours is not uncommon (Bourkhis and Nabi, 2013).
Louchini and Boujelbene (2017) employ unbalanced data covering 2005-2012 from the MENA
and South-East Asian countries. Despite shorter time span, they end up with only 117 banks in
their sample. The bank-level data are sourced from the Bankscope while macroeconomic and
regulation variables are from respectively World Development Indicators Database and Barth
et al. (2006).
Table 2 provides definition and descriptive statistics of the variables used in the present
study. On average, the Islamic banking sector has higher financing and deposit growth rates and,
at the same time, higher credit risk. In line with Beck et al. (2013), Islamic banks are on average
5 DATA AND ESTIMATION RESULTS
4.1 Data Descriptions
We gather bank-level data for dual-banking countries that have significant presence of the
Islamic banking sector or exhibit fast-growing Islamic banking in recent years. Our initial
sample comprises 296 banks (225 conventional banks and 71 Islamic banks) from 13 countries.
Since our objectives are to assess lending growth, deposit growth, and risk of Islamic banks
during the global financial crisis not only relative to those of conventional banks but also relative
to those during the normal or pre-crisis period, we require a sufficient number of observations
in the pre-crisis period such that meaningful comparison can be made. Accordingly, our sample
includes countries that have both conventional and Islamic banks having relevant bank-level
data available starting no later than 2005. This criterion filters out all banks from Egypt and
Pakistan since Islamic banking data are available only after 2005. In addition, we also require
data availability of the macroeconomic and regulation variables
ACCEPTED MANUSCRIPT
better capitalized but less cost efficient. As compared to conventional banks, Islamic banks are
less liquid and rely more on customer deposits. While Islamic banks are on average smaller,
they are more profitable. As far as the macroeconomic environments of these countries are
concerned, these countries record respectable growth, averaging over 5% over the sample
period. Indeed, the average annual growth exceeds 4% for all countries in the sample. However,
there are large variations in the inflationary experience across these countries. As reflected by
the ratio of credit to the private sector to GDP, the financial sector is not well developed. The
average credit-to-GDP ratio is 56.6%. Five countries, i.e. Bangladesh, Indonesia, Qatar, Saudi
Arabia, and the United Arab Emirates, have average credit-to-GDP ratio below 50% and only
Malaysia records the ratio above 100%. Finally, except supervisory power, the regulation
indices in these countries are on average slightly above the mid-level. We also compute the
correlation coefficients among the variables and find no indication of multicollinearity problem.
All correlations are below 0.60, among which only two are above 0.509.
4.2 Lending Growth
This section presents the estimation results for lending/financing growth to ascertain
whether Islamic banks are able to sustain financing supply and whether there is significant
difference between Islamic banks and conventional banks in the provision of credit during the
crisis period. These results are reported in Table 3. The key coefficients are the coefficients of
the Islamic bank dummy (IB), the crisis dummy (GFC) and the Islamic bank - crisis interactions
(IB * GFC), whose interpretation is explained earlier (see Table 1). These are presented in Panel
(a) of the Table. In the same panel, we provide the p-values for testing the (i) null hypothesis
that there is no significant difference in the financing growth of Islamic banks and the lending
growth of conventional banks during the crisis (i.e. P1 + P3 = 0), and (ii) null hypothesis that
there is no significant difference in the financing growth of Islamic banks during the non-crisis
and crisis periods (i.e. P2 + P3 = 0), which are the focus of the present study. Panel (b) of the
Table presents the coefficient estimates of the controlled variables. Regressions (1) and (2) refer
to the estimation of equation (1) while regressions (3) and (4) the estimation of equation (2).
The insignificance of the IB dummy in all regressions suggests no significant difference in
the lending/financing growth of Islamic banks and conventional banks during the normal period.
However, during the crisis period, the lending growth of conventional banks drops significantly
as reflected by the significant and negative coefficients of the crisis dummy. As compared to
the non-crisis years, i.e. regressions (1) and (2), the lending growth of conventional banks are
lower by roughly 4 to 5 percentage points. But, when we compare to the pre-crisis years, i.e.
regressions (3) and (4), the drop is roughly 8 percentage point. By contrast, the results indicate
no significant reduction in the financing growth of Islamic banks. That is, we fail to reject the
null hypothesis P2 + P3 = 0 in all regressions. This means that Islamic banks are able to sustain
their supply of financing during the crisis. Finally, as should be expected from the above results,
we find financing growth of Islamic banks to be significantly higher than the lending growth of
conventional banks during the crisis period. The null hypothesis P1 + P3 = 0 is rejected in all
regressions.
