risk and stability in islamic banking

Electronic copy available at: http://ssrn.com/abstract=1663406

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Risk and Stability in Islamic Banking

Pejman Abedifar*c, Amine Tarazi*, Philip Molyneux† * Université de Limoges, LAPE, 5 rue Félix Eboué, 87031 Limoges Cedex, France

† Bangor Business School, University of Wales, Bangor, LL57 2DG, UK C. Corresponding Author. E-mail: [email protected]

Preliminary draft: do not quote without the permission of the authors

NOVEMBER 2010

Abstract

This paper investigates risk and stability features of Islamic banking using a

simultaneous modeling framework and a sample of 456 banks from 22 countries

between 2001 and 2008. We find that Islamic banks appear to have similar credit and

insolvency risk features as conventional banks. Smaller Islamic banks, however, have

a lower exposure to credit risk than similar-sized conventional counterparts. We also

find evidence suggesting the limited role played by “profit-and-loss-sharing” (PLS)

contracts. Our results are robust to different samples, estimation procedures, risk and

other modeling specifications.

Key Words: Islamic Banking, Islamic Finance, bank risk, credit risk, stability,

insolvency, z-score


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Introduction

The world has observed various evolutionary stages in the field of banking and

currently we see that Islamic Banking is expanding fast. According to the Banker (2009)

report on the top 500 Islamic Financial Institutions, assets held by Islamic banks or

Islamic windows of conventional banks increased by 28.6% to $ 822bn from $639bn in

2008, while the annual asset growth of conventional banks was only 6.8%. Islamic

banking is not only practised in Islamic countries but it has also expanded to the other

continents like Europe and the Americas. Islamic finance and banking is a relatively new

area and studies on its impact, viability and overall role in the finanical markets is not

well documented in the academic literature.

Islamic finance has evolved on the basis of Shariá, which forbids payment or

receipt of Riba – the payment or receipt of interest (Obaidullah, 2005). Financing

principles are governed by Islamic rules on transactions “Figh Al-Muamelat” and follow

both profit and loss sharing (PLS) and non-PLS arrangements (such as leasing style

arrangements). In addition to being prohibited from dealing with any transactions

involving interest, Islamic banks also face other restrictions – such as the use of

derivatives, because according to Shariá all contracts should be free from excessive

uncertainty “Gharar” (Obaidullah, 2005).

Several studies have outlined the specific risks inherent in Islamic banking.

Errico and Farahbakhsh (1998) point out that prudential supervision and regulations

governing Islamic banks should place a greater emphasis on operational risk and

information disclosure. They explain the special risks attached to PLS. For instance,

Islamic banks cannot mitigate credit risk by demanding collateral from borrowers;

moreover, they do not have enough control over the management of projects financed in

the form of Mudarabah1

1 It is an Islamic mode of finance. Refer to Khan (1991), Khan (1992), Ahmad (1993) and Iqbal and

Mirakhor (2007) for more details of Islamic financial instruments.

. Khan and Ahmad (2001) claim that the level of operational and

legal risks associated with Islamic modes of finance is higher than in conventional

finance. This is because of the special framework under which Islamic banks extend

credit. Sundararajan and Errico (2002) suggest that the complexities of PLS modes of


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finance and the risks associated with the non-PLS activities should be taken into account

to establish more effective risk management. They also point out various moral hazard

issues that occur as a result of the special relationship between Islamic banks and

investment account holders. Obaidullah (2005) argues that (deposit) withdrawal risk may

persuade Islamic banks to deviate from traditional Sharia financing principles. This

occurs if banks pay competitive market returns to investment account holders regardless

of the bank’s actual performance.

Only a handful of studies have empirically addressed these issues. Cihak and

Hesse (2008), for instance, empirically compare the stability of Islamic versus

conventional banks. The results show that small Islamic banks are more stable than

similar sized conventional institutions. Large Islamic banks, however, are less stable than

their conventional counterparts.2

Mudaraba and Musharakah) play a minor role in Pakistani Islamic banking amounting to

less than 2% of their (Islamic) loan sample. Using a hazard modelling approach and

controlling for a variety of factors, the main finding is that default rates on Islamic loans

Chong and Liu (2009), studying the Malaysian banking

industry, claim that Islamic banks, in practice, are not too different from conventional

banks. Using a Granger causality test they show that rates of return on the investment

deposits of Islamic banks are closely related conventional banks’ deposit interest rates.

This is explained by competition pressures from conventional banks constraining the

actual rates offered by Islamic banks. The aforementioned study also notes that, on the

asset side, Malaysian Islamic banks apply PLS contracts for 0.5% of their financing.

Baele et al (2010) examine default risk for Islamic and conventional loans using data

obtained from the Pakistani Credit Information Bureau between April 2006 and

December 2008. The sample covers all business loans outstanding over the period.

Similar to Chong and Liu (2009), Baele et al (2010) also note that PLS contracts (such as

2 They employ the Z-score used by Boyd and Runkle (1993) as the stability indicator and they use the

Bankscope database classification to distinguish between Islamic and other types of banks. This is a limitation as Bankscope classifies banks as commercial, Islamic or other types. However an Islamic bank can be a commercial or a non-commercial bank. Such a classification is problematic: (1) In Bankscope some Islamic banks are mistakenly categorized as commercial banks. (2) Some Islamic banks are investment banks or other types that are not comparable with commercial banks. (3) The data-set also does not differentiate between conventional banks with Islamic windows and other conventional banks.

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are lower than for conventional loans. This may be explained by a greater reluctance of

borrowers to default on such loans for religious reasons.

This paper attempts to contribute to the aforementioned literature by investigating

credit and insolvency risk for a sample of Islamic and conventional banks from 22

member countries of the Organization of Islamic Conference (OIC) over 2001 to 2008.

Overall we find that Islamic banks appear to be as stable as their conventional

counterparts. The credit risk of large Islamic banks is not significantly different from

similar-sized conventional banks, while small Islamic banks exhibit lower levels of credit

risk compared to similar sized conventional banks. The paper is organized as follows:

Section I discusses the key features of Islamic finance and risk issues and Section II

outlines our methodology. Section III describes the data and Section IV presents the

results. Finally section V concludes.

I. Background on Islamic banking

This section briefly explains the key features of Islamic finance and its possible

impact on the risk and stability of Islamic banks.

I.I. Features of Islamic Finance

Islamic finance is based on Shariá principles which forbid payment or receipt of

Riba3

Islamic finance has evolved on the basis of Islamic rules on transactions, Figh al-

Muamalat, and it can mainly be categorized as: 1) Debt-based financing: the financier

purchases or has the underlying assets constructed or purchased and then this is sold to

. Riba means an excess to be returned on money lending. The Islamic terminology

for such a kind of lending is “Qard Al-Hasan”. It is interesting to note that Shariá

recognizes the time value of money, since according to the Islamic rules the price of a

good to be sold on a deferred payment basis can be different from its current value.

Interest reflects the time value of money and the interest rate is an exchange rate across

time. While Shariá recognizes interest in business, it prohibits interest on lending.

3 There are two types of Riba: Riba in debt and Riba in exchange. This paper focuses only on Riba in debt.

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the client. The sale would be on a deferred-payment basis with one or several

installments. 2) Lease-based financing: the financier purchases or has the underlying

assets constructed or purchased and then rents it to the client. At the end of the rental

period or proportionate to the rentals the ownership would be transferred wholly or

partially to the client. 3) PLS financing: the financier is the partner of the client and the

realized profit or loss would be shared according to the pre-agreed proportion (Khan and

Ahmed, 2001). The first two Islamic finance methods are collectively known as Non-

Profit and Loss Sharing “Non-PLS”. Besides restrictions on Riba, Shariá has some other

prohibitions which should be taken into account. For instance, according to the Shariá all

contracts should be free from excessive uncertainty “Gharar” (Obaidullah, 2005), hence

Islamic financial institutions face some restrictions on application of financial derivatives

and insurance policies.

