2. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
2
2020). Under these circumstances, we expect banks that operate in countries with macro-prudential policies in place before the crisis
to be more resilient.
Another strand of the banking literature focuses on possible performance differences depending on banks’ business orientation,
namely Islamic vs conventional. Some researchers highlight the potential superior performance of Islamic banks (IBs) during economic
downturns. The main argument is that IB asset-based and risk-sharing intermediation practices dictated by Islamic laws (Shariah)
protect IBs from crises’ adverse impacts. Indeed, IBs performed quite well in the GFC period based on profitability, credit supply,
deposit growth and withdrawals, and returns in the stock market. This contributed to their reputation as being more stable banks than
their conventional counterparts and increased their popularity.2
Early empirical evidence shows that during the GFC period, credit
growth was higher for IBs than conventional banks (CBs) (Hasan and Dridi, 2011; Beck et al., 2013, Ibrahim, 2016; Ibrahim and Rizvi,
2018). Although the existing literature would support higher intermediation capacity for IBs compared to CBs, there is no evidence of
whether IBs maintained higher credit growth than their counterparts during the COVID-19 episode.
Building on these strands of the literature on bank lending behavior during crises, and the importance of macro-prudential policies
to bank resilience, we zoom in on potential differences between IBs and CBs, and seek to address two essential economic questions in
the current study: (i) How did IBs perform in terms of lending activities compared to their peers (CBs) during the outbreak of the
COVID-19 crisis? (ii) Do country characteristics, and particularly the utilization of macroprudential measures in the pre-crisis period,
affect bank lending behavior during the crisis? These questions are critical given the importance of bank resilience to financial stability
and economic growth.
To answer these questions, we use a sample of 421 banks domiciled in 17 countries with dual banking systems. The period of the
study extends six months balanced around March 2020 when the World Health Organization declared the coronavirus disease as a
pandemic. Around 30 percent of the sample (i.e., 117 banks) is denoted Islamic, while the rest (i.e., 344 banks) is denoted conven
tional. Inspection of the data shows no difference between the credit growth of both types of banks in the period preceding the crisis.
The COVID-19 situation led to lower bank credit growth overall compared to pre-crisis levels. However, the negative impact is only
significant for CBs. Growth in loans of IBs is statistically more significant than that of CBs during the crisis.
We estimate regression models that control for a comprehensive set of bank variables to carefully isolate the impact of COVID-19 on
credit growth as it varies by bank business orientation. The findings confirm a higher resiliency for IBs compared to their conventional
counterparts during the early stage of the COVID-19 crisis. Specifically, our evidence shows that differential lending growth between
IBs and CBs during the crisis period was around 2.5 % higher for the former. This central finding remains robust against a series of
robustness checks, including the estimation of the regression models with alternative empirical approaches, a different definition of
loan growth, the extension of the pre-crisis period, the use of lags for control variables to mitigate the potential concern about reverse
causality, and additional control variables. Notably, rather than relying on a dummy variable that switches to one for all countries
simultaneously, our regression analysis differentiates among the country variations in COVID-19 severity. We employ the John
Hopkins University number of COVID-19 cases per 100,000 people in each country, the (Hale et al., 2020) COVID-19 stringency index,
and the risk of openness index. The reported results support our main conclusion that IBs sustained their lending activities during the
initial phase of the COVID-19 crisis while CBs did not.
We further extend the empirical analysis by investigating the effect of the pre-crisis usage of macroprudential policies on the
divergence of loan growth between IBs and CBs during the global health crisis. Implementing macroprudential policies aims to
strengthen financial stability and shield economic growth against systematic risks and financial imbalances. Prior research found
macroprudential policies to be effective in dampening procyclical bank lending and risk-taking following periods of economic ex
pansions (Claessens et al., 2013; Cerutti et al., 2017; Gómez et al., 2020). Applied to our context, we expect the higher ability of IBs to
maintain credit growth during the crisis to be stronger in countries that implemented macroprudential policies in the pre-crisis period.
The reported evidence supports our conjecture.
Our analysis advances the existing literature along two fronts: first, we contribute to the literature on Islamic banking by showing
that IBs showed higher resilience than CBs during COVID-19. This may have positively impacted economic growth during that period.
Second, by showing that this ability to sustain credit growth depends on macroprudential policies, we contribute to the literature on
the importance of such policies to financial stability in general.
The paper proceeds as follows. Section 2 reviews the literature on the relative performance of IBs, and further develops our hy
potheses. Section 3 describes our data and methodology. Section 4 discusses our empirical results, and Section 5 concludes.
2. Islamic versus conventional banks
2.1. Background and relevant literature review: IBs’ credit during crisis times
Like CBs, IBs perform the essential intermediation task by lowering the adverse consequences of information and transaction costs.
IBs and CBs engage in activities that reduce the cost of searching for profitable investment opportunities, exercise governance and
corporate control, and ultimately allocate resources. However, unlike CBs, IBs adhere to principles that determine Islamically
acceptable forms of business transactions. These governing laws, commonly known as Shariah, lead intermediation in IBs to be asset-
based and built on a risk-sharing structure. Financial transactions must have ’material finality’ by involving the exchange of tangible
2
By the end of 2018, the Islamic financial services industry’s Size had already surpassed the 2 trillion dollars to reach $2.19 trillion (Islamic
Financial Services Board, 2019).
N. Boubakri et al.
3. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
3
assets in the real economy, not financial assets. By linking financial transactions to the real economy, lending in IBs must be asset-based
and not debt-based.3
Besides, Shariah laws establish a risk-sharing framework whereby IBs are prohibited from producing ‘risk-free’
profits such as those made on collateralized loans. Alternatively, profits must be justly earned by taking an equity position in the
transactions with a proportional share of risk between the providers of funds and users of funds. Islam’s position towards the pro
hibition of interest and the implication of risk-sharing was concisely put by El Gamal (2000, p. 33), “In Islam, one does not lend to make
money, and one does not borrow to finance business.”.
Although comparative studies of IBs and CBs abound, only a few papers have focused on the lending behavior of IBs during crisis
times. By comparing the performance of IBs and CBs, Hassan and Dridi (2011) report that IBs’ credit growth was twice that of CBs
during the GFC and, in general, was less affected by the crisis. The higher solvency of IBs played a crucial role in helping IBs support the
demand for loans while the crisis unfolded. Another of their key results was that IBs conducted excessive due diligence and screening,
lending loans in sectors that were not affected by the GFC. Beck et al. (2013) reached a similar conclusion using a sample of 510 banks,
of which 88 are Islamic, and report that IBs are less likely to disintermediate during crisis times than CBs.
Beck et al. (2013) also reported that IBs observed superior stock market performance than CBs, which they attribute to IBs’ higher
asset quality and better capitalization. Farooq and Zaheer (2015) investigated how the financial panic affected banks’ deposit and
lending behavior in Pakistan. During the September – October 2008 financial panic, the authors reported an accelerated pattern of
deposit withdrawals that was unique to CBs. IBs experienced fewer deposit withdrawals, and remarkably, some reported higher deposit
rates during the financial panic period. Farooq and Zaheer (2015) contend that weaker withdrawals of deposits at IBs may explain their
robust credit growth. Ibrahim (2016) provides evidence from Malaysia that lending in CBs tends to be pro-cyclical, as a decline in GDP
growth results in lower loan growth. Conversely, the lending behavior of IBs is not influenced by business cycles and can thus be
considered counter-cyclical. This finding supports the view that IBs could play a stabilizing role in the economy. Using data on 25 IBs
and 114 CBs from 10 dual-banking countries, Ibrahim and Rizvi (2018) find no significant difference in the lending growth of IBs and
CBs during normal periods. However, such behavior differs during crisis periods, implying that CBs decrease their lending during the
crisis periods while IBs do not.
2.2. Hypotheses development
The comparative literature above argues that the asset-based and risk-sharing features of IBs, as opposed to the debt-based and risk-
transfer features of CBs, have shielded IBs from the negative consequences of crises. These unique characteristics could also explain
IBs’ ability to extend lending against the cycle. More precisely, according to Shariah principles, IBs raise funds through profit-sharing
investment accounts (PSIA) that allow profits to be shared at a pre-determined rate, do not guarantee the nominal value of such
deposits, and restrict losses to the account holders. Interest rates are excluded, and the returns on the bank assets determine the
depositors’ returns. Similarly, the profit- and loss-sharing (PLS) mechanism is also observed on the asset side by financing investments
using participation loans such as Mudarabah or Musharakah contracts.4
Equity participation principle provides IBs with the flexibility
to adjust to shocks during downturns. Realized losses to the bank asset value are then absorbed by a corresponding reduction in
deposits held by account holders. As a result, the assets’ and liabilities’ values in real terms are constantly aligned with each other. The
ability of IBs to engage in pass-through arrangements serves as protection from the asset-liability exposure typically faced by CBs.
The equity-based system in IBs predicts a different lending behavior than that in CBs during economic downturns, such as that
associated with the COVID-19 pandemic. In a traditional setting, banks become less willing to maintain the flow of credit to corporate
businesses during stressful times when their ability to make interest payments is particularly weakened. However, IBs strictly prohibit
lending based on pre-determined interest rates and premises agreements whereby the generated profits are commensurate with the
level of risk position or are linked to transactions in the real economy. IBs are expected to be readily available to provide credit to
businesses with profitable investment opportunities with much-needed funding. The underlying profit- and loss-sharing mechanism
alleviates the borrower’s pressure to make interest payments independent of the investment returns. Simultaneously, the PLS mitigates
the IBs’ concerns regarding losses since the investments are funded according to agreements requiring the distribution of profits
between the borrower and the bank at a pre-determined rate. Evidence consistent with the conjecture that PLS lowers Islamic banks’
risk aversion towards extending new loans when economic conditions deteriorate appears in Beck et al. (2013) and Ibrahim (2016).