To reaffirm our findings, we re-estimate equations (1) and (2) separately for conventional
banks and Islamic banks10. The results, as reported in Table 4, echo well the conclusions made
The correlation coefficients are not reported to conserve space. They are available from the authors upon
request.
Estimating the equations separately for conventional banks and Islamic banks relaxes the assumption of common
coefficients across the two types of banks.
9
10
ACCEPTED MANUSCRIPT
earlier. As may be noted from the Table, the crisis dummy carries a negative and significant
coefficient for conventional banks while it is not distinguishable from zero for Islamic banks.
Consistent with the results in Table 3, the lending growth of conventional banks drops by
roughly 5 to 8 percentage points during the crisis period. Our finding that Islamic banks maintain
their financing during the crisis is in line with earlier results by Hasan and Dridi (2011), Beck
et al. (2013), and Ibrahim (2016).
Turning to bank-specific controlled variables, we find bank size, liquidity, profitability,
funding ratio, credit risk, and cost efficiency to be significant. Larger banks tend to have lower
lending growth, regardless of whether the banks are conventional or Islamic. Likewise, Islamic
and conventional banks with higher credit risk have lower credit growth. While we find the
effect of bank liquidity, profitability, funding ratio and cost efficiency to be positive and
significant in a combined sample of conventional and Islamic banks (Table 3), they seem to
have differential effects on the lending growth of conventional and Islamic banks (Table 4).
More precisely, bank profitability and funding ratio significantly affect the lending growth of
only conventional banks while liquidity and cost efficiency are significant in explaining the
financing growth of Islamic banks.
As regards macroeconomic and regulatory variables, the results generally indicate their
significance in explaining credit growth of Islamic and conventional banks. Both GDP growth
and inflation affect lending growth of these banks positively. By contrast, financial development
tends to slow down credit growth of especially conventional banks. We also document
differential effects of regulatory variables on lending growth. While activity restrictions and
capital stringency tend to depress lending growth of conventional banks, private monitoring and
supervisory power tend to stimulate their credit growth. As for Islamic banks, the supervisory
power significantly contributes to their financing growth. Apart from these results, we also find
a slowdown in credit growth of conventional banks after the financial crisis. By contrast, Islamic
banks in the sample maintain their financing supply post-crisis period, as reflected by the
insignificant coefficient of the post-crisis dummy (Table 4).
4.3 Deposit Growth
As in the case of lending growth, we estimate equations (1) and (2) for deposit growth using
a combined sample as well as separately for Islamic banks and conventional banks. The results
are reported in Table 5 and Table 6.
Focusing on our main theme, we find the results quite puzzling. Using the combined
sample, we find no significant difference in the deposit growth of Islamic banks and
conventional banks during the global financial crisis as reflected by the non-rejection of fl1 + P3
= 0. Against this finding, it is puzzling to observe significant reduction in the deposit growth of
conventional banks during the crisis as suggested by the significance of the crisis dummy and
yet insignificant drop in the deposit growth of Islamic banks as indicated by nonrejection of the
null P2 + P3 = 0. Then, when we estimate the equations separately for conventional banks and
Islamic banks, we find significant reduction in deposit growth of both types of banks during the
crisis period albeit at only 10% significance level for Islamic banks. From these results, we are
unable to draw conclusion that the financing growth of Islamic banks during the crisis is
supported by a concomitant growth in their deposits.
Perhaps, Islamic banks draw down their liquidity holdings to maintain their financing during
the crisis episode. We observe that, during the pre-crisis period, their liquid asset-to- asset ratio
was 25.9%. During the crisis period, it dropped to 19.8%. At the same time, observing the
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increase in capitalization from 13.4% to 14.5% over the periods, we contend that Islamic banks
exhibit prudential behaviour by building capital amidst higher financing growth. While these
might be the answer for higher financing growth of Islamic banks during the crisis, we leave
them for future research. Here, we examine next whether financing growth of Islamic banks
during the crisis period is linked to their future credit risk and to excessive risk taking.
As for the controlled variables, we find consistent findings for negative and significant
effects of bank size and credit risk on deposit growth of both Islamic banks and conventional
banks. Given that larger banks tend to have better access to alternative sources of funds, they
are less likely to depend on bank deposits as a source of funds or they are less aggressive in
sourcing funds from deposits. This result is not in line with Farooq and Zaheer (2015), who
document insignificant relation between deposit growth and bank size for Pakistan. As regards
bank credit risk, our result conforms to the finding by Farooq and Zaheer (2015) that banks with
higher credit rating experience higher deposit growth. For other bank-specific variables, we find
bank liquidity and funding to be significant only for conventional banks, suggesting that banks
that are more liquid and have higher level of funding deposits tend to have lower deposit growth.