I.II. Are Islamic Banks Riskier than Conventional Banks?

In this section, the asset and liabilities structure of Islamic banks are analyzed

highlighting the specific risk features.

Liabilities

Islamic banks are authorized to receive deposits mainly in the following two

forms (Iqbal, et.al., 1998): current accounts4

Due to the obligations towards depositors as debt-holders, conventional banks aim

to allocate a considerable part of their funds to loans, and endeavor to decrease the

that bear no interest but are obliged to pay

principal to holders on demand, and investment (or savings) accounts that generate a

return based on profit rates. Such rates may be adjusted according to the realized profit or

even loss which would then be shared between the Islamic bank and the investment

account holders. This PLS arrangement can (in theory at least) provide pro-cyclical

protection to banks in the event of adverse conditions – profit rates decline in bad times

and increase in good times. The extent to which investment deposits are important as a

source of funding, therefore, can have an impact on the asset portfolio of Islamic banks.

4 Deposits are received by Islamic banks in the form of “Qard Al-Hasan” or “Amanaa”.

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volatility and uncertainty of loan revenues so as to meet depositor obligations. Islamic

banks, however, have more flexibility, since they can consider investment depositors

more like equity holders. However, this flexibility may be mitigated by the fact that

Islamic banks only have limited access to wholesale funding. There is a fledgling Islamic

money market (noticeably in Bahrain and Malaysia) although only the largest institutions

have access. As such Islamic banks are rather constrained from engaging in active

liability management like conventional banks.

Assets

The special relationship with investment account holders may have various

impacts on Islamic banks’ behavior. It may weaken incentives to ensure due diligence

and loan monitoring (Sundararajan and Errico, 2002). Alternatively, the special

relationship can discipline Islamic banks more effectively as compared to conventional

banks, since investment accounts holders have greater incentives to monitor the

performance of Islamic banks (compared to conventional banks). In addition, the greater

potential withdrawal risk (Khan and Ahmed, 2001) can discipline Islamic banks to be

more active than their conventional counterparts in monitoring their loan and investment

activities.

Sharing the realized profit or loss with investment account holders may make

Islamic banks more risky. On the upside, larger payouts to investment account holders

may increase deposits and this can force bank shareholders to raise more equity capital in

order to maintain capital ratios and prevent dilution of their ownership rights. Conversely,

poor payouts may encourage deposit withdrawals leading to potential liquidity and

(ultimately) solvency problems.

Islamic Banking: Principles and Practice

Islamic banks, in practice, tend to deviate from the above mentioned financing

principles and operate similarly to conventional banks. Obaidullah (2005) and Aziz

(2006) claim that the withdrawal risk may persuade management to deviate from PLS

principles by paying competitive market returns to investment account holders regardless

of realized performance. Chong and Liu (2009) use Malaysian data to show that

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investment deposit rates of Islamic banks are closely linked to those of their conventional

counterparts. They argue that competitive pressure from conventional banks constrains

the actual implementation of PLS arrangements. Nevertheless, it is noted that in the

likelihood of crisis, management are highly likely to share realized losses with investment

account holders to avoid insolvency. This suggests that Islamic banks may have a slightly

greater capacity to bear losses compared to conventional banks. When Islamic banks are

performing well they may adjust profit rates upward but at a slower rate than realized

profitability so as to limit the level and volatility of deposit inflows.

Implicitly, investment account holders own a bond, a long position on a call

option and a short position on a put option. The strike price of the call, however, is

determined arbitrarily by Islamic banks, in the absence of supportive regulations on the

account holders’ rights. The strike price of the put is determined based on the degree of

market competitive pressures, level of incurred loss and the capital ratio of the Islamic

bank. Figure 1 illustrates how the special relationship between investment account

holders and Islamic banks work in theory and practice as compared to depositors of

conventional banks, in the absence of a deposit insurance scheme5

[FIGURE 1]

.

Islamic banks, in practice, tend to apply the non-PLS paradigm, possibly due to

the risks associated with the PLS method. For instance, Chong and Liu (2009) show that

in Malaysia, only 0.5% of Islamic bank finance is based on the PLS. Dar and Presley

(2000) claim that even Mudarabah companies in Pakistan, which are supposed to operate

in the form of PLS mainly follow the Non-PLS modes of finance. (This is also

emphasized by Baele et al, 2010). According to the Bank Indonesia (2009) the PLS

modes of finance accounted for 35.7% in the finance portfolios of Islamic banks

operating in the country by the end of 2008. Milles and Presley (1999) also point-out that

PLS is only marginally practiced in Bangladesh, Egypt, Iran, Pakistan, Philippines and

Sudan. While Islamic banks appear to refrain from practicing PLS modes of finance they

5 Some countries such as Lebanon and Indonesia have introduced a formal deposit insurance scheme and the deposits held by Islamic banks are insured in the same way as the deposits held by conventional banks. Based on the type of the scheme and the coverage ceiling, the incurred loss of depositors (investment account holders) should be differently limited.

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still face possible greater withdrawal risks than conventional banks (Khan and Ahmad,

2001; and Sundararajan and Errico, 2002).

Investment Limitations

In addition to lending, banks also allocate a part of their funds to investments.

Such investments normally include purchase of bonds of different types that have

risk/return features that help manage portfolio risk. However, Islamic banks have limited

options for such investments, since they are not authorized to invest in bonds other than

Sukuk6

Complexity of Islamic Modes of Finance

. This limitation has been weakened overtime due to the expansion of alternative

Islamic financing instruments overtime although it remains the case that Islamic banks

have less flexibility in managing their investments and other earning assets than

conventional banks.

Islamic financing agreements7, even for Non-PLS methods, are not as

straightforward as conventional loan contracts (and according to anecdotal evidence also

take longer to process). Generally, in debt-based or lease-based finance, such as

Murabaha, Islamic banks arrange for the goods/projects to be purchased/ implemented

and then sell these on for a rent to clients. For purchase/implementation of the

goods/projects, Islamic banks normally appoint the client as their agent. Such a

framework is somewhat complicated as compared to conventional loan contracts.

Sundarajan and Errico (2002) note the specific risks attached to various Non-PLS

methods, such as Salam8 and Ijara9

Another area of debate relates to the treatment of default penalties. Some

jurisdictions rule that such penalties are not authorized by Sharia, so banks make use of

. In the former, Islamic banks are exposed to both

credit and commodity price risks; in the latter, unlike conventional lease contracts,

Islamic banks cannot transfer ownership and therefore have to bear all the risks until the

end of the lease period.

6 They are similar in nature to debt certificate. 7 See Khan (1991), Khan (1992), Ahmad (1993) and Iqbal and Mirakhor (2007) for details on the features of various Islamic financial instruments.

8 Similar in nature to futures contracts. 9 Similar in nature to conventional leasing contracts.

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rebates instead (Khan and Ahmed, 2001). Here the mark-up on the finance arrangement

implicitly covers the return to the banks as well as a default penalty component. If the

client repays the loan in a timely manner then they will receive the rebate. While default

interest payments are typically calculated over the delayed period in conventional

banking, some Islamic banks collect the delayed penalty over the whole financing period.

In addition Islamic banks can also face restrictions regarding the use of derivatives as

well as different types of collateral, for instance, they are not authorized to use interest-

based assets, like bonds, for security (Khan and Ahmed, 2001).

Overall, Islamic banking is characterized by various activities that appear on the

one hand to reduce risk (e.g. PLS sharing) and the other to increase risk (e.g. limited tools

for active balance sheet management). As such, whether Islamic banking is more or less

risky than conventional banking is an empirical question. The following section outlines

the methodology used to investigate this issue.