Based on this discussion and considering the COVID-19 crisis as a natural experiment, we draw our first hypothesis as follows:
Hypothesis 1: IBs sustained a higher lending activity than CBs during the COVID-19 crisis.
Next, we consider the link between the lending behavior of IBs/CBs in the crisis period of COVID-19 to pre-crisis country char
acteristics, particularly the reliance on macroprudential policies. The devastating effects of the 2008 GFC spurred increasing attention
on the role of financial regulations and supervision, particularly macroprudential policies that are meant to help stabilize the financial
system. Despite continued efforts of the Bank for International Settlements to advocate macroprudential policies since the 2008 GCF,
emerging countries’ regulatory and supervisory frameworks started to incorporate macroprudential perspectives only two decades
3
CBs aim to make loans with low credit risk at a pre-determined interest rate. In comparison, IBs link lending to the purchase and subsequent
selling of an underlying tangible asset to borrowers.
4
Mudharabah is a participation contract between the bank and the borrower, such that profits are shared at a pre-determined ratio and losses are
exclusively suffered by the bank, except for situations that involve misconduct, negligence, or breach of contract by the investor. Whereas,
Musharakah requires the Islamic bank and the customer to contribute capital to a business adventure such that profits are shared according to the
agreement and losses in proportion to capital contribution.
N. Boubakri et al.
4. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
4
after in the early 2000 s. Researchers agree on the success of macroprudential tools in curbing credit growth (e.g., Bruno et al., 2017;
Cerutti et al., 2017).5
Studies that assess the ability of macroprudential policies to influence banking indicators usually find that some
devices, such as caps on loan-to-value (LTV) and changes in debt-to-income (DTI) ratios for mortgage loans, are successful in con
taining credit growth (Claessens et al., 2013; Lim et al., 2011). Alam et al. (2019) found that macroprudential tools such as loan-
targeted instruments significantly impacted real credit to households. More recently, Gómez et al. (2020) reported that macro-
prudential policies in Colombia reduced the credit cycle and risk-taking from 2006 to 2009.
Although the academic debate on the effectiveness of macroprudential policies in promoting financial stability and mitigating
economic shocks is gaining momentum, it remains vastly conducted in conventional banking. Evidence on the consequences of the
interplay between Islamic banking and macroprudential tools is scarce and practically nonexistent. Our study addresses this void in the
empirical banking literature by comparing how the COVID-19 crisis affected the lending behavior of IBs and CBs in countries that differ
in their adoption of macroprudential policies.
Due to imperfections, such as the lack of instruments that comply with risk-sharing principles, IBs may not be perfectly shielded
against shocks. Therefore, macroprudential policies are likely to reinforce the resilience of IBs to sustain credit growth in the face of
economic adversity. We consequently state our second hypothesis as follows:
Hypothesis 2: IBs in countries with active utilization of macroprudential policies in the year approaching the pandemic sustain
higher lending activity than CBs during the COVID-19 crisis.
3. Methodology and data
3.1. Methodology
The first objective of the current study is to examine whether IBs sustain their lending compared to CBs counterparts during the
COVID-19 outbreak. We check this by employing the following model:
gict = β0 + β1.Crisist × IBic + δ.Xict + φi + φct + εict (1)
Above, i, c, and t denote bank i, country c, and quarter t. gict is the growth (or variation) of bank loans from the end of quarter t − 1 to
the end of quarter t, estimated as, alternatively, i) growth in total outstanding loans computed as g(L) = (Loant − Loant− 1)/Loant− 1, (ii)
change in total outstanding loans to total assets ratio (LoanAsset) computed as Δ(LTA) = (LoanAssett − LoanAssett− 1) and (iii) change in
total outstanding loans as a share of (2018) GDP computed as Δ(LTGDP) = (Loant − Loant− 1)/GDP2018. Compared to g(L), which
measures growth in the total amount of credit created through new loans made by a bank, Δ(LTA) calculates the change in relation to
the bank’s loan portfolio to its total assets. The third proxy, Δ(LTGDP), indicates the increase in a bank’s credit scaled by the country’s
GDP where the bank is domiciled.
Crisist is a dummy variable that takes value one during the COVID-19 pandemic (2020Q1-2020Q3) and zero for the three quarters
preceding the crisis (2019Q2-2019Q4). Using quarterly series spanning from 2019Q2 to 2020Q3, we select a period of three quarters
during the pandemic and three quarters before the crisis. We then examine the relative performance of IBs during the pandemic using
an indicator variable,IBic, which is a dummy variable that takes value one for Islamic bank i in country c, and zero otherwise. We treat
conventional banks as the base. To examine our first hypothesis, we rely on an interaction term , Crisist × IBic. The coefficient β1
measures the difference between performances in banks (IBs versus CBs) during the COVID-19 pandemic. A positive and significant
point estimate of β1 indicates that IBs sustained their lending during the pandemic compared to their CBs counterparts (consistent with
our first hypothesis).
Xict is a vector of bank-specific control variables that may explain banks’ lending behavior. Specifically, we include a measure of
bank size and five proxies of bank healthiness as captured by CAMEL.6
First, Size represents bank size, measured by the natural
logarithm of total assets. Banks of different sizes may have different business models and lending policies. Second, we include a proxy
for capital adequacy, the ratio of equity to total assets (Equity). Banks with a high level of equity ratio face lower costs of funding. Thus,
they can invest in risky assets such as loans. In addition, well-capitalized banks have a lower risk of bankruptcy and consequently have
a greater capacity to withstand financial shocks (Demirgüç-Kunt et al., 2013; Kapan and Minoiu, 2018). Third, we consider loan loss
provisions to total loan ratio (LoanLoss) as a proxy for asset quality. Banks with a high level of impaired loans are assumed to be more
fragile and lend less. Fourth, we capture the quality of bank management by including the cost-to-income ratio (Cost). We assume that
inefficient banks have fewer resources to originate new loans. Fifth, we add return on assets (ROA) to consider the earnings dimension,
as more profitable banks may be better positioned to lend more. Sixth, we add liquid assets to total deposits and short-term funding
ratio (Liquidity) to account for a bank’s liquidity position, as one may expect that banks holding more liquid assets have a lower
potential for growth.
We include bank fixed effects (φi) in all regressions to control for time-invariant bank-specific factors that may affect bank lending
policies, such as bank risk culture. This also removes unobserved heterogeneity in bank loan policies (Li et al., 2020). In addition, we
capture the effects of country × time variables by including φct, which represent the time-variant country fixed effects. They account for
time-variant country-specific features that might drive cross-bank differences in loan activities, such as demand for credit, macro
economic shocks, regulatory differences, and country responses to the crisis (Demirgüç-Kunt et al., 2013). Note that φi subsumes the
5
Please refer to Galati and Moessner (2018) for a survey on macroprudential policies and their effectiveness.
6
CAMEL is an acronym for capital adequacy, assets, management capability, earnings, and liquidity.
N. Boubakri et al.
5. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
5
level effects of IBs, and φct subsumes the level effects of the crisis, and thus they both fully absorb the direct effects of IB and Crisis in Eq.
(1). These two types of fixed effects control for time-invariant variations across banks and time-variant factors across countries. By
including all potential bank-level control variables that may affect bank lending activities and this rich set of fixed effects, we reduce
concerns about omitted variable bias. The disturbance term is εict. Regressions are estimated using ordinary least square (OLS) esti
mates, and the statistical inferences are based on clustered standard errors at the bank level to address potential autocorrelation in the
residuals.
The second objective of our study is to assess whether pre-crisis financial conditions affect the relative resilience of IBs to the
COVID-19 pandemic (see, for example, Cornett et al., 2011 concerning the 2008 GFC and Li et al., 2020 regarding the COVID-19
pandemic). The existing literature highlights the role of pre-crisis conditions in influencing the performance of banks during the
subsequent crisis (see, for example, Demirgüç-Kunt et al., 2013; Brei et al., 2013; Igan and Mirzaei; 2020, Cornett et al., 2011; Ivashina
and Scharfstein 2010; Beltratti and Stulz 2012; Balvers et al., 2017). Specifically, as previously discussed, we expect that a country’s
pre-crisis activation of macroprudential policies conditions the resilience of banks during the crisis. To examine the impact of pre-crisis
macroprudential policies on the loan sustainability of IBs during the crisis, we conduct a subsample analysis by splitting the dataset
into two subsamples at the median proxy of pre-crisis usage of macroprudential policies. We then estimate Eq. (1) for each subsample.
3.2. Data
Bank-level quarterly data come from the ORBIS database by Bureau Van Dijk, which provides financial data for more than 40
million firms (including banks) from more than 100 countries worldwide. Our raw data cover the period 2017 Q4 – 2020 Q3. We,
however, utilize 2019 Q2 up to the latest quarter available (at the time of conducting this research), which is 2020 Q3. This enables us
to study growth in bank loans around the COVID-19 pandemic and to have a nearly equal number of observations in the sample before
and during the crisis. Since not all banks enter the sample every quarter, our final dataset is unbalanced. We select all banks (con
ventional and Islamic) that belong to countries with dual banking systems. Given our interest in evaluating the resilience of IBs during
the COVID-19 crisis, we focus our baseline analysis on banks that are present before and during the crisis.
As a result, 461 banks (117 IBs and 344 CBs) from 17 countries survive the above filtering criteria.7
The number of banks in our
dataset varies by country. On average, each country has about 27 banks with available data. We handle outliers by winsorizing all
variables at the 1st and 99th percentiles to reduce the influence of outliers.
3.3. Descriptive evidence
As a preliminary way of exploring the data, we present some descriptive evidence on how the pandemic affected bank lending and
whether IBs reacted differently from their CBs counterparts regarding loan growth.