Finally, it is very interesting to observe that Islamic bank deposits are not responsive to business
cycle, macroeconomic uncertainty and the level of financial development. These results may
reflect the notion of “captive customers” in Islamic banking (Azzam and Rettab, 2013). By
contrast, deposit growth in conventional bank is influenced by macroeconomic cycle and
financial development.
4.4 Credit Risk
We first estimate equation (3) by setting the lag order of the credit/financing growth to 311.
The first-difference and system GMM results, reported in Table 7, provide evidence that there
are lagged effects of credit growth on credit risk. More precisely, credit growth bears significant
risk implications after two years, as reflected by significant coefficients of credit growth lagged
two and three years. This finding conforms well to Foos et al. (2010). As regards the controlled
variables, capitalization, liquidity, economic growth and inflation appear significantly and
negatively related to credit risk while the remaining controlled variables are not significant.
Thus, the better-capitalized and more liquid banks are less risky. Moreover, economic growth
tends to improve credit risk. Finally, under an inflationary environment, banks are likely to be
more cautious and accordingly have better credit risk.
Having verified that credit growth anticipates future risk, we proceed to assessing whether
the credit growth - risk relations are the same for Islamic banks and conventional banks as
specified in (4) as a further check of the link between credit growth and future risk. The results
from the GMM estimators are given in Table 8. The credit growth - risk relations of conventional
banks mimic closely those reported in Table 7. As for Islamic banks, only financing growth
lagged two years is statistically significant. When we test the null hypothesis 27=1^21/ = 27= 1 P22j,
however, we fail to find significant difference between the credit growth - risk relations of
conventional and Islamic banks. As for the controlled variables, the results are in conformity to
those documented previously.
The key result that we have established here is that the financing growth of Islamic banks
does in general lead to future risk. The next question is: Does financing growth of Islamic banks
during the crisis, which we find earlier to be higher than the credit growth of conventional banks
We also experiment with the lag order of 4 as in Foos et al. (2010) but find credit/financing growth lagged 4 to
be insignificant.
11
ACCEPTED MANUSCRIPT
and which we observe to be indistinguishable from the level recorded prior to the crisis, amounts
to excessive risk taking? To this end, we estimate equation (5) using a sample of only Islamic
banks. The results using the system GMM and bias-corrected LSDV estimators are provided in
Table 912. The lag order of financing growth is set to 2, in line with the finding we document in
Table 8 that only financing growth lagged two years is significantly different from zero.
Regressions (1) and (2) of Table 9 relook at the link between credit risk and past credit
growth of Islamic banks. As in the combined sample, the coefficient of financing growth lagged
two years is positive and statistically significant. The results of estimating equation (5) using
the system GMM are given in regressions (3) and (4). While we find financing growth to be
related to future risk, we find no evidence that the crisis makes Islamic banks to be more prudent
or to be riskier. As reflected by the insignificance of the credit growth - crisis interactions,
financing growth during the crisis does not affect risk differentially beyond its documented
effect during the normal period. In other words, there is no evidence of excessive risk taking by
Islamic banks. Using the biased-corrected LSDV, we find that the once-lagged financing growth
turns significant. Similar to the system GMM estimation results, the financing growth - crisis
interactions are statistically insignificant. Thus, our conclusion that Islamic financing during the
crisis is not the result of excessive risk-taking stands. Among the controlled variables, only
capitalization remains robust. Indeed, the negative coefficient of capital suggests that capital
adequacy requirements would serve as a risk-mitigating mechanism. In addition, we also note
the significance of private monitoring and supervisory power in respectively reducing and
increasing bank risk.
These results strengthen the ability of Islamic banks to play a stabilizing role during the
crisis period. Facing the crisis, Islamic banks sustain their financing supply and have higher
financing growth as compared to conventional banks. While we are not able to verify whether
there is a parallel increase in Islamic deposits, we find no evidence that Islamic banks exhibit
excessive risk taking in their financing expansion during the crisis.
5. CONCLUSION
Whether a nascent but fast-growing Islamic banking sector has the ability to be a stabilizing
force in times of crisis is a subject that has attracted substantial research attention especially
since the global financial crisis. We contribute to this area of research by analysing lending
growth, deposit growth and risk of Islamic and conventional banks covering 10 major dualbanking countries from 2000 to 2014. Our main aim is to empirically evaluate whether financing
and deposit growth of Islamic banks is resilient to the crisis and whether the Islamic financing
growth during the crisis is related to future risk and can be construed as excessive risk-taking.
The evaluation is made in comparison not only to normal years but also to conventional banks.