II. Methodology and Econometric Specifications

In order to analyze the risk features of Islamic and conventional banking we adopt

an approach similar to Altunbas et. al. (2007) and Fiordelisi et. al. (2010) to examine

risk, capital and bank efficiency relationships. We first investigate credit risk

relationships between Islamic and conventional banks using a simultaneous modeling

approach. We then investigate insolvency risk using a single model set-up.

Relationships between Risk, Capital and Efficiency

Several studies investigate the relationship between risk, capital and efficiency in

banking10

10 Refer to Shrieves and Dahl (1992), Jacques and Nigro (1997), Kwan and Eisenbeis (1997), Hughes and Mester (1998) and Altunbas et al. (2007) and Fiordelisi et al. (2010).

. Shrieves and Dahl (1992), Jacques and Nigro (1997) and Rime (2001), for

instance, consider contemporaneous links between capital and risk and find there is a

positive relationship – namely, increases in risk encourage banks to grow their capital.

Alternatively, the level of capital might have positive or negative impact on the riskiness

of bank loans. Kahane (1977), Koehn and Santomero (1980) and Kim and Santomero

10

(1980) claim that banks may increase their risks in response to regulatory requirements

for higher levels of capital, since such actions by regulators limits the return-risk frontier

and therefore encourages banks to select riskier asset portfolios. Furlong and Keely

(1989) and Keely and Furlong (1990) argue that the option value of deposit insurance is

decreasing in bank’s leverage so institutions with relatively high levels of risk and

leverage have greater incentives to exploit deposit insurance subsidies incentivizing

greater risk-taking. Konishi and Yasuda (2004) use Japanese data to show that capital

adequacy requirement decrease bank risk-taking incentives.

Jensen (1986) and Harris and Raviv (1990) discuss the possible impact of capital

on inefficiency. They argue that when capital is more expensive than debt (at the margin)

management might endeavor to reduce operating costs to offset the higher financial costs

of the capital raise required by the regulators. On the other hand, a fall in interest

expenses may reduce managerial attempts to control operating expenses. Kwan and

Eisenbeis (1997) find a positive relationship between inefficiency and capital since

regulators require inefficient banks to hold a higher level of capital. Using the

simultaneous equations technique, they also show that the inefficiency would increase the

risk taking which supports the moral hazard hypothesis that poor banks have more

incentive for risk taking.

Hughes and Moon (1995) and Hughes and Mester (1998) claim that risk and

capital may be determined simultaneously taking into account the level of efficiency. The

regulators may authorize an efficient bank to exhibit higher leverage. Less efficient banks

may try to increase their risk levels to reach higher levels of profit to compensate for

losses incurred due to inefficiency. Berger and DeYoung (1997) argue that a bank, which

does not efficiently monitor its loan activities, is unlikely to be very efficient in its

operations. Efficiency and risk might also move in the same direction if banks, in an

attempt to maximize short-term profit, decide to become more cost efficient by dedicating

fewer resources to loan screening and monitoring.

Altunbas et. al. (2007), use Zellner’s (1962) seemingly unrelated regression

(SUR) approach11

11 This approach controls for contemporaneous correlation among the equations’ error terms.

to estimate a system of equations for risk, capital and efficiency. They

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show that inefficient European banks take less risk but hold more capital. They also find

a positive relationship between risk and capital, possibly because banks with a higher

level of risk are required to hold more capital. In a similar vein, Fiordelisi et al (2010)

examine risk and efficiency dimensions in European banking, using a similar model set-

up although individual risk (accounting and market-based measures), efficiency (cost and

revenue) and capital models are estimated using GMM in order to test for causal

relationships. They find that reductions in efficiency cause increases in bank risk and that

improvements in efficiency strengthens bank capital.

Model Specification for Credit Risk

Following from the aforementioned literature we investigate the credit risk

features of Islamic versus conventional banks by estimating the following system of

equations:

, 1 1,1 , 1,2 , ,1

1,1 1,2 ,1,1 1,2, , ,

3 2

1,1, 1, , , ,1 1

i t i t i t i t

i ti t i t i t

lk i t k i t lk l

LoanRisk c IslamicBankD IslamicWindowD SizeEquity Inefficiency Liquidity LoansGrowth

OwnershipStructureD AgeD

βα αψ ψδ δ

γ θ= =

= + × + × + ×

+ × + × + × + ×

+ × + × +∑ ∑7 21

1, 1, ,,1 1

t n i tt nt n

CountryDYearDη λ ε= =

× + × +∑ ∑

(1)

2 2,1 , 2,2 , ,2,

2,1 , 2,2 ,2,1,

3 2 7 21

2, 2,2, 2,, , , ,1 1 1 1

i t i t i ti t

i t i ti t

l t nk ti t k i t lk l t n

Equity c IslamicBankD IslamicWindowD SizeInefficiencyLoanRisk ROAA

OwnershipStructureD AgeD YearD

βα αψδ δ

γ ηθ λ= = = =

= + × + × + ×

+ × + × + ×

+ × + × + × +∑ ∑ ∑ 2, ,i tnCountryD ε× +∑

(2)

3 3,1 , 3,2 , ,3,

3 2 7

3,3, 3,, , , ,1 1 1

3,1 , 3,2 ,, 3,1

i t i t i ti t

l tk ti t k i t lk l t

i t i ti t

Inefficiency c IslamicBankD IslamicWindowD SizeEquity NonInterestIncomeLoanRisk

OwnershipStructureD AgeD Ye

βα αψδ δ

γ ηθ= = =

= + × + × + ×

+

+ × + × + ×

× + × + ×

∑ ∑ ∑21

3, 3, ,1

n i tnn

CountryDarD λ ε=

+ × +∑

(3)

Credit risk (LoanRisk), equity capital (Equity) and inefficiency (Inefficiency) are

modeled in equations 1 to 3, respectively. The effect of being an Islamic bank is captured

in the dummy variable which takes the value of one when the bank is Islamic and zero

otherwise. Conventional banks with Islamic windows are also represented by the dummy

variable.

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Control Variables

Several factors are controlled for in the estimation including: bank size, liquidity,

loan growth, non-interest income, profitability, ownership structure, banks’ age, and time

and country dummies. The logarithm of total asset is considered as a proxy for size.

Large banks can more benefit from both scale economies and diversification as claimed

by Hughes et al. (2001). At the same time, bigger banks might be more risky, since they

may try and exploit Too-Big-To-Fail safety net subsidies (Kane, 2010). A variety of

bank-specific indicators are included in the model: equity capital , inefficiency, liquidity,

loans’ growth, returns on average assets and the share of non-interest income to total

operating income, are incorporated to control for the possible impact of banks’ financial

performance and structure on their risk level.

Liquidity and loan growth are controlled for in the first equation. The more

liquidity a bank has maybe suggestive of lower liquidity risk but also lower returns (as

liquid assets tend to yield returns less than loans). It is important to control for loans’

growth as a considerable increase in credit may be reflective of weakening screening

standards and therefore higher risk.

Berger (1995) shows the positive impact of earnings on capital and therefore we

include returns on average assets as an explanatory variable in our capital equation. The

ratio of non-interest income to total operating income is also controlled for in the

inefficiency equation, since DeYoung and Roland (2001) show that non-interest income

can change the structure of a bank’s cost, leading to higher operating leverage.

Bank ownership structure should be also taken into account. La Porta et. al.

(2002) analyze government ownership of large banks in 92 countries and show that it

reduces efficiency. Bonin et. al. (2007) investigate the impact of ownership on bank

efficiency for eleven transition countries and find that foreign-owned banks are more cost

efficient than other banks. Iannotta et. al. (2007) using a sample of 181 large banks from

15 European countries claim that state-owned banks have poorer loan quality and higher

insolvency risk than other types of banks. In our model, we classify banks in four

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categories12

State-owned banks may invest in risky projects as a result of political influence,

or/and they may also enjoy some benefits and informational rents from political bodies.