Table 1, Panels A and B provide definitions and descriptive statistics of the main variables used in the present study for the entire
sample and the subsamples of IBs and CBs. According to Panel A, the average loan growth of IBs during pre-crisis was almost equal to
that of CBs but decreased for both types of banks during the crisis. Nonetheless, IBs allocated more loans than CBs during the COVID-19
pandemic. The differences between pre and during the crisis are statistically insignificant for IBs but significant for CBs. IBs are, on
average, smaller, better capitalized, but less efficient than conventional banks. In addition, they are more liquid but less profitable than
their rival conventional ones. These results align with Beck et al. (2013) and Ibrahim and Rizvi (2018).
Furthermore, Table 1, Panel B shows a univariate analysis of the differences in bank lending between IBs and CBs in pre and during
the COVID-19 pandemic. We find no difference between the credit growth of both types of banks in pre-crisis. However, when it comes
to the COVID-19 crisis, the growth in loans by IBs is statistically more significant than that of CBs.
Table 2 presents the number of IBs and CBs, average loan growth (or variation), and the severity of the crisis by country. Out of 117
IBs in our sample, 18 banks come from Malaysia, followed by 16 from Iraq. As measured by growth in gross loans, the mean of credit
growth is the highest in Syria (8 %) and lowest in Pakistan (-2%) during the whole sample period. Focusing on the Oxford stringency
index to measure the severity of the COVID-19 pandemic, the most affected countries are Oman and Palestine. In contrast, the least
affected are Indonesia and Malaysia. Fig. 1 plots the change in the average bank loan growth (measured by the growth in gross loans)
between the COVID-19 crisis and pre-COVID-19 for both IBs and CBs to complement this country-level analysis. The change in credit
growth is negative for eight countries and positive for the remaining countries when considering IBs. However, the change in bank loan
growth for CBs is negative for most (more than 80 % of) countries.
To investigate further the impact of the COVID-19 crisis on IBs and CBs, we compare the resilience of banks based on bank distance
to default (DTD) and probability of default (PD). The former reveals how far away a bank is from default, with higher figures denoting
lower risk. The latter reflects the default risk of publicly-listed firms by quantitatively analyzing numerous covariates. The data are for
43 IBs, and 145 CBs obtained from the Credit Research Initiative, National University of Singapore. Appendix Table A1 summarizes the
effects of the COVID-19 crisis on these bank risk indicators. It is worth highlighting that the impact on both types of banks is negative,
but the adverse impact is more pronounced for CBs.
Overall, we find that: (i) the COVID-19 pandemic adversely affected the lending behavior of both IBs and CBs; (ii) there is no
significant difference between loan growth for IBs and CBs in the pre-crisis period; (iii) IBs performed better with regards to lending
7
Following the literature on IBs, we include only fully-fledged IBs, neglecting CBs with an Islamic window.
N. Boubakri et al.
6. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
6
Table 1
Definition and summary statistics of main variables: Islamic versus conventional banks. Panel A reports a detailed definition and a comparison of
mean values between IBs and CBs for all variables in our analysis. Panel B reports a univariate analysis of bank lending. ***, **, and * denote statistical
significance at the 1%, 5%, and 10% levels, respectively. Our sample includes 461 banks (out of which 117 are IBs) in 17 countries over 2019Q2-
2020Q3 (pre-crisis: 2019Q2-2019Q4 vs crisis: 2020Q1-2020Q3). Panel A: Descriptive statistics.
Table 1A: Definition and summary statistics of all variables
All banks IBs CBs
Pre-
crisis
Crisis Diff. Pre-
crisis
Crisis Diff. Pre-
crisis
Crisis Diff.
Variable Definition (1) (2) (3)=
(2)-(1)
(4) (5) (6)=
(5)-(4)
(7) (8) (9)= (8)-
(7)
Dependent variables
g(L) The growth in
total
outstanding
loans, calculated
as g(L)=(Loant-
Loant-1) /Loant-1.
0.024 − 4E-
04
− 0.024*** 0.022 0.014 − 0.008 0.025 − 0.005 − 0.030***
Δ(LTA) The chnage in
total
outstanding
loans to asset
ratio
(LoanAsset),
calculated as
Δ(LTA)=
(LoanAssett-
LoanAssett-1).
− 0.001 − 0.006 − 0.005*** − 0.003 − 0.002 0.001 − 0.001 − 0.008 − 0.007***
Δ(LTGDP) The change in
total
outstanding
loans as share of
(2018) GDP,
calculated as
Δ(LTGDP)=
(Loant-Loant-1)/
GDP2018.
0.422 0.259 − 0.163** 0.546 0.603 0.057 0.378 0.143 − 0.236***
Bseline controls
Size Natural
logarithm of a
bank total
assets.
14.561 14.591 0.030 14.483 14.510 0.027 14.588 14.619 0.031
Equity The ratio of
equity to total
assets of a bank.
0.202 0.203 0.001 0.241 0.241 0.000 0.188 0.190 0.002
LoanLoss Loan loss
provisions to
total loan ratio.
A loan loss
provision is an
expense set
aside as an
allowance for
bad loans.
0.008 0.008 0.000 0.008 0.008 0.000 0.008 0.008 0.000
Cost Bank cost-to-
income ratio, as
calculated by
dividing the
operating
expenses by the
operating
income.
0.616 0.605 − 0.010 0.714 0.697 − 0.017 0.584 0.576 − 0.008
ROA Return on assets,
which is defined
as profit before
tax as a share of
average assets of
a bank.
0.011 0.012 0.001 0.004 0.006 0.002 0.013 0.014 0.001
(continued on next page)
N. Boubakri et al.
7. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
7
during the crisis, as compared to their conventional counterparts, and (iii) the adverse impact of the COVID-19 on banks is also evident
if considering other performance indicators, such as firm survival. Next, we turn to formal regression analyses to understand the
relative performance of IBs during the crisis based on such an essential heterogeneity in bank lending observed across both types of
banks.
Table 1 (continued)
Table 1A: Definition and summary statistics of all variables
All banks IBs CBs
Pre-
crisis
Crisis Diff. Pre-
crisis
Crisis Diff. Pre-
crisis
Crisis Diff.
Liquidity The bank liquid
assets to total
deposits and
short-term
funding ratio.
0.439 0.462 0.023 0.564 0.540 − 0.024 0.397 0.436 0.038
Other controls
ZSCORE Bank Z-score, as
a proxy for
individual bank
overall risk. It is
computed as
sum of return on
asset and capital
to asset ratio
divided by
return volatility.
Return volatility
is measured
based on a 5-
quarter window
basis of
volatility of the
return on assets
of the bank.
8.826 5.685 − 3.140*** 8.520 5.909 − 2.611*** 8.931 5.608 − 3.323***
AssetDiversification Asset
diversification
as measured by
1-|(Net loans –
Other earning
assets)/(Total
earning assets)|.
Asset diversity
takes values
between zero
and one with
higher values
indicating
greater
diversification.
0.516 0.525 0.008 0.444 0.444 0.000 0.539 0.549 0.010
FeeIncome Bank ratio of fee
and other
operating
income to total
assets.
0.016 0.015 − 0.001 0.017 0.013 − 0.004** 0.016 0.016 0.000
WholesaleFunding Bank short-term
funding to total
assets ratio.
0.077 0.072 − 0.006 0.074 0.071 − 0.003 0.078 0.072 − 0.007
Panel B: Univariate analysis of bank lending: Islamic versus conventional banks.
Table 1B: Univariate analysis of bank lending
N g(L) Δ(LTA) Δ(LTGDP)
Differences
IBPre - CBPre 1,251 − 0.003 − 0.002 0.168
IBCrisis - CBCrisis 1,205 0.019*** 0.007** 0.460***
N. Boubakri et al.
8. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
8
4. Results
4.1. Baseline results
We start our analysis by examining how IBs behave differently from CBs during the COVID-19 pandemic concerning loan growth.
Specifically, our main research question is whether IBs maintain their lending during the pandemic more than CBs. Table 3 reports the
results from estimating Eq. (1), using growth in total outstanding loans (g(L)), as well as change in total outstanding loans to total assets
ratio (Δ(LTA)) and change in total outstanding loans as a share of GDP (Δ(LTGDP)) as the response variables. Our interest variable is
the interaction term between Crisis and IB dummy indicators (Crisis × IB).
The positive and statistically significant coefficient of Crisis × IB in Columns 1–3 reveals that IBs were more resilient than CBs
during the early stage of the COVID-19 crisis. We find that lending by IBs grew about 2.5 % faster than that by CBs during the crisis
period. This is in line with the view that IBs have a comparative advantage when facing an external shock. Previous literature focusing
on the GFC reports that IBs continue extending credit during crisis periods due to their asset-based and risk-sharing structures (Beck
et al., 2013; Farooq and Zaheer, 2015; Ibrahim and Rizvi, 2018). Beck et al. (2013) show that IBs intermediate more funds than CBs,
especially during a crisis. Bilgin et al. (2021) recently found that economic uncertainties significantly decrease credit growth for
conventional banks rather than Islamic banks. The results also align with Mirzaei et al. (2022), who find disproportionately better
stock returns of IBs than CBs during the pandemic. Thus, our results extend the findings of previous studies by showing that IBs
sustained credit growth during the health crisis.
Credit supply is crucial during normal times and vital in recovering an economy from a crisis. The reduction in banks’ access
(especially CBs) to money market funds and wholesale funds affects their ability to allocate loans during a crisis (Cornett et al., 2011;
Zheng, 2020). IBs could tolerate more risks than their counterparts in a crisis, given that they are more liquid, better capitalized, and
could suffer fewer deposit withdrawals. Our results may also suggest that IBs can provide an alternative source of financing during
economically challenging times.
Although the estimated coefficients for the control variables generally have the expected signs, they are statistically weak or
insignificant in most cases. Larger banks allocate more credit, but the opposite is true when loans are normalized by total assets. Banks
with higher equity ratios tend to lend more, especially when measured by growth in loan-to-asset ratio. As measured by the loan loss
ratio, banks with high credit risk tend to reduce loan growth. It also appears that less efficient and more liquid banks lend less. The
impact of bank return on assets on bank lending during the COVID-19 pandemic is mixed.