From the analysis, we observe no significant reduction in Islamic financing growth during
the crisis period. In addition, there is strong evidence that the growth of Islamic financing is
higher than the growth of conventional lending during the period. However, we are unable to
draw conclusion that the financing growth of Islamic banks during the crisis is supported by a
concomitant growth in their deposits. Finally, while we find evidence that financing/lending
growth of both Islamic and conventional banks is related to future risk, we find no evidence to
suggest that the sustained and higher financing growth of Islamic banks during the crisis period
The first-difference GMM estimator and using longer lags yield similar results. These results are not reported to
conserve space.
12
ACCEPTED MANUSCRIPT
is related to excessive risk taking. That is, there is no increased financing growth - risk relations
beyond their observed relation during the normal period. The implication from these findings is
straightforward. Given that the Islamic banking system has the ability to contribute positively
to financial and economic stability, its development should be further bolstered. Still, safeguard
measures are needed to be in place even for the Islamic banking sector. Although we find no
evidence that Islamic banks behave in a morally hazardous manner, they do face risk from their
intermediation activities. Particularly, encouraging the build-up of bank capital should be
facilitated as it would constrain Islamic bank risk.
Having stated these, we believe that the ability of the Islamic banking system to inject the
much needed financial stability requires further investigation. In the present study, focusing
specifically on the effects of the global financial crisis on Islamic and conventional banks, we
define the crisis period to be common for all countries in the sample. The documented better
performance of Islamic banks may be due to the fact that they are less integrated and less
exposed to developments in the global financial market as compared to the longer-established
conventional banks and not due to the Islamic principles underlying their operations. Thus,
extending the present analysis by assessing the implications of country-level indicators of
financial crises as in Doumpos et al. (2015) on bank performance in a dual banking system
would provide a firmer evidence for the stabilizing role of the Islamic banking system. In
addition, our analysis on the moral hazard behaviour of Islamic banks relies exclusively on the
strengthened link between bank financing and bank risk. To be more concrete, this must be
further evaluated and extended to whether Islamic banks really share risk or they, as
conventional banks do, shift risk. Finally, a more focus and thorough analysis on the
determinants of Islamic bank risk is required since, by the very nature of banking, Islamic banks
do face risk. The fact that there are risks unique only to Islamic banks such as the Shariahcompliant risk or displaced business risk, the issue of risk determinants requires deeper
evaluation.
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ACCEPTED MANUSCRIPT
Table 1: Conditional Models
Bank/Period
Conventional
Islamic
Normal
Crisis