Foreign-owners can face greater risk in monitoring the bank’s activities since they may

be less familiar with legal and judicial setting in which they operate. Alternatively, due to

such problems they may pursue relatively conservative strategies. A subsidiary, on the

one hand, might structure a risky portfolio of loans, simply because such a portfolio can

beneficially contribute to diversification of the parent’s overall portfolio. Failure of a

subsidiary may not be viewed as undesirable in the event of a crisis if reputational risks

are low.

: domestic state-owned banks, domestic private-owned banks, foreign-owned

banks and subsidiaries. Domestic private-owned banks are used as the benchmark and

hence three dummies are introduced to represent the other banks.

We also consider the age of the bank by defining two dummy variables. Banks

with at most three years of operation are categorized as young banks and those which

have been operating for a period ranging from three to seven years are considered as

middle aged. Other banks, called matured banks, are considered as the benchmark. The

age of banks is expected to account for the level of experience. Older banks have clearer

information and experience on dealing with clients. On the other hand, young banks

might over-estimate the quality of their recent loans, classifying a lower percentage of

them as problem loans and allocate less reserves and provisions than what is actually

required. Alternatively, younger banks might also hold less risky loans if they are more

conservative in their activities. Year and country dummy variables are also introduced in

the regressions to control for cross-country and time variations.

12 We classify a bank as a state-owned bank when at least fifty percent of the equity belongs to the

government. Similarly, at least fifty percent of a bank should be owned by one or more foreign entity(ies) to be classified as a foreign-owned bank. A bank which is owned by a foreign government is considered as a foreign-owned bank. We assume that although a government may decide to invest in a bank abroad based on political ties with the host country, it will not intervene in the bank’s operation as intensively as the host country’s government.

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Model Specification for Insolvency Risk

The modeling approach to investigating insolvency is as follows13

1 2 1

1 2 3 4

3 21

,1 1

i i ii

i ii i

n ik i k nk n

Stability c IslamicBankD IslamicWindowD SizeLiquidity InefficiencyAssetGrowth NonInterestIncome

OwnershipStructureD CountryD

βα αδ δ δ δγ λ ε

= =

= + × + × + ×

+ × + × + × + ×

+ × + × +∑ ∑

:

(4)

Two dummy variables aim to capture whether insolvency risk of Islamic banks

and banks with Islamic window are significantly different from that of conventional

banks. To compare insolvency risk of Islamic banks with conventional banks, we control

for the ratio of non-interest income to total operating income, size, asset growth,

inefficiency, liquidity, ownership structure and country level factors. According to

previous studies, an increase in the share of non-interest income to total operating income

is expected to inversely impact stability. De Young and Roland (2001) and Stiroh (2004,

2006), for instance, claim that the increased reliance on non-interest income has raised

the volatility of bank portfolios without increasing average profits. Lepetit et al. (2008)

show that European banks with higher non-interest income share in their net operating

income, have higher insolvency risk. Asset growth is also incorporated in the model to

control for the growth strategy of banks and the other control variables have been

explained earlier. (See Annex 1 for a summary of the loan and insolvency risks proxies,

variables of interest and control variables).

III. Data and Variables

Data

The empirical analysis is based on cross-country evidence. The data has been

obtained from the Bankscope database and the web sites of individual banks. The

Bankscope classification for Islamic banks is incorrect in places so all banks have been

cross-checked with their websites to ensure accuracy in classification. The sample covers

2674 observations for 456 commercial banks, across 22 countries members of OIC

13 We adopt a single equation set-up, since the insolvency risk proxies account for the degree of leverage.

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wherein Islamic banking is practiced, over the period 2001-2008. Our sample comprises

100 Islamic commercial banks, 72 conventional commercial banks with Islamic

window/branches and 284 conventional commercial banks. Outliers and observations for

countries with less than 1% of the total number of observations are eliminated

(observations belonging to Iraq, Palestine and Brunei are dropped from the sample).

Annex 3 (Table A) presents the number of observations of Islamic banks,

conventional banks and conventional banks with Islamic Window across 22 countries.

For Iran and Sudan observations are only available for Islamic banks as the banking

system is 100% Riba-free. In other countries, both Islamic and conventional banking are

authorized and practiced. The largest number of observations is from Indonesia and the

lowest from Gambia. Approximately, 19% of the total observations are for Islamic banks,

conventional banks with Islamic window banks represent 17% of the sample (the

remaining 64% relate to conventional banks). Table B of Annex 3 shows the different

types of banks in terms of their ownership structure and age. As expected, Islamic banks

are relatively younger than conventional banks. The table also shows that the number of

banks with foreign owners is higher for Islamic compared to conventional banks.

The full sample is split to two sub-samples to incorporate small and large banks.

Banks with total assets less than $US 1 billion are classified as small. De Young, et. al.

(2004) claim that small and large banks operate differently - small banks generally deal

with small companies, which are relatively opaque. Large banks, on the other hand, can

benefit from economies of scale, standardized products and are more transaction (as

opposed to relationship) based. They mostly analyze hard information obtained from

transparent firms. Hence, empirical investigation of the sub-samples might show the

possible impact of different customer relationships on the credit risk of Islamic versus

conventional banks.

Credit Risk

We employ three proxies for credit risk (Loan Risk): the ratio of problem loans to

gross loans (PLGL), the ratio of loan-loss reserves to gross loans (LLRGL) and the ratio

of loan-loss provisions to average gross loans (LLPAGL). These proxies are in-line with

the variables used by Angbazo (1997), Kwan and Eisenbeis (1997), Shiers (2002),

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Konishi and Yasuda (2002), Cebenoyan and Strahan (2004), Gonzalez (2004), Altunbas

et al. (2007) and Lepetit et al. (2008). Nevertheless, these indicators of credit risk only

partly reflect the quality of the loan portfolio, since differences across banks may be due

to different internal policies regarding problem loan classification, reserve requirements

and write-off policies.

Insolvency Risk

Two different proxies are used to represent insolvency risk: Z-Score as well as the

ratio of Unreserved Impaired Loans14

( )( )

E ROA ETAZscore

SD ROA+

=

to Equity “UIL”. The Z-Score is a stability

indicator widely used in the literature (see, for instance, Goyeau and Tarazi, 1992; Boyd

and Runkle, 1993; Lepetit et al., 2008; Hesse and Cihak, 2007; Cihak and Hesse, 2008;

Laeven and Levine, 2009). Using accounting information on asset returns, its volatility

and leverage, the Z-Score is calculated as follows: , where E(ROA)

is the expected return on assets, ETA is the equity to asset ratio and SD(ROA) is the

standard deviation of ROA. Z-Score is inversely related to the probability of a bank’s

insolvency. A bank becomes insolvent when its asset value drops below its debt. The

insolvency probability can be written as P(ROA<-ETA). If we assume that ROA is

normally distributed, then the probability can be written as the standardized ROA, i.e.

( )( )

ROA E ROAP Zsocre

SD ROA −

< −

. Hence the Z-Score shows the number of standard deviation that

a bank’s return has to fall below its expected value to deplete equity and make the bank

insolvent. A higher Z-Score implies that the bank is more stable.

Bank insolvency occurs due deterioration in the quality of assets and/or from

higher leverage. A bank with lower reserves and capital therefore is more prone to

default. UIL considers asset quality and the respective reserves together with capital as a

source of insolvency risk. This proxy shows the extent to which a bank is protected

against default and a higher ratio indicates that the bank is more risky.

14 Unreserved impaired loans are obtained by deducting loan-loss reserves from impaired loans.

17

Summary Statistics

Tables (1-A) and (1-B) illustrate sample descriptive statistics. Table (1-A)

illustrates that relatively large conventional banks establish Islamic windows.

Interestingly, while small Islamic banks have, on average, higher liquidity and non-

interest income ratios than similar-sized conventional banks, liquidity and non-interest

income of large Islamic banks are, on average, lower than those of large conventional

banks.