Before moving to the next section, we check the impact of COVID-19 on the performance of all banks. This is to validate our primary
hypothesis that while bank lending was affected negatively by the pandemic, IBs were relatively more resilient. We investigate
whether IBs perform better than their conventional counterparts, regardless of the COVID-19 crisis. We remove the country-fixed
effects and re-estimate a variation of Eq. (1) after including the IB dummy variable in the model as our variable of interest. The re
sults are reported in Appendix Table A2, Columns 1–3. Of the three estimated models, only one that uses the change in total loan to
total asset ratio as the dependent variable shows marginal evidence that IBs had superior performance than CBs. The lack of signif
icance on the individual dummy variables in Columns 1 and 3 indicates no evidence for better performance of IBs in general. Alter
natively, when the crisis period is considered in the analysis, the significant positive sign on the estimated coefficients of the
interaction terms between Crisis and IB confirms our main finding that IBs exhibited higher credit growth during the COVID-19 crisis
Table 2
Number of banks, loan growth, and severity of the crisis by country.
Number of banks Loan growth Severity of the crisis
ID country Total IBs % CBs % g(L) Δ(LTA) Δ(LTGDP) Case per 100,000
population
Oxford
stringency index
Opening risk
index
1 Bahrain 13 8 61.5 5 38.5 − 0.008 − 0.002 1.078 6.481 72.530 0.769
2 Bangladesh 36 8 22.2 28 77.8 0.017 − 0.007 0.294 2.155 80.557 0.448
3 Egypt 21 3 14.3 18 85.7 0.034 0.002 0.314 2.855 72.840 0.555
4 Indonesia 104 11 10.6 93 89.4 0.003 − 0.007 0.013 2.387 59.263 0.643
5 Iraq 30 16 53.3 14 46.7 0.039 0.000 − 0.004 4.097 83.333 0.647
6 Jordan 19 5 26.3 14 73.7 0.015 0.001 1.102 2.740 70.680 0.389
7 Kenya 30 3 10.0 27 90.0 0.021 − 0.002 0.232 1.556 80.867 0.474
8 Kuwait 15 10 66.7 5 33.3 0.021 0.001 1.416 5.601 77.470 0.673
9 Malaysia 42 18 42.9 24 57.1 0.010 0.001 0.389 3.018 59.570 0.418
10 Oman 9 2 22.2 7 77.8 0.002 − 0.004 0.421 5.241 86.420 0.667
11 Pakistan 31 9 29.0 22 71.0 − 0.019 − 0.012 − 0.140 3.201 65.280 0.613
12 Palestine 5 2 40.0 3 60.0 0.035 0.001 2.064 3.901 86.423 0.603
13 Qatar 10 5 50.0 5 50.0 0.012 0.001 1.383 6.633 77.160 0.693
14 Saudi
Arabia
13 5 38.5 8 61.5 0.034 0.001 1.531 4.925 72.840 0.562
15 Syria 14 3 21.4 11 78.6 0.083 − 0.013 0.319 0.295 67.130 0.565
16 Turkey 47 3 6.4 44 93.6 − 0.012 − 0.002 − 0.001 4.750 69.910 0.585
17 UAE 22 6 27.3 16 72.7 0.015 − 0.002 0.560 5.014 69.137 0.536
All 461 117 25.4 344 74.6 0.012 − 0.004 0.342 3.333 69.778 0.570
N. Boubakri et al.
9. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
9
period.
We also consider how the crisis affected credit growth notwithstanding the bank business orientation by estimating Eq (1) after
removing Country*Year-Quarter fixed effects. In this model, we include both dummies Crisis and IB. The significant negative co
efficients on Crisis, reported in Columns 4–6, establish the adverse impact of the COVID-19 outbreak on bank lending. However, the
significant positive coefficients on the interaction terms between Crisis and IB show that IBs were more resilient to such adverse
impacts, further supporting our original findings in Table 3.
4.2. Sensitivity tests
So far, we have observed the resilience of IBs during the COVID-19 outbreak for loan growth, which is in line with our first hy
Fig. 1. Change in bank loan growth (as measured by g(L)) between crisis (average 2020Q1-2020Q3) and pre-crisis (average 2019Q1-2019Q4)
periods: Islamic vs conventional banks.
N. Boubakri et al.
10. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
10
pothesis. We do several sensitivity tests to ensure our primary finding is robust in this section.8
Results of six robustness tests are
reported in Table 4 Panels A, B, and C when the dependent variable is g(L), Δ(LTA), and Δ(LTGDP), respectively.
Our baseline specification includes standard errors clustered at the bank level. We employ-two alternative empirical approaches to
this clustering. First, a general preference is a cluster at the higher level of aggregation at the country level. Second, we use Weighted
Least Square (WLS) regression as an alternative to clustering with the number of banks in each country as weights. Columns 1 and 2 in
Table 4 report both results. The estimation results with the country-level clustering and WLS are consistent with the main results. IBs
fared better during the COVID-19 crisis period in terms of credit growth.
Third, a reasonable concern is that our choice of measure drives the results for loan growth. We use the data for gross loans in the
baseline to construct our dependent variables. We now use net loans, defined as gross loans minus allowances for loan losses, to
formulate bank loan growth. While a bank’s liquidity can be viewed as the value of its gross loans, net loans represent the actual
performing loans. Therefore, we check whether this alternative measure corroborates our findings. The results are reported in Column
3. We again find that IBs sustain their lending during the current health crisis compared to CBs.
Fourth, we consider an alternative time horizon for the dependent variables. We used three periods for pre-crisis and three periods
during the crisis in the baseline. We aimed to rule out any misrepresentation from possible survival bias by extending the pre-crisis to
2017 Q4 (the past available year data). The reported results in Column 4 confirm the primary finding that Islamic banks are more
resilient.
Fifth, our findings remain mostly robust to using the lag of control variables in Column 5, Panels A and C. Using the lagged variable
can mitigate the potential concern about the reverse causality between bank balance sheet variables and bank credit growth.
Finally, while we acknowledge that we control for a range of bank-level variables, some unobservable factors could explain our
primary finding of less vulnerability of IBs during the COVID-19 pandemic. We now attempt to control for additional bank-level
control variables to address the concern that our findings may be biased due to omitted variables. We add to the model the
following four bank-specific variables. (i) Z-score (ZSCORE), computed as Zscore = ROA+ETA
sigROA , where ROA is the return on assets, ETA is
Table 3
Bank lending during the COVID-19 pandemic: Islamic banks vs conventional banks. Baseline results This table reports the results esti
mating gict = β0 +β1.Crisist × IBic +δ.Xict +φi +φct +εict where i, c, and t denote bank i, country c and quarter t. gict is, alternatively, i)
growth in total outstanding loans: g(L), (ii) change in total outstanding loans to total assets ratio: Δ(LTA), and (iii) change in total
outstanding loans as a share of GDP: Δ(LTGDP), from quarter t − 1 to quarter t. Crisist is a dummy variable that takes value 1 during the
COVID-19 pandemic (2020Q1-2020Q3) and zero before the pandemic (2019Q2-2019Q4). IBic is a dummy variable that takes value 1 if
bank i domiciled in country c is an Islamic bank, and zero otherwise. Xict is a vector of bank-specific variables that may explain the lending
behavior of banks. We include bank fixed effects (φi) and country-year/quarter fixed effects (φct) in all regressions. See Table 1 for a
detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the
bank level (associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1 %, 5 %, and 10 % levels,
respectively. Our sample includes 461 banks (out of which 117 are IBs) in 17 countries over the period 2019Q2-2020Q3.
g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3)
Crisist × IBic 0.025** 0.013*** 0.557**
(2.037) (2.883) (2.567)
Sizeict 0.105*** − 0.135*** 0.771*
(2.744) (-7.063) (1.854)
C: Equityict 0.197 0.172** 0.805
(1.581) (2.184) (1.059)
A: LoanLossict − 1.004 − 0.223 − 1.585
(-1.369) (-1.219) (-0.439)
M: Costict − 0.030 − 0.010 0.011
(-1.030) (-0.918) (0.098)
E: ROAict − 0.139 − 0.041 1.627
(-0.385) (-0.282) (0.431)
L: Liquidityict − 0.014 − 0.017** − 0.162
(-0.959) (-2.018) (-1.194)
Constant − 1.829*** 2.341*** − 10.355
(-2.733) (6.939) (-1.376)
Bank FEs Y Y Y
Country*Year-Quarter FEs Y Y Y
# Countries 17 17 17
# Banks 461 461 461
N 2,104 2,104 2,104
Adj. R2
0.331 0.060 0.269
8
Arguably, the financial sectors of Iraq, Palestine, and Syria, which experienced war in recent years, are unstable and do not function properly.
Including bank data from these countries in our empirical analysis could bias our findings. To address this concern, we rerun the regressions in
baseline Table 3 after dropping bank data from each country separately, any pairwise combination, and all three countries. Our preliminary results
remain unchanged. For brevity, we only present the results in Appendix Table A3 after excluding all three countries.
N. Boubakri et al.
11. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
11
Table 4
Sensitivity tests.