00 + 0Controlit
(0o + 02) + $Controlit
(£0 + 0i) + $Controlit
(0o + 0i + 02 + 03~) + $Controlit
Table 2: Variable Definition and Descriptions
(a) Bank-specific Variables
Variable
Loan Growth
Deposit
Growth
Credit Risk
Capitalization
Liquidity
Size
Profit
Funding
Definition
Logarithmic difference of gross
loans, AL
Logarithmic
difference
of
customer deposits, AD
Ratio of non-performing loans to
gross loans, NPL
Equity to asset ratio, EQA
Ratio of liquid assets to total
assets, LIQA
Natural logarithm of total assets,
Ln(TA)
Return on average assets, ROAA
Ratio of customer deposits to
total liabilities, FUNDING Cost to
income ratio, CIR
All Banks
Mean Std D
0.156 0.201
IBs
CBs
Mean Std D Mean
J0.182 0.210 0.151
Std D
0.199
0.142
0.246
0.183
0.435 0.34
0.186
6.792
10.53
7.242
10.24 6.918
10.60
12.61
24.87
7.815
15.70
13.03
21.85
8.434 12.53
11.11 25.48
7.686
16.40
15.17
1.612
15.01
1.419 15.20
1.650
1.709
67.32
2.072
16.07
1.797
71.16
2.890 1.691
18.07 66.56
1.867
15.54
Ratio Cost
46.16 33.14
51.34 48.20 45.13 29.13
Efficiency
(b) Macroeconomic and Regulatory Variables
Variable
Definition
Mean
Std Dev
Economic
Growth rate of real gross domestic product, AGDP
5.400
3.674
Growth
Inflation
CPI inflation rate, INF
5.933
7.506
Ratio of credit to the private sector to GDP, FMD
56.573
31.642
Financial
Development
Activity
The degree to which banks are restricted or permitted 2.567
0.671
Restrictions to activities related to securities, insurance and real
estate markets and to ownership and control of nonfinancial firms, AR
Private
5.778
1.648
Monitoring
The degree to which regulations facilitate incentives
and ability for private monitoring of banks, PM
Supervisory
11.044
2.490
Power
The degree to which the country’s bank supervisory
agency has the power to take specific actions, SUP
The degree of capital stringency regulation, CR
4.806
1.774
Capital
Stringency
ACCEPTED MANUSCRIPT
Table 3: Bank Lending Growth - All Banks
Independent Variables
Regressions
(1)
(a) Key Variables
IB
GFC
IB x GFC
(2)
(3)
(4)
0.941
(0.504)
-3.909***
(0.002)
4.366
(0.176)
0.410
(0.776)
-5.443***
(0.000)
4.589
(0.136)
-0.958
(0.661)
-7.927***
(0.000)
6.339*
(0.060)
-1.623
(0.463)
-7.805***
(0.000)
6.642**
(0.044)
0.0488
0.8762
0.0511
0.7645
0.0446
0.6093
0.0536
0.7088
-1.456***
(0.000)
-0.037
(0.778)
0.139***
(0.003)
1.844***
(0.000)
0.101***
(0.008)
-0.466***
(0.000)
0.105***
(0.000)
0.639***
(0.000)
0.410***
(0.001)
-0.069***
(0.001)
-1.824***
(0.000)
-0.026
(0.850)
0.091*
(0.065)
1.532***
(0.000)
0.112***
(0.004)
-0.457***
(0.000)
0.095***
(0.000)
0.530***
(0.000)
0.304**
(0.013)
-0.071***
(0.001)
-2.672***
(0.004)
0.698
(0.108)
0.458**
(0.040)
-0.693**
(0.016)
-0.931***
(0.010)
0.114
(0.412)
0.082*
(0.089)
1.465***
(0.000)
0.114***
(0.002)
-0.513***
(0.000)
0.102***
(0.000)
0.568***
(0.000)
0.399***
(0.001)
-0.057***
(0.004)
-1.405***
(0.000)
0.083
(0.561)
0.062
(0.223)
1.358***
(0.001)
0.121***
(0.001)
-0.490***
(0.000)
0.095***
(0.000)
0.526***
(0.000)
0.327***
(0.008)
-0.064***
(0.002)
-2.486***
(0.007)
0.279
(0.511)
0.349
(0.107)
-0.264
(0.374)
-6.002***
(0.000)
4.434
(0.116)
27.589***
(0.001)
1766
139
0.215
Tests (p-values)
Pl+P3 = 0
p2+p3 = o
(b) Controlled Variables
Ln(TA)t-1
EQAt-1
LIQAt-1
ROAAt-1
FUNDINGt-1
NPLt-1
CIRt-1
AGDP
INF
FMD