Table (1-B) presents the descriptive statistics on our risk measures. The table

shows that both small and large Islamic banks have significantly lower levels of credit

risk according to PLGL and LLRGL, compared to small and large conventional banks.

However our third (flow) credit risk proxy, LLPAGL, shows that loan risk of small

Islamic banks is higher than those of small conventional banks, but it exhibits no

significant difference between large Islamic and conventional banks. In terms of

insolvency risk, except higher volatility of asset returns of small Islamic banks, the mean

test results show that the z-score and its components for both small and large Islamic

banks are not significantly different from those of conventional banks. Nevertheless, the

mean test for the 3-year rolling window z-score suggests that both small and large Islamic

banks are less stable than conventional banks. On the other hands, the UIL measure

suggests that Islamic banks are, on average, less prone to insolvency compared to

conventional banks. (A correlation matrix is presented in Annex 4 and this does not

suggest any major collinearity problems among our independent variables).

[TABLE 1-A]

[TABLE 1-B]

IV. Empirical Results

Loan Risk

Table 2 presents the estimated model using the seemingly unrelated regression

approach adopted by Zellner (1962) on the basis of an unbalanced panel for the period

18

2001-2008. When we use PLGL as the proxy for credit risk, the results show that small

Islamic banks have lower risks than small conventional banks. However, credit risk is not

significantly different for large Islamic and conventional banks. Interestingly, Islamic

banks tend to have lower level of capitals when considering the full sample and small

banks sub-sample. As a robustness check, we substitute PLGL with LLRGL (annex 5).

The results show that both small and large Islamic banks have lower credit risk than

conventional banks of similar size. The capital ratio of Islamic banks is not significantly

different from that of conventional banks in full sample and small or large banks sub-

samples. Table 3 illustrates the results when we use LLPAGL as the credit risk proxy.

The results are, on the whole, in-line with the findings presented in table 2. Small Islamic

banks have less risky loans and lower capital ratios than small conventional banks. In

terms of inefficiency, we do not find any robust and significant difference between

Islamic banks and conventional banks.

The results also show that risk and capital are positively and significantly related

consistent with Altunbas et. al. (2007). Risk and inefficiency are also positively and

significantly related to each other. This is in-line with the findings of Hughes and Moon

(1995), Kwan and Eisenbeis (1997) and Hughes and Mester (1998) and contrary to the

results of Altunbas et al. (2007). Finally capital and inefficiency are negatively related

which supports the finding of Kwan and Eisenbeis (1997), but in contradiction with the

results of Altunbas et. al. (2007). The impact of size on credit risk is ambiguous, since the

regression results are different across proxies. Loan growth is associated with lower

credit risk, when the stock proxies, ie PLGL and LLRGL, are employed; however, we

find no significant relationship between the loans growth and credit risk when we use

flow proxy (LLPAGL) in the model.

In terms of ownership structure, small foreign-owned banks exhibit higher credit

risk than small domestic private-owned banks, while large foreign-owned banks are less

cost inefficient than large domestic private-owned banks, consistent with the finding of

Bonin et. al. (2007). The credit risk of sate-owned banks and subsidiaries are not

significantly different from that of domestic private-owned banks. Small subsidiaries are

more capitalized and less inefficient than small domestic private-owned banks. Finally,

19

we find that asset quality of large young banks tends to be lower than those of large

matured banks, when we use LLPAGL and LLRGL. However, the results are not robust

in case PLGL is employed as the credit risk proxy.

[TABLE 2]

[TABLE 3]

Insolvency Risk

Table 4 reports the results of the estimation using the Z-Score and its components.

The empirical results show that the stability of Islamic banks is not significantly different

from that of conventional banks. This result persists across different specifications. To

control for the outliers and skewness of the distribution, the Z-Score and its components

are logged, however, the results are not changed when we use the level of the Z-Score

and its components. Using the alternative proxy, UIL, does not change our results (see

Table 5). We find no significant impact of size, asset growth and liquidity on insolvency

risk. The results show that large banks with higher noninterest income share in total

operating income have, on average, a lower z-score. Large banks with higher inefficiency

also exhibit, on average, a lower z-score.

The insolvency risk of state-owned banks is not significantly different from that

of domestic private-owned banks. Both small and large foreign-owned banks are less

stable than small and large domestic private-owned banks, when the z-score is employed

as the proxy; however, there is no difference between these two categories of banks,

when we use UIL as the proxy. Small subsidiaries have slightly higher z-score on average

than domestic private-owned banks of the same size. However, when UIL is used as the

proxy for insolvency risk we do not find any significant difference between subsidiaries

and domestic private-owned banks.

[TABLE 4]

[TABLE 5]

20

Overall we find that Islamic banks are as stable as conventional banks. The credit

risk of large Islamic banks is also not significantly different from that of similar sized

conventional banks. This suggests that the various operational and other risks attached to

specific Islamic modes of finance do not appear to materially impact their risk and

stability. Small Islamic banks, however, have lower levels of credit risk compared to

small conventional banks. One possible explanation for this finding could be the fact that

small Islamic banks are more likely to be relatively new, conservative in their operations

and attract clients due to religious reasons that are less likely to default (as partially

suggested in Baele et al , 2010).

Other Issues and Robustness Checks

Our results are accord with the view that Islamic banks, in practice, behave

similarly to conventional banks. To further check this finding, we compare the payoffs to

depositors with profitability (ROE) for Islamic and conventional banks. Figure 2 presents

the payoffs across different banks. Figures (2-a) and (2-b) show that Islamic banks which

incur losses have paid positive returns to their investment account holders. The evidence

relates to 18 Islamic banks operating in 9 countries. This supports the findings of

Obaidullah (2005) and Aziz (2006) who argue that due to competitive pressures Islamic

banks tend to deviate from traditional PLS financing. At higher levels of ROE Islamic

banks seem to pay more to investment account holders, showing that partly share realized

profits. Table 6 exhibits the correlation between ROE and the implicit interest expense

rate across different banks. For observations above 75 percentile, only Islamic banks

exhibit positive and high correlation between ROE and the depositors’ payoff. It would

be interesting to note that at 25 percentile, similar to large conventional banks with

Islamic window, large Islamic banks even show negative and high correlation!

[FIGURE 2]

[TABLE 6]

21

Robustness Checks for Credit Risk Analysis

To check whether our findings persist when we consider endogeneity between

risk, capital and inefficiency, the 3SLS method is employed to estimate the system of

equations. The results are presented in Annex 6 and generally support our previous

finding. Using PLGL as the credit risk proxy, the results for the full sample and small

banks sub-sample specifications show that Islamic banks, on average, have lower loan

risk, but are more leveraged than conventional banks. However, the credit risk and

leverage of large Islamic banks are not significantly different from those of large

conventional banks. To further check the results, PLGL is replaced by LLRGL.

According to the risk equation estimates, the coefficient on the Islamic bank dummy is

negative and significant at the one percent level for the full sample and small banks sub-

sample. The results also suggest that large Islamic banks face the same level of credit risk

as similar sized conventional banks. LLPAGL is also employed as a final credit risk

proxy and we find that the credit risk of Islamic and conventional banks are similar. In

terms of inefficiency, the results support our previous finding.

Annex 7 illustrates the results when the loan risk equation is estimated alone. The

coefficient on the Islamic banks dummy is negative and significant under all

specifications of full sample and small banks sub-sample, when stock proxies are used.

Under large banks sub-sample, we find no significant difference between Islamic banks

and conventional banks using either stock or flow proxy. Overall, when we estimate the

risk equation separately, the results regarding credit risk remain almost the same.

Robustness Checks of Insolvency Risk Analysis

As a robustness check, we estimate the model, using 3-year rolling window Z-

Scores; this approach enables panel data estimations to control for unobservable factors,

but at the expense of increased noise due to the lower number of observations used to

compute standard deviations. Moreover, we use the lagged values of the explanatory

variables in our model to deal with endogeneity issues. The results are presented in

Annex 8 and are in line with previous findings. In all our specifications the stability of

Islamic banks is not significantly different from that of conventional banks.