4A: g(L)
Cluster at country WLS Net loans Since 2017Q4 Lag controls Other controls
(1) (2) (3) (4) (5) (6)
Crisist × IBic 0.025* 0.038*** 0.024** 0.017* 0.036*** 0.020*
(1.897) (2.620) (2.055) (1.865) (3.499) (1.731)
Sizeict 0.105* 0.056 0.132*** 0.050 − 0.153*** 0.107***
(1.938) (0.689) (3.276) (1.273) (-2.711) (2.678)
C: Equityict 0.197 − 0.943 0.175 0.368*** 0.498** 0.235*
(1.154) (-1.176) (1.391) (3.372) (2.253) (1.682)
A: LoanLossict − 1.004 − 0.582 − 1.597** − 0.873* − 1.179* − 0.533
(-1.297) (-0.387) (-2.207) (-1.764) (-1.687) (-0.946)
M: Costict − 0.030 − 0.023 − 0.026 − 0.028** − 0.054* − 0.041
(-0.960) (-0.543) (-0.865) (-2.434) (-1.773) (-1.382)
E: ROAict − 0.139 1.542* − 0.162 − 0.280 − 0.380 0.177
(-0.333) (1.847) (-0.440) (-0.907) (-0.871) (0.463)
L: Liquidityict − 0.014 − 0.041 − 0.013 − 0.020* 0.047 − 0.037**
(-0.966) (-1.368) (-0.812) (-1.905) (1.473) (-2.072)
Other controlsict
(ZSCORE, AssetDiversification, FeeIncome,
WholesaleFunding)
– – – – – √
Constant − 1.829* − 0.843 − 2.315*** − 0.851 2.641*** − 1.843***
(-1.933) (-0.572) (-3.277) (-1.238) (2.648) (-2.656)
Bank FEs Y Y Y Y Y Y
Country*Year-Quarter FEs Y Y Y Y Y Y
# Countries 17 17 17 17 17 17
# Banks 461 461 461 461 461 461
N 2,104 2,104 2,104 3,848 2,114 2,074
Adj. R2
0.331 0.322 0.339 0.277 0.369 0.359
4B:Δ(LTA)
Table 4B: Sensitivity tests
Cluster at country WLS Net loans Since 2017Q4 Lag controls Other controls
(1) (2) (3) (4) (5) (6)
Crisist x IBic 0.013*** 0.015** 0.035*** 0.010*** 0.007 0.012**
(4.676) (2.563) (3.013) (2.784) (1.359) (2.560)
Sizeict − 0.135*** − 0.211*** − 0.268*** − 0.080*** 0.132*** − 0.123***
(-14.442) (-5.312) (-5.923) (-6.872) (5.384) (-6.658)
C: Equityict 0.172*** 0.378* 0.481*** 0.165*** − 0.079 0.186***
(3.949) (1.792) (3.080) (3.503) (-1.193) (2.600)
A: LoanLossict − 0.223* 0.264 − 1.533** − 0.108 − 0.067 − 0.326
(-1.771) (0.954) (-2.087) (-0.780) (-0.397) (-1.552)
M: Costict − 0.010** − 0.009 − 0.031 − 0.007 − 0.004 − 0.010
(-2.658) (-0.756) (-1.217) (-1.301) (-0.630) (-0.871)
E: ROAict − 0.041 0.001 − 0.444 − 0.109 − 0.048 − 0.152
(-0.596) (0.005) (-1.054) (-1.063) (-0.383) (-0.951)
L: Liquidityict − 0.017 − 0.081*** − 0.026 − 0.023*** 0.033** − 0.024***
(-1.676) (-4.086) (-1.430) (-3.192) (2.230) (-2.705)
Other controlsict
(ZSCORE, AssetDiversification, FeeIncome,
WholesaleFunding)
– – – – – √
Constant 2.341*** 3.652*** 4.631*** 1.395*** − 2.299*** 2.205***
(14.533) (5.165) (5.849) (6.781) (-5.372) (6.748)
Bank FEs Y Y Y Y Y Y
Country*Year-Quarter FEs Y Y Y Y Y Y
# Countries 17 17 17 17 17 17
# Banks 461 461 461 461 461 461
N 2,104 2,104 2,104 3,848 2,114 2,074
Adj. R2
0.060 0.203 0.173 0.069 0.070 0.087
4C:Δ(LTGDP)
Table 4C: Sensitivity tests
Cluster at country WLS Net loans Since 2017Q4 Lag controls Other controls
(1) (2) (3) (4) (5) (6)
Crisist x IBic 0.557* 1.240* 0.495*** 0.447** 0.537** 0.511**
(2.052) (1.745) (2.738) (2.540) (2.491) (2.293)
Sizeict 0.771 3.291 0.812** 0.879*** − 1.728*** 1.102**
(continued on next page)
N. Boubakri et al.
12. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
12
the equity to asset ratio, and SigROA is the standard deviation of ROA over a five-quarter window. When facing adverse shocks, solvent
banks are in a better position to absorb such shocks, and thus, it is natural that solvent banks maintain their lending during a crisis.
Following Boubakri et al. (2017), we include two diversification variables: (ii) asset diversification (AssetDiversification) as measured
Table 4 (continued)
4C:Δ(LTGDP)
Table 4C: Sensitivity tests
(1.485) (0.831) (2.265) (3.485) (-2.609) (2.534)
C: Equityict 0.805 10.356 0.810 1.370* 0.275 0.723
(1.239) (0.528) (1.168) (1.705) (0.220) (0.899)
A: LoanLossict − 1.585 23.595 − 4.727 − 1.177 − 2.397 − 2.572
(-0.697) (0.825) (-1.445) (-0.417) (-0.749) (-0.719)
M: Costict 0.011 − 1.085 0.078 − 0.027 0.023 0.038
(0.090) (-1.174) (0.808) (-0.444) (0.218) (0.279)
E: ROAict 1.627 50.329** 3.320 − 0.462 1.115 3.404
(0.388) (2.024) (1.151) (-0.191) (0.308) (0.831)
L: Liquidityict − 0.162 − 1.682 − 0.161 − 0.212* 0.264 − 0.318*
(-1.088) (-1.265) (-1.170) (-1.775) (1.161) (-1.708)
Other controlsict
(ZSCORE, AssetDiversification, FeeIncome,
WholesaleFunding)
– – – – – √
Constant − 10.355 − 55.849 − 12.628** − 10.575** 32.966*** − 15.111*
(-1.143) (-0.796) (-1.985) (-2.314) (2.814) (-1.945)
Bank FEs Y Y Y Y Y Y
Country*Year-Quarter FEs Y Y Y Y Y Y
# Countries 17 17 17 17 17 17
# Banks 461 461 461 461 461 461
N 2,104 2,104 2,104 3,848 2,114 2,074
Adj. R2
0.269 0.417 0.307 0.238 0.265 0.277
Table 5
Robust to splitting the sample to pre-crisis and crisis periods. This table reports the results estimating gict = β0 +β1.IBic +δ.Xict +φct +εict where i, c, and
t denote bank i, country c and quarter t. gict is, alternatively, i) growth in total outstanding loans: g(L), (ii) change in total outstanding loans to total
assets ratio: Δ(LTA), and (iii) change in total outstanding loans as a share of GDP: Δ(LTGDP), from quarter t − 1 to quarter t. IBic is a dummy variable
that takes value 1 if bank i domiciled in country c is an Islamic bank, and zero otherwise. Xict is a vector of bank-specific variables that may explain the
lending behavior of banks. We include country-year/quarter fixed effects (φct) in all regressions. See Table 1 for a detailed definition of variables.
Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the bank level (associated t-values reported in
parentheses). ***, **, and * denote statistical significance at the 1 %, 5 %, and 10 % levels, respectively. Our sample includes 461 banks (out of which
117 are IBs) in 17 countries over 2019Q2-2020Q3.
Splitting sample to
Pre-crisis (2019Q2-2019Q4) Crisis (2020Q1-2020Q3)
g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3) (4) (5) (6)
IBic − 0.002 − 0.004 − 0.033 0.027*** 0.005* 0.482***
(-0.222) (-1.518) (-0.244) (3.634) (1.934) (2.684)
Sizeict − 0.000 − 0.002** 0.252*** 0.003 0.000 0.099**
(-0.115) (-2.077) (5.570) (1.211) (0.543) (2.337)
C: Equityict 0.030 − 0.021* 0.435 − 0.058 0.017 − 0.471
(0.594) (-1.688) (0.928) (-1.071) (1.064) (-0.924)
A: LoanLossict − 0.222 − 0.208** − 6.029 − 0.758** − 0.107 − 5.472
(-0.459) (-2.271) (-1.307) (-2.312) (-0.988) (-1.620)
M: Costict − 0.015 − 0.003 − 0.027 0.010 0.006 − 0.032
(-1.049) (-0.692) (-0.215) (0.576) (1.176) (-0.209)
E: ROAict 0.140 0.130 1.759 0.033 − 0.097 − 1.300
(0.432) (1.614) (0.527) (0.150) (-1.432) (-0.341)
L: Liquidityict − 0.012 − 0.003 0.008 0.008 − 0.004 0.069
(-0.786) (-0.855) (0.103) (0.866) (-1.145) (0.890)
Constant 0.031 0.036* − 1.888 − 0.054 − 0.020 − 1.661**
(0.472) (1.663) (-1.245) (-1.110) (-1.145) (-2.072)
Bank FEs N N N N N N
Country*Year-Quarter FEs Y Y Y Y Y Y
# Countries 17 17 17 17 17 17
# Banks 461 461 461 461 461 461
N 1,093 1,093 1,093 1,011 1,011 1,011
Adj. R2
0.095 0.046 0.243 0.425 0.083 0.217
N. Boubakri et al.
13. Journal
of
International
Financial
Markets,
Institutions
&
Money
84
(2023)
101743
13
Table 6
Robust to the severity of the crisis. This table reports the results estimating gict = β0 +β1.Severity crisisct × IBic +δ.Xict +φi +φct +εict where i, c, and t denote bank i, country c and quarter t. gict is,
alternatively, i) growth in total outstanding loans: g(L), (ii) change in total outstanding loans to total assets ratio: Δ(LTA), and (iii) change in total outstanding loans as a share of GDP: Δ(LTGDP), from
quarter t − 1 to quarter t. Severity crisisct is a proxy for severity of the COVID-19 pandemic in country c in quarter t. IBic is a dummy variable that takes value 1 if bank i domiciled in country c is an Islamic
bank, and zero otherwise. Xict is a vector of bank-specific variables that may explain the lending behavior of banks. We include bank fixed effects (φi) and country-year/quarter fixed effects (φct) in all
regressions. See Table 1 for a detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the bank level (associated t-values
reported in parentheses). ***, **, and * denote statistical significance at the 1 %, 5 %, and 10 % levels, respectively. Our sample includes 461 banks (out of which 117 are IBs) in 17 countries over 2019Q2-
2020Q3.