AR
PM
SUP
CR
PFC
IB x PFC
Constant
Observations
Number of Banks
R2
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
21.722***
(0.003)
1766
139
0.187
31.424***
(0.000)
1766
139
0.207
-7.654***
(0.000)
4.109
(0.143)
16.942**
(0.021)
1766
139
0.207
ACCEPTED MANUSCRIPT
Table 4: Bank Lending Growth - Separate Samples
Independent
__
Conventional Banks
Variables
(1)
(2)
(a) Key Variables
-5.495***
-7.874***
GFC
(0.000)
(0.000)
-1.864***
(0.000)
-0.109
(0.390)
0.082
(0.108)
1.634**
(0.012)
0.116***
(0.004)
-0.421***
(0.000)
0.061
(0.142)
0.540***
(0.000)
0.331**
(0.026)
-0.083***
(0.001)
-3.263***
(0.002)
0.770*
(0.078)
0.370*
(0.084)
-0.799**
(0.017)
-1.392***
(0.000)
0.017
(0.899)
0.045
(0.401)
1.395**
(0.033)
0.122***
(0.002)
-0.455***
(0.000)
0.059
(0.146)
0.532***
(0.000)
0.366**
(0.013)
-0.074***
(0.001)
-3.039***
(0.003)
0.317
(0.465)
0.229
(0.285)
-0.272
(0.431)
-6.098***
(0.000)
32.807***
(0.000)
1466
114
0.218
Islamic Banks
(3)
(4)
0.551
(0.804)
0.506
(0.863)
-2.992**
(0.022)
0.467
(0.228)
0.344***
(0.003)
0.551
(0.344)
0.041
(0.684)
-0.799***
(0.000)
0.112***
(0.002)
0.660*
(0.067)
0.345*
(0.085)
0.032
(0.501)
-0.662
(0.669)
-1.384
(0.290)
2.293***
(0.001)
0.489
(0.530)
-2.978**
(0.014)
0.468
(0.234)
0.344***
(0.005)
0.546
(0.353)
0.041
(0.687)
-0.800***
(0.000)
0.112***
(0.002)
0.661*
(0.067)
0.346*
(0.080)
0.032
(0.504)
-0.652
(0.680)
-1.392
(0.295)
2.292***
(0.001)
0.496
(0.527)
-0.111
(0.973)
19.468
(0.437)
300
25
0.262
(b) Controlled Variables
Ln(TA)t-1
EQAt-1
LIQAt-1
ROAAt-1
FUNDINGt-1
NPLt-1
CIRt-1
AGDP
INF
FMD
AR
PM
SUP
CR
PFC
Constant
Observations
Number of Banks
R2
37.198***
(0.000)
1466
114
0.208
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
19.615
(0.448)
300
25
0.262
ACCEPTED MANUSCRIPT
Table 5: Bank Deposit Growth - All Banks
Independent
Variables
(a) Key Variables
IB
Regression
(1)
(2)
(3)
(4)
4.188***
(0.001)
-2.689**
(0.042)
-0.595
(0.868)
3.844***
(0.006)
-5.153***
(0.001)
0.023
(0.995)
0.347
(0.927)
-6.954***
(0.000)
3.248
(0.357)
0.072
(0.984)
-7.892***
(0.000)
3.758
(0.282)
0.2735
0.3658
0.2486
0.1995
0.2913
0.2421
0.2676
0.1947
-2.115***
(0.000)
0.084
(0.776)
-0.032
(0.450)
0.821*
(0.083)
-0.157*
(0.076)
-0.562***
(0.002)
0.081*
(0.095)
0.312*
(0.079)
0.178
(0.163)
-0.048**
(0.031)
-2.461***
(0.000)
0.052
(0.858)
-0.087**
(0.039)
0.755*
(0.095)
-0.165*
(0.068)
-0.573***
(0.002)
0.086*
(0.077)
0.227
(0.304)
0.189
(0.117)
-0.050**
(0.039)
-0.115
(0.889)
2.193***
(0.000)
-0.390
(0.384)
-0.276
(0.485)
-1.646***
(0.000)
0.238
(0.432)
-0.091**
(0.031)
0.482
(0.316)
-0.144
(0.106)
-0.609***
(0.001)
0.081
(0.116)
0.243
(0.168)
0.164
(0.205)
-0.036*
(0.091)
-2.048***
(0.000)
0.170
(0.555)
-0.120***
(0.005)
0.596
(0.181)
-0.155*
(0.087)
-0.607***
(0.001)
0.088*
(0.086)
0.222
(0.305)
0.211*
(0.092)
-0.043*
(0.070)
0.028
(0.973)
1.746***
(0.003)
-0.505
(0.240)
0.178
(0.674)
-6.897***
(0.000)
7.973
(0.228)
53.972***
(0.000)
1762
139
0.114
GFC
IB x GFC
Tests (p-values)
Pl+P3 = 0
p2+p3 = o
(b) Controlled Variables
Ln(TA)t-1
EQAt-1
LIQAt-1
ROAAt-1
FUNDINGt-1
NPLt-1
CIRt-1
AGDP
INF
FMD
AR
PM
SUP
CR
PFC
IB x PFC
Constant
Observations
Number of Banks
R2
55.299***
(0.000)
1762
139
0.092
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
57.211***
(0.000)
1762
139
0.106
-8.234***
(0.000)