22

V. Summary and Conclusion

This paper attempts to analyze the risk and performance features of Islamic banks.

The obligations of Islamic banks towards investment account holders are different from

those of conventional banks and hence they face different risks. Conventional banks have

to fulfill their obligations towards depositors irrespective of their profits or losses

whereas Islamic banks are supposed to share the realized profit or loss with investment

account holders. In practice, to avoid the withdrawal risk and active monitoring of

investment account holders, Islamic banks tend to deviate from the PLS principles of

Islamic finance. They pay a relatively competitive rate of return to investment account

holders, regardless of their realized profit or loss. On the asset side, Islamic banks try to

apply the non-PLS modes of Islamic finance which are in nature similar to conventional

finance. Nevertheless, Islamic banks still may face extra operational risks and concerns

because of the complexity of Islamic modes of finance and limitations in their investment

activities.

This paper investigates the credit risk and stability features of Islamic commercial

banks. We use a sample of 456 conventional and Islamic banks from 22 countries for the

period of 2001 to 2008. Our results show that the specific features of Islamic banking do

not appear to adversely affect their credit risk and stability. By controlling for various

factors we show that such banks appear to be as stable as conventional banks.

Furthermore, the credit risk of large Islamic banks is not significantly different from

similar sized conventional banks. Smaller Islamic banks, however, have a lower exposure

to credit risk than small conventional banks.

New evidence pointing towards partial deviation of Islamic banks from Islamic

financing principles is also found. For instance we find evidence that at low levels of

profitability Islamic banks tend to pay competitive returns on investment account deposits

whereas at high levels of profitability they try to some extent share realized gains –

partially reflecting the PLS motive. Given the similarities in the credit and solvency risk

features of Islamic and conventional banks this would suggest there is no pressing need to

develop separate regulatory and supervisory systems to oversee the different types of

banking business.

23

References

Aigner, D.J., Lovell, C.A.K. and Schmidt, P., 1977, “Formulation and estimation of stochastic frontier production function models”, Journal of Econometrics, Vol. 6, pp. 21-37.

Ahmad, A., 1993, “Contemporary practices of Islamic financing techniques” Research Paper No. 20, IRTI, Islamic Development Bank, Jeddah.

Altunbas, Y., Carbo, S., Gardener, E.P.M. and Molyneux, P., 2007, “Examining the relationships between capital, risk and efficiency in European banking”, European Financial Management, Vol. 13, No. 1, pp. 49-70.

Angbazo, L., 1997, “Commercial bank net interest margins, defaults risk, interest-rate risk, and off-balance sheet banking” Journal of Banking & Finance, Vol. 21, pp. 55-87.

Baele, L., Farooq, M. and Ongena, S., 2010, “Of religion and redemption: evidence from default on Islamic loans”, Working Paper.

Battese, G.E. and Coelli, T.J., 1992, “Frontier production functions, technical efficiency and panel data: With application to Paddy farmers in India”, Journal of Productivity Analysis, Vol. 3, pp. 153-169.

Beatty A., Harris, D., 1999, “The effects of taxes, agency costs and information asymmetry on earnings management: a comparison of public and private firms” Rev Acc Stud Vol. 4, pp. 299–326.

Berger, A. N., 1995, “The relationship between capital and earnings in banking”, Journal of Money, Credit, and Banking, Vol. 27 (2), pp. 432–456.

Berger, A. N. and De Young, R., 1997, “Problem loans and cost efficiency in commercial banking”, Journal of Banking and Finance, Vol. 21 (6), pp. 849–870.

Bonin, J.P., Hasan, I., Wachtel, P., 2005, “Bank performance, efficiency and ownership in transition countries”, Journal of Banking and Finance, Vol. 29, 31-53.

Boyd, J. H. and Runkle, D.E., 1993, “Size and performance of banking firms”, Journal of Monetary Economics, Vol. 31, pp. 47–67.

Cebenoyan, S.A. and Strahan, P.E., 2004, “Risk management, capital structure and lending at banks”, Journal of Banking & Finance, Vol. 28, 19-43.

Chong, B.S., and Liu, M.H., 2009, “Islamic banking: Interest-free or interest-based?”, Journal of Pacific-Basin Finance, Vol. 17, pp. 125-144.

Čihák, M. and Hesse, H., 2008, “Islamic banks and financial stability: An empirical analysis”, IMF Working Paper, No. WP/08/16.

Čihák, M., Maechler, A., Schaeck, K. and Stolz, S., 2009, “Who disciplines bank managers?”, IMF Working Paper, No. WP/09/272.

Dar, Humayon A., Presley, J. R., 2000, “Lack of profit loss sharing in Islamic banking: management and control imbalances”, International Journal of Islamic Financial Services, Vol. 2 (2).

24

DeYoung, R., Hunter, W.C. and Udell, G.F., 2004, “The past, present, and probable future for community banks”, Journal of Financial Services Research, Vol. 25, Nos. 2-3. p. 85-133.

DeYoung R. and Roland, K. P., 2001, “Product mix and earnings volatility at commercial banks: Evidence from a degree of total leverage model”, Journal of Financial Intermediation, Vol. 10 (1), pp. 54-84.

El-Hawary, D., Wakif, G. and Iqbal, Z., 2004, “Regulating Islamic financial institutions: The nature of the regulated” World Bank Policy Research Working Paper No. 3227.

Errico, L. and Farrahbaksh, M., 1998, “Islamic banking: Issues in prudential regulation and supervision”, IMF Working Paper 98/30.

Fiordelisi, F., Marques-Ibanez, D. and Molyneux, P., 2010, “Efficiency and risk in European banking”, Journal of Banking and Finance, Article in Press.

Furlong, F. and Keely, M., 1989, “Bank capital regulation and asset risk”, Economic Review, Federal Reserve Bank of San Francisco, Spring, pp. 20–40.

Gonzalez, F., 2005, “Bank regulation and risk taking incentives: An international comparison of bank risk”, Journal of Banking & Finance, Vol. 29, 1153-1184.

Goyeau, D. and Tarazi, A., 1992, “Evaluation du risque de de´faillance bancaire en Europe”, Revue d’Economie Politique, Vol. 102 (2), pp. 249–280.

Grais, W. and Pellegrini, M., 2006, “corporate governance and Shariah compliance in institutions offering Islamic financial services” World Bank Policy Research Working Paper No. 4054.

Harris, M. and Raviv, A., 1990, “Capital Structure and the informational role of debt”, Journal of Finance, Vol. 45, pp. 321-349.

Hesse, H. and Čihák, M., 2007, “Cooperative banks and financial stability”, IMF Working Paper, No. 07/02.

Hughes, J.P. and Mester, L., 1998, “Bank capitalization and cost: evidence of scale economies in risk management and signalling”, Review of Economics and Statistics, Vol. 80 (2), pp. 314–25.

Hughes, J.P., Mester, L. and Moon, C., 2001, “Are scale economies in banking elusive or illusive: Evidence obtained by incorporating capital structure and risk-taking into models of bank production”, Journal of Banking and Finance, Vol. 25 (12), 2001, pp. 2169–2208.

Hughes, J. P. and Moon, C., 1995, “Measuring bank efficiency when managers trade return for reduced risk”, Department of Economics Rutgers University Working Paper, No. 1995-20.

Iannota, G., Giacomo, N., Sironi, A., 2007, “Ownership structure, risk and performance in the European banking industry”, Journal of Banking and Finance, Vol. 31, pp. 2127-2149.

Iqbal, M., Ausaf, A. and Khan, T., 1998, “Challenges facing Islamic banking” Occasional Paper, No. 1, IRTI, Islamic Development Bank, Jeddah.

25

Iqbal, Z. and Mirakhor, A., 2007, “An introduction to Islamic finance: Theory and practice” Wiley, London, ISBN 0470821884.