Cases per 100,000 population Oxford stringency index Risk of openness index
g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3) (4) (5) (6) (7) (8) (9)
Severity_crisisct × IBic 0.003 0.001 0.053 0.0003* 0.0002*** 0.009** 0.037* 0.018** 0.722*
(1.242) (0.731) (1.125) (1.747) (2.785) (2.564) (1.682) (2.485) (1.887)
Sizeict 0.109*** − 0.132*** 0.868** 0.106*** − 0.135*** 0.750* 0.106*** − 0.135*** 0.805*
(2.870) (-7.015) (2.045) (2.760) (-7.068) (1.820) (2.771) (-7.068) (1.915)
C: Equityict 0.193 0.170** 0.700 0.196 0.172** 0.796 0.197 0.172** 0.781
(1.545) (2.167) (0.905) (1.570) (2.170) (1.062) (1.577) (2.178) (1.019)
A: LoanLossict − 1.019 − 0.232 − 1.943 − 1.020 − 0.231 − 1.915 − 1.002 − 0.222 − 1.597
(-1.385) (-1.272) (-0.528) (-1.392) (-1.268) (-0.535) (-1.372) (-1.216) (-0.448)
M: Costict − 0.030 − 0.010 0.027 − 0.030 − 0.010 0.008 − 0.030 − 0.010 0.028
(-1.018) (-0.888) (0.237) (-1.031) (-0.921) (0.071) (-1.008) (-0.882) (0.241)
E: ROAict − 0.179 − 0.064 0.694 − 0.150 − 0.046 1.562 − 0.146 − 0.046 1.352
(-0.494) (-0.444) (0.176) (-0.415) (-0.319) (0.421) (-0.407) (-0.316) (0.355)
L: Liquidityict − 0.015 − 0.018** − 0.196 − 0.015 − 0.018** − 0.168 − 0.014 − 0.018** − 0.175
(-1.030) (-2.100) (-1.403) (-0.991) (-2.037) (-1.219) (-0.973) (-2.029) (-1.268)
Constant − 1.889*** 2.285*** − 12.009 − 1.839*** 2.337*** − 9.986 − 1.837*** 2.332*** − 10.921
(-2.857) (6.888) (-1.568) (-2.749) (6.942) (-1.338) (-2.757) (6.946) (-1.438)
Bank FEs Y Y Y Y Y Y Y Y Y
Country*Year-Quarter FEs Y Y Y Y Y Y Y Y Y
# Countries 17 17 17 17 17 17 17 17 17
# Banks 461 461 461 461 461 461 461 461 461
N 2,104 2,104 2,104 2,104 2,104 2,104 2,104 2,104 2,104
Adj. R2
0.329 0.055 0.266 0.330 0.060 0.270 0.330 0.059 0.268
N.
Boubakri
et
al.
14. Journal
of
International
Financial
Markets,
Institutions
&
Money
84
(2023)
101743
14
Table 7
Role of the pre-crisis usage of macroprudential measures This table reports the results estimating ΔLoanict = β0 +β1.Crisist × IBic +δ.Xict +φi +φct +εict where i, c, and t denote bank i, country c and quarter
t. Each panel displays the results obtained by running the regression in a subsample determined by the median value of pre-crisis macroprudential measures proxy. gict is, alternatively, i) growth in total
outstanding loans: g(L), (ii) change in total outstanding loans to total assets ratio: Δ(LTA), and (iii) change in total outstanding loans as a share of GDP: Δ(LTGDP), from quarter t − 1 to quarter t. Crisist is a
dummy variable that takes value 1 during the COVID-19 pandemic (2020Q1-2020Q3) and zero before the pandemic (2019Q2-2019Q4). IBic is a dummy variable that takes value 1 if bank i domiciled in
country c is an Islamic bank, and zero otherwise. Xict is a vector of bank-specific variables that may explain the lending behavior of banks. We include bank fixed effects (φi) and country-year/quarter fixed
effects (φct) in all regressions. See Table 1 for a detailed definition of variables. Regressions are estimated using OLS. The statistical inferences are based on clustered standard errors at the bank level
(associated t-values reported in parentheses). ***, **, and * denote statistical significance at the 1 %, 5 %, and 10 % levels, respectively. Our sample includes 461 banks (out of which 117 are IBs) in 17
countries over 2019Q2-2020Q3.
MPI_Total MPI_Finance MPI_Borrower
Countries with low usage Countries with high usage Countries with low usage Countries with high usage Countries with low usage Countries with high usage
g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18)
Crisist × IBic 0.025 0.009 − 0.069 0.022* 0.014*** 0.745*** 0.035* 0.009 0.549 0.016 0.015*** 0.541** − 0.001 0.003 − 0.125 0.022** 0.013*** 0.582***
(0.913) (0.980) (-0.154) (1.761) (2.800) (2.878) (1.665) (1.077) (1.241) (1.219) (2.626) (2.139) (-0.018) (0.297) (-0.079) (2.035) (2.743) (2.599)
Sizeict − 0.097 − 0.141*** 0.580 0.139*** − 0.146*** 0.745 − 0.076 − 0.137*** 0.965 0.142*** − 0.145*** 0.601 0.364 0.038 15.542 0.104*** − 0.140*** 0.502
(-0.961) (-3.819) (0.515) (3.532) (-7.164) (1.597) (-0.884) (-4.013) (0.804) (3.462) (-6.912) (1.424) (1.692) (0.290) (1.630) (2.669) (-7.183) (1.359)
C: Equityict 0.013 0.058 − 0.184 0.196 0.198** 0.822 − 0.052 0.073 − 0.992 0.221* 0.196** 1.031 0.225 0.144 33.005 0.192 0.180** 1.011
(0.034) (0.640) (-0.086) (1.553) (2.329) (0.915) (-0.152) (0.821) (-0.450) (1.722) (2.303) (1.165) (0.162) (0.276) (1.334) (1.550) (2.301) (1.399)
A: LoanLossict 2.225 0.562 5.414 − 1.516* − 0.381** − 0.508 2.014 0.397 5.476 − 1.579* − 0.339* − 1.520 9.827*** 1.444** 6.979 − 1.397** − 0.271 − 1.405
(1.388) (1.372) (0.331) (-1.932) (-1.972) (-0.143) (1.384) (1.039) (0.370) (-1.963) (-1.728) (-0.434) (3.937) (2.363) (0.228) (-2.019) (-1.494) (-0.390)
M: Costict − 0.017 − 0.008 1.110 − 0.029 − 0.010 − 0.004 0.000 0.013 0.893 − 0.030 − 0.011 − 0.034 − 0.188 − 0.023 3.975 − 0.029 − 0.010 − 0.027
(-0.238) (-0.243) (0.892) (-0.928) (-0.885) (-0.037) (0.005) (0.438) (0.943) (-0.977) (-1.002) (-0.303) (-0.938) (-0.349) (0.824) (-0.978) (-0.905) (-0.232)
E: ROAict 2.718 0.711 36.384* − 0.304 − 0.081 0.671 2.451 0.765* 29.682 − 0.306 − 0.083 − 0.156 0.194 − 0.288 26.370 − 0.199 − 0.037 0.461
(1.445) (1.327) (1.754) (-0.814) (-0.556) (0.184) (1.611) (1.730) (1.505) (-0.814) (-0.580) (-0.042) (0.109) (-0.586) (0.493) (-0.532) (-0.249) (0.129)
L: Liquidityict − 0.043 − 0.036*** − 0.357 − 0.008 − 0.012 − 0.111 − 0.039 − 0.037*** − 0.250 − 0.009 − 0.011 − 0.123 − 0.091 − 0.042 − 1.428 − 0.010 − 0.016* − 0.142
(-1.482) (-5.615) (-0.922) (-0.543) (-1.360) (-0.914) (-1.302) (-5.188) (-0.653) (-0.619) (-1.341) (-1.012) (-0.727) (-1.510) (-1.109) (-0.762) (-1.864) (-1.053)
Constant 1.683 2.441*** − 7.933 − 2.104*** 2.208*** − 11.185 1.314 2.368*** − 14.295 − 2.154*** 2.196*** − 8.973 − 6.318* − 0.648 − 272.633 − 1.806*** 2.423*** − 5.639
(0.954) (3.783) (-0.399) (-3.454) (7.050) (-1.563) (0.886) (3.967) (-0.675) (-3.389) (6.805) (-1.388) (-1.701) (-0.280) (-1.653) (-2.659) (7.067) (-0.836)
Bank FEs Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
Country*Year-
Quarter
FEs
Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y Y
# Countries 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17
# Banks
N 541 541 541 1,563 1,563 1,563 643 643 643 1,461 1,461 1,461 135 135 135 1,969 1,969 1,969
Adj. R2
0.240 0.047 0.160 0.378 0.070 0.307 0.226 0.041 0.132 0.389 0.071 0.334 0.607 0.051 0.149 0.336 0.068 0.274
N.
Boubakri
et
al.
15. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
15
by 1-|(Net loans – Other earning assets)/(Total earning assets)| and (iii) fee income (FeeIncome) measured as the ratio of fee and other
operating income to total assets. It is argued that diversified banks (both in terms of assets and income) generate more profits. Hence,
these banks are more resilient to financial instability and more willing to allocate funds (Elsas et al., 2010). (iv) Wholesale funding
(WholesaleFunding), defined as bank short-term funding to total assets ratio. We capture the bank’s liabilities structure, which may
affect its stability and credit growth (Craig and Dinger, 2013; Zheng, 2020). Ippolito et al. (2016) find that banks reliant on wholesale
funds reduce their loans more than banks reliant on core deposits during a crisis. The estimation results with these additional control
variables are reported in Column 6. They show that the baseline results that loan growth in IBs is higher than CBs during the COVID-19
crisis remain unchanged.