8.022
(0.242)
51.552***
(0.000)
1762
139
0.107
ACCEPTED MANUSCRIPT
Table 6: Bank Deposit Growth - Separate Samples
Independent
Variables
(a) Key Variables
GFC
(b) Controlled Variables
Ln(TA)t-1
EQAt-1
LIQAt-1
ROAAt-1
FUNDINGt-1
NPLt-1
CIRt-1
AGDP
INF
FMD
AR
PM
SUP
CR
Conventional Banks
(1)
(2)
__
Observations
Number of Banks
R2
(4)
-4.065***
(0.002)
-6.480***
(0.000)
-7.691*
(0.079)
-8.279*
(0.090)
-2.887***
(0.000)
-0.203**
(0.034)
-0.093**
(0.035)
0.719
(0.195)
-0.154*
(0.070)
-0.397***
(0.000)
-0.016
(0.711)
0.413***
(0.004)
0.266**
(0.045)
-0.054**
(0.033)
-1.395*
(0.059)
1.598***
(0.000)
-0.062
(0.809)
-0.232
(0.369)
-2.332***
(0.000)
-0.067
(0.536)
-0.131***
(0.003)
0.479
(0.390)
-0.142*
(0.093)
-0.430***
(0.000)
-0.019
(0.652)
0.404***
(0.004)
0.305**
(0.019)
-0.043*
(0.076)
-1.182
(0.118)
1.173***
(0.003)
-0.216
(0.394)
0.278
(0.276)
-6.068***
(0.000)
65.022***
(0.000)
1463
114
0.149
-3.855**
(0.018)
1.084
(0.239)
0.364
(0.209)
-1.740
(0.326)
-0.546
(0.148)
-1.905***
(0.008)
0.143**
(0.030)
-0.553
(0.400)
0.443
(0.140)
0.031
(0.700)
2.215
(0.343)
2.773*
(0.081)
0.676
(0.587)
0.685
(0.580)
-3.664**
(0.015)
1.102
(0.227)
0.355
(0.211)
-1.798
(0.312)
-0.549
(0.152)
-1.919***
(0.009)
0.143**
(0.032)
-0.545
(0.412)
0.453
(0.114)
0.033
(0.679)
2.335
(0.336)
2.659
(0.152)
0.660
(0.590)
0.777
(0.558)
-1.461
(0.772)
68.341**
(0.040)
299
25
0.195
PFC
Constant
Islamic Banks
(3)
71.237***
(0.000)
1463
114
0.136
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
70.263*
(0.054)
299
25
0.195
ACCEPTED MANUSCRIPT
Table 7: Risk and Bank Lending Growth
First-Difference GMM
Independent
Variables
(1)
(2)
NPL-1
0.641***
0.638***
(0.000)
(0.000)
ALt-i
0.001
-0.003
(0.929)
(0.776)
ALt-2
0.014***
0.011**
(0.008)
(0.038)
ALt-3
0.013***
0.010**
(0.000)
(0.010)
Ln(TA)t-1
-0.239
-0.016
(0.673)
(0.976)
EQAt-1
-0.237*
-0.236*
(0.081)
(0.080)
LIQAt-1
-0.021
-0.028
(0.344)
(0.244)
AGDP
-0.053**
-0.053**
(0.016)
(0.012)
INF
-0.057**
-0.046*
(0.044)
(0.054)
FMD
0.007
0.005
(0.699)
(0.784)
GFC
0.225
0.190
(0.222)
(0.376)
AR
-0.022
(0.944)
PM
0.010
(0.963)
SUP
0.093
(0.534)
CR
0.009
(0.914)
Constant
Observations
Number of Banks
P(Hansen)
P(AR1)
P(AR2)
AA1267
139
0.109
0.005
0.976
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
1267
139
0.176
0.005
0.886
System GMM
(3)
0.742***
(0.000)
0.003
(0.772)
0.025***
(0.000)
0.024***
(0.000)
0.525
(0.362)
-0.172
(0.141)
-0.035*
(0.072)
-0.084**
(0.012)
-0.042
(0.153)
-0.025
(0.155)
0.258
(0.228)
-2.919
(0.779)
1406
139
0.074
0.005
0.925
(4)
0.738***
(0.000)
0.001
(0.957)
0.023***
(0.000)
0.021***
(0.003)
0.673
(0.282)
-0.165
(0.159)
-0.040**
(0.035)
-0.081***
(0.009)
-0.035
(0.173)
-0.022
(0.220)
0.269
(0.271)
0.174
(0.784)
-0.086
(0.818)
0.142
(0.577)
0.008
(0.932)
-6.852
(0.584)
1406
139
0.152
0.005
0.983
ACCEPTED MANUSCRIPT
Table 8: Risk and Conventional and Islamic Lending Growth
Independent
___ First-Difference GMM
Variables
(1)
(2)
NPL-1
0.646***
0.644***
(0.000)
(0.000)
ALt-i I(IB = 0)
-0.002
-0.005
(0.827)
(0.511)
ALt-2 I(IB = 0)
0.013**
0.010**
(0.013)
(0.033)
ALt-3 I(IB = 0)
0.015***
0.013***
(0.001)
(0.008)
ALt-1 I(IB = 1)
0.023
0.018
(0.457)
(0.513)
ALt-2 I(IB = 1)
0.024*
0.018
(0.098)
(0.203)
ALt-3 I(IB = 1)
0.007
0.003
(0.429)
(0.717)
Ln(TA)t-1
-0.230
-0.082
(0.681)