Jacques, K. and Nigro, P., 1997, “Risk-based capital, portfolio risk, and bank capital: A simultaneous equations approach”, Journal of Economics and Business, Vol. 49 (6), pp. 533–47.

Jensen, M. C., 1986, “Agency Costs of free cash flow, corporate finance and takeovers”, American Economic Review, Vol. 76 (2), pp. 323–29.

Kahane, Y., 1977, “Capital adequacy and the regulation of financial intermediaries”, Journal of Banking & Finance, Vol. 1 (2), pp. 207-18.

Kahf, M. and Khan, T., 1992, “Principles of Islamic financing” Research Paper No. 16, IRTI, Islamic Development Bank, Jeddah.

Kanagaretnam, K. G. J. Lobo and D. H. Yang, 2005, “Determinants of signaling by banks through loan loss provisions” Journal of Business Research, Vol. 58, pp. 312-320.

Kane, E., 2010, “Redefining and containing systemic risk”, Atlantic Economic Journal, Vol. 38, pp. 251–264.

Kao C, and C. Wu, 1994, “Tests of dividend signaling using the Marsh–Merton model: a generalized friction approach”, Journal of Business, Vol. 67, pp. 45– 68.

Keely, M. and Furlong, F., 1990, “A re-examination of mean–variance analysis of bank capital regulation”, Journal of Banking and Finance, Vol. 14, pp. 69–84.

Khan, M. Fahim, 1991, “Comparative economics of some islamic financing techniques” Research Paper No. 12, IRTI, Islamic Development Bank, Jeddah.

Khan, T. and Ahmed, H., 2001, “Risk management: An analysis of issues in Islamic financial industry”, Occasional Paper No. 5, IRTI, Islamic Development Bank, Jeddah.

Kim, D. and Santomero, A. M., 1988, “Risk in banking and capital regulation”, Journal of Finance, Vol. 43 (5), pp. 1219-33.

Koehn, M. and Santomero, A., 1980, “Regulation of bank capital and portfolio risk”, Journal of Finance, Vol. 35 (5), pp. 1235-45.

Konishi, M. and Yasuda, Y., 2004, “Factors affecting bank risk taking: Evidence from Japan”, Journal of Banking and Finance, Vol. 28, pp. 215-232.

Kwan, S. and Eisenbeis, R., 1997, “Bank risk, capitalization and operating efficiency”, Journal of Financial Services Research, Vol. 12 (2 and 3), pp. 117-31.

Laeven, L. and Levine, R., 2009, “Bank governance, regulation and risk taking”, Journal of Financial Economics, Vol. 93, pp. 259-275.

La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2002, “Government Ownership of Banks”, Journal of Finance, Vol. LVII (1), pp. 265-301 .

Lepetit, L., Nys, E., Rous, P. and Tarazi, A., 2008, “Bank income structure and risk: An empirical analysis of European banks”, Journal of Banking & Finance, Vol. 32, pp. 1452-1467.

26

Meeusen, W. and van den Broeck, J., 1977, “Efficiency estimation from Cobb-Douglas production functions with composed error”, International Economic Review, Vol. 18, pp. 435-444.

Mills, P. S. and Presley, J.R., 1999, “Islamic finance: Theory and practice”, Macmillan, London.

Mokhtar, H.S., Abdullah, N. and Al-Habshi, S.M., 2006, “Efficiency of Islamic banks in Malaysia: A stochastic frontier approach” Journal of Economic Cooperation among Islamic Countries, Vol. 27, No. 2, pp. 37–70.

Obaidullah, M. 2005, “Islamic financial services” Islamic Economics Research Center, King Abdulaziz University, Jeddah, Saudi Arabia.

Ongena, S. and Smith, D.C., 2000, “What determines the number of bank relationships? Cross-country evidence”, Journal of Financial Intermediation, Volume 9, 26–56.

Rime, B., 2001, “Capital requirements and bank behaviour: Empirical evidence for switzerland”, Journal of Banking and Finance, Vol. 25 (4), pp. 789–805.

Shiers A.F., 2002, “Branch banking, economic diversity and bank risk”, The Quarterly Review of Economics and Finance, Vol. 42, 587-598.

Shrieves, R.E. and Dahl, D., 1992, “The Relationship between Risk and Capital in Commercial Banks”, Journal of Banking and Finance, Vol. 16 (2), pp. 439–57.

Stiroh, K., 2004, “Diversification in banking: Is non-interest income the answer?”, Journal of Money, Credit and Banking, Vol. 36 (5), pp. 853–882.

Stiroh, K., 2006, “A portfolio view of banking with interest and noninterest activities”, Journal of Money, Credit and Banking, Vol. 38 (5), pp. 1351-1361.

Sundararajan, V. and Errico, L., 2002, “Islamic financial institutions and products in the global financial system: Key issues in risk management and challenges ahead” IMF Working Paper, WP/02/192.

The Banker Report, the global finance magazine of the Financial Times and Group HSBC Amanah, Issue November 2009.

Titman, S. and Wessels, R., 1988, “The determinants of capital structure choice” Journal of Finance, Volume 43, Issue 1, 1-19.

Warfield, T.D., Wild, J.J. and Wild, K.L., 1995, “Managerial ownership, accounting choices, and informativeness of earnings”, Journal of Accounting and Economics, Vol. 20, pp. 61– 91.

Zellner, A., 1962, “An efficient method of estimating seemingly unrelated regression and tests for aggregation bias”, Journal of the American Statistical Association, Vol. 57 (298), pp. 348–368.

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Figure 1 – Depositors’ Payoff in Islamic and Conventional Banking

Earnings

Depositors’ Payoff

Depletion of Islamic Banks’

Capital

Loss

Depletion of Conventional Banks’

Capital

Theory of Islamic Banking

Islamic Banking in Practice Conventional

Banking

28

Table 1-A – General Descriptive Statistics This table presents summary statistics for banks across 22 countries for the years 2001-2008.

TA (th $) TAG (%) NLTA (%) GLG (%)

ETA (%) LIQ (%) NNI (%) ROAA

(%) ROAE (%) IIER (%) Inefficiency

Small Islamic Banks

Number 270 206 259 193 260 262 198 265 266 214 183

Mean 306,349 51 45 48 16 72 32 1.6 12 4.33 1.62

SD 249,583 88 23 77 14 54 25 2.8 19 3.41 0.46

Small Conventional Banks

Number 916 733 909 724 906 902 753 904 903 900 781

Mean 365,452 29 45 24 14 66 23 1.3 11 5.26 1.61

SD 255,875 138 20 47 10 41 18 2.0 18 2.92 0.34

T-Stat. of Mean Test -3.40*** 2.70*** -0.39 4.18*** 2.76*** 1.72* 4.56*** 1.63 0.79 -3.70*** 0.24

Small ISW Banks

Number 139 109 139 109 138 137 99 137 138 135 109

Mean 433,237 32 53 28 13 53 25 1.8 17 5.43 1.53

SD 261,528 104 17 44 11 39 17 1.7 12 2.87 0.22

large Islamic Banks

Number 247 210 247 210 247 247 207 244 246 221 176

Mean 7,152,162 37 57 40 12 49 18 2.1 18 4.64 1.65

SD 9,530,585 42 16 50 8 36 13 2.1 13 3.18 0.31

Large Conventional Banks

Number 774 668 771 671 773 771 707 766 754 767 647

Mean 8,129,447 27 47 25 10 57 22 1.4 13 4.73 1.72

SD 11,700,000 144 19 39 6 28 13 1.6 18 2.62 0.32

T-Stat. of Mean Test -1.32 1.63 7.47*** 3.87*** 4.69*** -3.09*** -4.40*** 4.76*** 4.92*** -0.41 -2.51**