We next employ an alternative model by excluding the Crisis dummy from the model and splitting the sample into the pre-crisis
versus crisis periods. Table 5 shows the results where Columns 1–3 report results when running regression for the pre-crisis period,
and Columns 4–6 report results during the crisis period. The dummy IB is statistically significant during the crisis but insignificant in
pre-crisis. These results also reinforce our main finding that IBs were more resilient during the early stage of the COVID-19 pandemic.
Another robustness test is conducted to ascertain the baseline results in Table 3. Not all countries were affected in the same manner
by the COVID-19 pandemic. For example, while Bahrain was affected more severely by the crisis, Kenya was affected slightly. We show
that the resilience of IBs during the COVID-19 outbreak for bank lending is more pronounced in countries critically affected by the
pandemic. We assert that IBs’ resilience in maintaining their credit growth during the health crisis is evident in countries more severely
affected by the crisis. We apply a form of Eq. (1) where our interest variable is an interaction term between the severity of the COVID-
19 crisis and Islamic bank dummy (Severity crisis × IB). We use three proxies for the severity of the COVID-19 pandemic. (i) We employ
the number of COVID-19 cases per 100,000 people in each country. The data are from the John Hopkins University dataset. (ii) The
second proxy is the stringency of COVID-19, a country-level severity of the lockdown measures in response to the pandemic. This
composite measure is based on nine response indicators, including school closures, workplace closures, and travel bans (Hale et al.,
2020). (iii) The third proxy is the risk of openness index. It calculates a country’s risk from adopting an ’open’ policy stance. The data
for the last two proxies are from the Oxford University dataset. Columns 1–3, 4–6, and 7–9 in Table 6 report the results on bank lending
of IBs in countries that were affected severely by the COVID-19 crisis where the severity is the number of cases, stringency index, or risk
of openness index, respectively. We find that IBs in countries significantly hit by the COVID-19 crisis performed relatively better than
CBs, only when severity is measured by the stringency index or the risk of openness index.
4.3. Role of pre-crisis usage of macroprudential measures
We finally check our section hypothesis that using macroprudential tools in pre-pandemic may affect the performance of banks
during the pandemic. Previous studies (e.g., Abedifar et al., 2013; Bilgin et al., 2021) find that country-level variables may shape the
performance of IBs versus CBs.
We consider the impact of macro-prudential policies on the lending behavior of IBs versus CBs during the COVID-19 pandemic.
Some studies investigate the mitigating role of macro-prudential policies in the aftermath of the current health crisis (Igan et al., 2022).
Following the classification proposed by Cerutti et al. (2017), we account for macro-prudential policies using a total index measure
(MPI_Total), a financial institution-based index (MPI_Finance), aimed at improving the liquidity position of banks, and a borrower-
based index (MPI_Borrower), aimed at controlling the borrowers’ leverage and financial positions. MPI Borrower covers i) loan-to-
value ratio (LTV), and ii) debt-to-income ratio (DTI). MPI Finance covers: i) limits on foreign currency loans, ii) limits on domestic
currency loans, iii) reserve requirement ratio, iv) limits on interbank exposures, v) countercyclical capital-buffer requirement, vi)
dynamic loan loss provisioning, vii) leverage ratio for the bank, viii) capital surcharges on systematically important financial in
stitutions, ix) concentration limits, and x) tax on financial institutions. For a given country, the value of the MPI Borrower variable is
between 0 and 2. Similarly, the value of the MPI Finance variable ranges from 0 to 10, and the value of the total variable (MPI Total)
from 0 to 12. A yearly dummy variable is designated a value of unity if the tool was activated (or was in place) and zero otherwise
(Cerutti et al., 2017).
Using the data for macro-prudential tools in the latest available year, 2017, we split the sample countries into a high or low category
based on their macro-prudential usage. We assign countries to the high macro-prudential usage category if their macro-prudential
indicator ranks above the cross-country median. Similarly, countries with macro-prudential indicators falling below the cross-
country median are grouped in the low macro-prudential usage category. Our cross-country median of MPI Total is 4.73, with a
minimum of 1 (Kenya) and a maximum of 8 (Turkey). Regarding MPI Borrower, the sample median is 1.33, with a range of 0 to 2.
Finally, the sample median of MPI Finance is 3.4, with the lowest score at 0 and the highest at 6.
The results are presented in Table 7. We find that IBs extended more loans than CBs during the COVID-19 crisis only in countries
that activated macroprudential policies, i.e., countries with high macro-prudential usage (either in terms of MPI_Total, MPI_Finance, or
MPI_Borrower) in the year approaching the health crisis. There is no significant difference in the lending behavior between Islamic and
conventional banks during the crisis period in countries with low macro-prudential usage. This finding is consistent across all three
macro-prudential indicators, except for the results in Column 7, as reported in Columns 1–3, 8–9, and 13–15, respectively. The re
ported results highlight the importance of macro-prudential policies in supporting the differential ability of IBs to sustain credit growth
during bad times over their counterparts.
5. Conclusion
Adherence to Islamic principles makes IBs blend concepts of moral and social values with banking transactions. Enshrined in
N. Boubakri et al.
16. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
16
Shariah, Islamic values condemn interest rates or excessive risk as unethical and advocate fair banking, as exemplified in profit-loss
sharing contracts. The salient feature of aligning business transactions with Shariah principles is that it acts as an additional layer
of governance on top of any other regulations and has proven beneficial to IBs, especially in times of crisis. Supported by empirical
findings, previous studies that compare IBs to their counterparts acknowledge that the IBs are more stable during stressful periods such
as the recent GFC. Our findings lend further support to the resilience of IBs during challenging times. The reported evidence shows that
credit grew 2.5 % faster for IBs compared to CBs during the initial phase of the COVID-19 crisis period.
We further show that our primary finding of loan growth sustainability for IBs during the COVID-19 pandemic is more robust in
countries where regulators were more active in utilizing macroprudential policies in the pre-COVID-19 pandemic. These observations
imply that the lending behavior of IBs during downturns was shaped by bank regulations in the years preceding the crisis.
Our results on the determinants of bank resilience during downturns, especially in dual banking systems where IBs compete with
CBs, are essential given the central role of bank resilience in financial stability and economic growth. Our evidence also sheds light on
how banking principles affect bank resilience. We add to the literature on Islamic banking by showing that IBs were more resilient than
CBs during the COVID-19 period. In addition, by documenting that this ability to sustain credit growth depends on macroprudential
policies, we contribute to the literature on the importance of such policies to financial stability in general.
CRediT authorship contribution statement
Narjess Boubakri: Writing – original draft, Writing – review & editing. Ali Mirzaei: Conceptualization, Methodology, Software,
Data curation, Investigation. Mohsen Saad: Writing – original draft, Writing – review & editing.
Table A1
Change in bank distance to default (DTD) and probability of default (PD) from pre-crisis to the crisis: Islamic vs conventional banks.
IBs = 43 CBs = 145
Pre-crisis 2019Q4 Crisis 2020Q3 Diff. Pre-crisis 2019Q4 Crisis 2020Q3 Diff.
Risk indicator (1) (2) (3)= (2)-(1) (4) (5) (6)= (5)-(4)
DTD 2.391 1.896 − 0.495 2.137 1.529 − 0.608
PD (1 month) 0.00025 0.00033 0.00008 0.00031 0.00043 0.00012
PD (3 months) 0.00080 0.00102 0.00022 0.00100 0.00136 0.00036
PD (12 months) 0.00371 0.00445 0.00074 0.00469 0.00593 0.00124
Table A2
Bank lending during the COVID-19 pandemic: Islamic banks vs conventional banks.
g(L) Δ(LTA) Δ(LTGDP) g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3) (4) (5) (6)
Crisist − 0.031*** − 0.007*** − 0.275***
(-7.526) (-3.880) (-3.549)
IBic − 0.003 − 0.004* − 0.074 − 0.006 − 0.005** 0.035
(-0.346) (-1.742) (-0.544) (-0.839) (-2.318) (0.279)
Crisist × IBic 0.031*** 0.010*** 0.604*** 0.041*** 0.012*** 0.433***
(2.790) (2.697) (3.208) (3.443) (3.267) (2.652)
Sizeict 0.001 − 0.000 0.180*** 0.002 − 0.000 0.184***
(0.591) (-0.870) (5.510) (0.854) (-0.448) (5.794)
C: Equityict − 0.007 0.004 0.067 − 0.008 0.005 0.041
(-0.261) (0.414) (0.179) (-0.302) (0.613) (0.118)
A: LoanLossict − 0.475* − 0.172** − 6.324* − 0.331 − 0.241*** − 2.549
(-1.818) (-2.143) (-1.867) (-1.295) (-3.220) (-1.102)
M: Costict − 0.005 0.001 − 0.057 0.004 − 0.003 0.044
(-0.493) (0.178) (-0.526) (0.351) (-0.727) (0.474)
E: ROAict 0.090 − 0.016 − 0.315 0.206 − 0.173*** 1.276
(0.558) (-0.290) (-0.119) (1.232) (-3.504) (0.990)
L: Liquidityict − 0.003 − 0.003 0.049 − 0.004 − 0.002 0.060
(-0.386) (-1.196) (0.859) (-0.503) (-0.945) (1.233)
Constant 0.005 0.012 − 0.628 − 0.018 0.011 − 1.582*
(0.128) (0.676) (-0.438) (-0.481) (0.849) (-1.836)
Country*Year-Quarter FEs Y Y Y N N N
Country FEs N N N Y Y Y
# Countries 17 17 17 17 17 17
# Banks 461 461 461 461 461 461
N 2,104 2,104 2,104 2,104 2,104 2,104
Adj. R2
0.281 0.064 0.230 0.057 0.026 0.110
N. Boubakri et al.
17. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
17
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data will be made available on request.