(0.876)
EQAt-1
-0.216
-0.214
(0.154)
(0.142)
LIQAt-1
-0.019
-0.026
(0.420)
(0.289)
AGDP
-0.057**
-0.059***
(0.011)
(0.006)
INF
-0.057**
-0.043*
(0.043)
(0.073)
FMD
0.008
0.007
(0.650)
(0.684)
GFC
0.233
0.221
(0.229)
(0.319)
AR
0.002
(0.994)
PM
-0.013
(0.952)
SUP
0.097
(0.494)
CR
0.013
(0.884)
Constant
System GMM
(3)
0.747***
(0.000)
-0.001
(0.912)
0.022***
(0.000)
0.023***
(0.001)
0.037
(0.234)
0.044***
(0.006)
0.024
(0.103)
0.519
(0.335)
-0.145
(0.275)
-0.033*
(0.077)
-0.087***
(0.009)
-0.043
(0.101)
-0.024
(0.171)
0.268
(0.181)
-3.282
(0.747)
(4)
0.745***
(0.000)
-0.003
(0.713)
0.020***
(0.003)
0.022***
(0.004)
0.033
(0.267)
0.041***
(0.010)
0.022
(0.136)
0.639
(0.292)
-0.139
(0.321)
-0.038**
(0.046)
-0.088***
(0.006)
-0.034
(0.195)
-0.020
(0.269)
0.290
(0.235)
0.233
(0.731)
-0.106
(0.794)
0.139
(0.594)
0.005
(0.961)
-6.874
(0.583)
P-value:
27=1^2/ = 27=1^3/
0.5340
0.6190
0.1801
0.1913
Observations
Number of Banks
P(Hansen)
P(AR1)
P(AR2)
1267
139.000
0.096
0.007
0.866
1267
139.000
0.152
0.006
0.782
1406
139.000
0.073
0.006
0.960
1406
139.000
0.143
0.006
0.879
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
ACCEPTED MANUSCRIPT
Table 9: Risk and Financing Growth of Islamic Banks
Independent
System GMM
Variables
(1)
(2)
(3)
NPL-1
1.034***
0.946***
1.038***
(0.000)
(0.000)
(0.000)
ALt-i
0.034
0.026
0.035
(0.208)
(0.259)
(0.371)
ALt-2
0.037***
0.023**
0.045**
(0.000)
(0.016)
(0.010)
ALt-i x GFCt-i
-0.007
(0.791)
ALt-2 x GFCt—2
-0.024
(0.657)
Ln(TA)t-1
0.492
-0.080
0.407
(0.669)
(0.972)
(0.729)
EQAt-1
-0.229***
-0.164
-0.244*
(0.006)
(0.131)
(0.051)
LIQAt-1
0.033
0.025
0.031
(0.685)
(0.603)
(0.741)
AGDP
-0.116
-0.039
-0.108
(0.318)
(0.638)
(0.332)
INF
-0.018
-0.006
-0.020
(0.350)
(0.830)
(0.387)
FMD
0.003
0.031
0.011
(0.952)
(0.508)
(0.766)
GFC
-0.582
0.368
-0.843
(0.546)
(0.633)
(0.597)
AR
1.823
(0.170)
PM
-1.881**
(0.027)
SUP
1.218**
(0.015)
CR
0.057
(0.755)
Constant
-6.609
-7.521
-5.585
(0.659)
(0.825)
(0.750)
Observations
r* 251
251
251
Number of
25
25
25
Banks
P(Hansen)
0.977
0.999
0.963
P(AR1)
0.077
0.118
0.078
P(AR2)
0.843
0.862
0.978
(4)
0.990***
(0.000)
0.012
(0.688)
0.031***
(0.003)
-0.006
(0.772)
0.004
(0.915)
0.183
(0.907)
-0.205*
(0.058)
0.001
(0.975)
-0.003
(0.976)
-0.002
(0.968)
0.025
(0.484)
0.064
(0.969)
-0.693
(0.761)
-1.360**
(0.048)
1.163**
(0.024)
0.106
(0.549)
-7.021
(0.787)
251
25
LSDVC
(5)
(6)
1.094***
1.075***
(0.000)
(0.000)
0.036***
0.027**
(0.008)
(0.045)
***
0.036
0.027**
(0.008)
(0.049)
0.016
0.003
(0.507)
(0.898)
-0.026
-0.030
(0.252)
(0.192)
0.474
0.462
(0.393)
(0.480)
-0.130**
-0.163***
(0.014)
(0.002)
0.018
0.004
(0.567)
(0.910)
-0.039
-0.038
(0.626)
(0.633)
-0.037
-0.045
(0.437)
(0.343)
0.058*
0.082**
(0.092)
(0.022)
-0.517
-0.084
(0.443)
(0.907)
1.294**
(0.014)
-1.039***
(0.001)
0.783***
(0.000)
-0.229
(0.111)
251
25
251
25
1.000
0.053
0.935
p-values in parentheses
*
p < 0.1, ** p < 0.05, *** p < 0.01
HIGHLIGHTS
•
Investigate the lending growth, deposit growth and credit risk of Islamic and conventional
banks during the crisis
•
•
•
ACCEPTED MANUSCRIPT
Lending growth of Islamic banks is resilient to the crisis
No different in deposit growth of Islamic and conventional banks during the crisis
No evidence of excessive risk taking by Islamic banks in their expansion of lending