Appendix A
See Tables A1-A3
References
Abedifar, P., Molyneux, P., Tarazi, A., 2013. Risk in Islamic banking. Eur. Finan. Rev. 17, 2035–2096.
Acharya, V., and Steffen, S., 2020. ‘Stress Test’ for Banks as Liquidity Insurers in a time of COVID”, mimeo.
Acharya, V., Naqvi, H., 2012. The seeds of a crisis: a theory of bank liquidity and risk taking over the business cycle. J. Finan. Econ. 106, 349–366.
Alam, Z., Alter, A., Eiseman, J., Gelos, G., Kang, H., Narita, M., Nier, E., Wang, N., 2019. Digging deeper - evidence on the effects of macroprudential policies from a
new database. Int. Monetary Fund Working Paper 19/66.
Altavilla, C., Barbiero, F., Boucinha, M., Burlon, L., 2020. The great lockdown: pandemic response policies and bank lending conditions. Eur. Central Bank Working
Paper Series No 2465.
Altunbas, Y., Tommaso, C.D., Thornton, J., 2016. Do better-capitalized banks lend less? Evidence from European banks. Financ. Res. Lett. 17, 246–250.
Balvers, R.J., Gu, L., Huang, D., 2017. Profitability, Value, and Stock Returns in Production- Based Asset Pricing without Frictions. J. Money Credit Bank. 49,
1621–1651.
Beck, T., Demirgüç-Kunt, A., Merrouche, O., 2013. Islamic vs. conventional banking: business model, efficiency and stability. J. Bank. Financ. 37, 433–447.
Beltratti, A., Stulz, R.M., 2012. The credit crisis around the globe: why did some banks perform better? J. Financ. Econ. 105, 1–17.
Benetton, M., Fantino, D., 2018. Competition and the pass-through of unconventional monetary policy: evidence from TLTROs. Banca d’Italia Working Papers No
1187.
Bilgin, M.H., Danisman, G.O., Demir, E., Tarazi, A., 2021. Bank credit in uncertain times: Islamic vs. conventional banks. Financ. Res. Lett. 39, 101563.
Boeckx, J., de Sola Perea, M., Peersman, G., 2020. The transmission mechanism of credit support policies in the euro area. Eur. Econ. Rev. 124 (C).
Boubakri, N., Mirzaei, A., Samet, A., 2017. National culture and bank performance: evidence from the recent financial crisis. J. Financ. Stab. 29, 36–56.
Brei, M., Gambacorta, L., von Peter, G., 2013. Rescue packages and bank lending. J. Bank. Finan. 37, 490–505.
Brei, M., Schclarek, A., 2013. Public bank lending in times of crisis. J. Financ. Stab. 9, 820–830.
Bruno, V., Shim, I., Shin, H.S., 2017. Comparative assessment of macroprudential policies. J. Financ. Stab. 28, 183–202.
Cerutti, E., Claessens, S., Laeven, L., 2017. The use and effectiveness of macroprudential policies: new evidence. J. Financ. Stab. 28, 203–224.
Chen, Y.-S., Chen, Y., Lin, C.-Y., Sharma, Z., 2016. Is there a bright side to government banks? Evidence from the global financial crisis. J. Financ. Stab. 26, 128–143.
Claessens, S., Ghosh, S.R., Mihet, R., 2013. Macro-prudential policies to mitigate financial system vulnerabilities. J. Int. Money Financ. 39 (C), 153–185.
Cornett, M.M., McNutt, J.J., Strahan, P.E., Tehranian, H., 2011. Liquidity risk management and credit supply in the financial crisis. J. Financ. Econ. 101, 297–312.
Craig, B.R., Dinger, V., 2013. Deposit market competition, wholesale funding, and bank risk. J. Bank. Financ. 37, 3605–362.
Table A3
Bank lending during the COVID-19 pandemic: Islamic banks vs conventional banks. The sample set excludes Iraq, Palestine, and Syria.
g(L) Δ(LTA) Δ(LTGDP)
(1) (2) (3)
Crisist × IBic 0.021* 0.013*** 0.449**
(1.915) (2.809) (2.098)
Sizeict 0.114*** − 0.136*** 0.715*
(2.918) (-7.002) (1.656)
C: Equityict 0.029 0.178** 0.762
(0.215) (2.183) (0.945)
A: LoanLossict − 1.924* − 0.293 − 2.384
(-1.831) (-1.008) (-0.403)
M: Costict − 0.004 − 0.008 0.021
(-0.148) (-0.638) (0.142)
E: ROAict − 0.466 − 0.078 1.353
(-0.774) (-0.391) (0.280)
L: Liquidityict − 0.009 − 0.016* − 0.174
(-0.663) (-1.901) (-1.210)
Constant − 1.963*** 2.357*** − 9.375
(-2.889) (6.897) (-1.205)
Bank FEs Y Y Y
Country*Year-Quarter FEs N N N
# Countries 14 14 14
# Banks 412 412 412
N 1,968 1,968 1,968
Adj. R2
0.304 0.040 0.235
N. Boubakri et al.
18. Journal of International Financial Markets, Institutions & Money 84 (2023) 101743
18
Cull, R., Martinez-Peria, M.S., 2013. Bank ownership and lending patterns during the 2008–2009 financial crisis: evidence from Latin America and Eastern Europe.
J. Bank. Financ. 37, 4861–4878.
Demirgüç-Kunt, A., Detragiache, E., Merrouche, O., 2013. Bank capital: Lessons from the financial crisis. J. Money Credit Bank. 45, 1147–1164.
El Gamal, M.A., 2000. A Basic Guide to Contemporary Islamic Banking and Finance. Islamic Society of North America, Plainfield.
Elsas, R., Hackethal, A., Holzhauser, M., 2010. The Anatomy of Bank Diversification. J. Bank. Financ. 34, 1274–1287.
Farooq, M., Zaheer, S., 2015. Are Islamic banks more resilient during financial panics? Pac. Econ. Rev. 20, 101–124.
Galati, G., Moessner, R., 2018. What do we know about the effects of macroprudential policy? Economica 85, 735–770.
García-Suaza, A.F., Gómez-González, J.E., Pabón, A.M., Tenjo-Galarza, F., 2012. The cyclical behavior of bank capital buffers in an emerging economy: size does
matter? Econ. Model. 29, 1612–1617.
Gómez, E., Murcia, A., Lizarazo, A., Mendoza, J., 2020. Evaluating the impact of macroprudential policies on credit growth in Colombia. J. Financ. Intermed. 42,
10084.
Hale, T., Webster, S., Petherick, A., Phillips, T., Kira, B., 2020. Oxford Covid-19 government response tracker. Blavatnik School of Government. https://www.bsg.ox.
ac.uk/research/research-projects/oxford-covid-19-government-response-tracker.
Hasan, M., Dridi, J., 2011. The effects of the global crisis on Islamic and Conventional Banks: a comparative study. J. Int. Econ. Commer. Policy 2, 163–200.
Ibrahim, M.H., 2016. Business cycle and bank lending procyclicality in a dual banking system. Econ. Model. 55, 127–134.
Ibrahim, M.H., Rizvi, S.A.R., 2018. Bank lending, deposits and risk-taking in times of crisis: a panel analysis of Islamic and conventional banks. Emerg. Mark. Rev. 35,
31–47.
Igan, D., Mirzaei, A., 2020. Does going tough on banks make the going get tough? Bank liquidity regulations, capital requirements, and sectoral activity. J. Econ.
Behav. Organ. 177, 688–726.
Igan, D., Mirzaei, A., Tomoe, M., 2022. Does macroprudential policy alleviate the adverse impact of COVID-19 on the resilience of banks? J. Bank. Financ. In press.
Ippolito, F., Peydrò, J., Polo, A., Sette, E., 2016. Double bank runs and liquidity risk management. J. Financ. Econ. 122, 135.
Islamic Financial Services Board (IFSB), 2019. Islamic Financial Services Industry Stability Report, Kuala Lumpur, Malaysia: Islamic Financial Services Board.
Ivashina, V., Scharfstein, D., 2010. Bank lending during the financial crisis of 2008. J. Financ. Econ. 97 (3), 319–338.
Kapan, T., Minoiu, C., 2018. Balance sheet strength and bank lending: Evidence from the global financial crisis. J. Bank. Financ. 92, 35–50.
Kosak, M., Li, S., Loncarski, I., Marinc, M., 2015. Quality of bank capital and bank lending behaviour during the global financial crisis. Int. Rev. Financ. Anal. 37,
168–183.
Li, L., Strahan, P.E., Zhang, S., 2020. Banks as Lenders of First Resort: Evidence from the COVID-19 Crisis. Rev. Corp. Financ. Stud. 9 (3), 472–500.
Lim, C., Columba, F., Costa, A., Kongsamut, P., Otani, A., Saiyid, M., Wezel, T., Wu, X., 2011. Macroprudential policy: what instruments and how to use them? Lessons
from country experiences. Int. Monetary Fund Working Paper 11/238.
Mirzaei, A., Saad, M., Emrouznejad, A., 2022. Bank Stock Performance during the COVID-19 Crisis: Does efficiency explain why Islamic banks fared relatively better?
In Press, Annals of Operations Research.
Repullo, R., Saurina, J., 2011. The countercyclical capital buffer of Basel III: A critical assessment. CEPR Discussion Papers. CEPR Discussion Papers 8304.
Rostagno, M., Altavilla, C., Carboni, G., Lemke, W., Motto, R., Saint Guilhem, A., Yiangou, J., 2019. A tale of two decades: the ECB’s monetary policy at 20. Eur.
Central Bank Working Papers No 2346.
Stolz, S., Wedow, M., 2011. Banks’ regulatory capital buffer and the business cycle: evidence for Germany? J. Financ. Stability 7, 98–110.
Zheng, Y., 2020. Does bank opacity affect lending? J. Bank. Financ. 119, 105900.
N. Boubakri et al.