Large ISW Banks

Number 328 286 327 286 328 328 308 327 328 328 307

Mean 11,600,000 18 50 22 10 47 20 1.7 19 3.78 1.60

SD 13,400,000 23 14 34 6 23 10 1.3 12 2.16 0.25

Islamic Banks

Number 517 416 506 403 507 509 405 509 512 435 359

Mean 3,576,979 44 51 44 14 61 24 1.8 15 4.49 1.64

SD 7,419,696 69 21 65 12 48 21 2.5 16 3.29 0.39

Conventional Banks

Number 1690 1401 1680 1395 1679 1673 1460 1670 1657 1667 1428

Mean 3,921,270 28 46 24 12 62 23 1.3 12 5,02 1.66

SD 8,784053 141 20 43 9 36 16 1.8 18 2.80 0.33

T-Stat. of Mean Test -0.88 3.12*** 4.07*** 5.65*** 4.38*** -0.36 1.61 4.38*** 3.59*** -3.10*** -1.09

ISW Banks

Number 467 395 466 395 466 465 407 464 466 463 416

Mean 8,254,375 22 51 24 11 48 21 1.8 18 4.26 1.58

SD 12,300,000 58 15 37 8 28 12 1.5 12 2.50 0.25

TA= Total Assets, TAG=Total Assets Growth, NLTA=Net Loans to Total Asset Ratio, GLG= Gross Loans Growth, ETA=Equity to Asset Ratio, LIQ= Liquid Assets to Deposits and Short Term Funding Ratio, NNI= Net Commission & Trading Revenue to Total Operating Income Ratio, ROAA=Return on Average Assets, ROAE=Return on Average Equity Ratio, IIER= Implicit Interest Expense Rate, Inefficiency=Cost Inefficiency, the estimation method is presented in annex 4, * significant at 10%, ** significant at 5%, *** significant at 1%.

29

Table 1-B – Descriptive Statistics of Risk Measure Variables

This table presents summary statistics on risk measure variables for banks across 22 countries for the years 2001-2008.

LLRGL (%)

PLGL (%)

LLPAGL (%) UIL (%) Log

zscore Log

sdroaa Log

zscorep1 Log

zscorep2 Log

zscore3rw Log

sdroaa3rw Log

zscorep13rw Log

zscorep23rw

Small Is B

Number 156 60 178 42 18 19 15 18 134 139 121 136

Mean 3.74 5.21 2.09 26.0 2.59 -4.57 0.64 2.49 3.21 -5.17 1.10 3.03

SD 3.84 5.69 2.90 44.0 1.20 0.93 0.86 1.18 1.22 1.15 1.19 1.29

Small Con B

Number 723 632 861 355 78 80 74 79 520 532 488 522

Mean 9.54 10.93 1.59 40.6 3.07 -5.10 0.74 2.90 3.53 -5.56 1.20 3.41

SD 9.71 12.36 2.62 58.1 0.92 0.93 0.99 1.03 1.11 1.12 1.24 1.10 T-Stat. of Mean Test -12.23*** -6.48*** 2.14** -1.95* -1.59 2.24** -0.41 -1.37 -2.79*** 3.59*** -0.80 -3.17***

Small ISW B Number 108 85 120 55 11 11 10 11 82 82 79 82

Mean 8.71 9.74 1.70 39.8 2.75 -4.83 0.63 2.64 3.26 -5.42 0.99 3.13

SD 9.48 12.02 2.48 59.8 0.57 0.60 0.53 0.57 1.02 1.04 1.54 1.01

large Is B

Number 193 119 199 77 36 36 36 36 168 168 164 168

Mean 4.31 5.20 1.26 12.4 2.77 -4.87 0.78 2.60 3.26 -5.39 1.18 3.09

SD 3.99 5.56 1.87 11.7 0.63 0.81 0.62 0.67 1.02 1.13 1.12 1.05

Large Con B

Number 701 577 741 369 108 109 99 108 519 528 507 522

Mean 6.98 8.60 1.29 28.5 2.75 -5.05 0.82 2.64 3.54 -5.77 1.42 3.37

SD 8.97 8.97 2.13 48.8 1.12 0.94 0.83 1.06 1.24 1.25 1.24 1.30 T-Stat. of Mean Test -6.02*** -5.38*** -0.19 -5.62*** 0.15 1.14 -0.32 -0.26 -2.99*** 3.70*** -2.26** -2.85***

Large ISW B

Number 315 283 319 145 42 42 42 42 232 232 227 232

Mean 5.14 5.93 0.88 24.2 2.97 -5.26 1.09 2.79 3.62 -5.88 1.77 3.45

SD 3.97 5.88 1.43 35.7 0.76 0.80 0.90 0.75 1.05 1.18 1.20 1.05

Is Banks

Number 349 179 377 119 54 55 51 54 302 307 285 304

Mean 4.06 5.20 1.65 17.2 2.71 -4.76 0.74 2.56 3.24 -5.29 1.15 3.06

SD 3.93 5.59 2.44 28.3 0.85 0.85 0.69 0.87 1.11 1.14 1.15 1.16

Con Banks

Number 1431 1209 1615 724 186 189 173 187 1039 1060 995 1044

Mean 8.28 9.82 1.45 34.4 2.88 -5.07 0.79 2.75 3.54 -5.67 1.31 3.39

SD 8.37 10.93 2.41 53.9 1.05 0.94 0.90 1.05 1.18 1.19 1.24 1.20 T-Stat. of Mean Test -13.84*** -8.83*** 1.45 -5.25*** -1.23 2.30** -0.42 -1.33 -4.10*** 5.01*** -2.05** -4.31***

ISW Banks

Number 423 368 439 200 53 53 52 53 314 314 306 314

Mean 6.05 6.81 1.10 28.5 2.93 -5.17 1.00 2.76 3.53 -5.76 1.57 3.37

SD 6.08 7.89 1.82 44.0 0.72 0.78 0.86 0.72 1.05 1.16 1.34 1.04

LLRGL=Loan Loss Reserves on Gross Loans Ratio, PLGL=Problem Loans on Gross Loans Ratio, LLPAGL=Loan Loss Provision on Average Gross Loans, UIL=Unreserved Impaired Loans to Equity Ratio, Zscore=(M_ROAA+M_ETA)/SDROAA, M_ROAA = Mean of ROAA over the sample period, M_ETA=Mean of ETA over the sample period, SDROAA= standard deviation of ROAA over the sample period (banks needs to have at least five consecutive observations,) Zscorep1=M_ROAA/SDROAA, Zscorep2=M_ETA/SDROAA, Zscore3rw=(ROAA+ETA)/SDROAA3rw, SDROAA3rw= standard deviation of ROAA over 3 years (current year and two previous consecutive years), Zscorep13rw=ROAA/SDROAA3RW, Zscorep23rw=ETA/SDROAA3RW, * significant at 10%, ** significant at 5%, *** significant at 1%.

30

Table 2 – Regression Estimates of Loan Risk Model Using Problem Loans on Gross Loans

(PLGL) as the Proxy (SUR Simultaneous Approach)

Full Sample Small Banks Sample Large Banks Sample

Variables PLGL Equity to Asset Ratio Inefficiency PLGL Equity to

Asset Ratio Inefficiency PLGL Equity to Asset Ratio Inefficiency

Islamic Bank Dummy -0.027*** -0.015*** 0.028 -0.077*** -0.022* 0.000 0.004 0.002 0.029 (-3.16) (-2.72) (1.06) (-3.68) (-1.79) (0.01) (0.51) (0.46) (1.02)

Islamic Window Dummy -0.010 -0.018*** -0.097*** 0.001 -0.007 -0.051 -0.008 -0.013*** -0.101*** (-1.48) (-4.37) (-4.80) (0.07) (-0.65) (-1.05) (-1.48) (-3.86) (-4.99)

Log of Total Asset -0.006*** -0.021*** -0.003 -0.014* -0.053*** -0.033 -0.002 -0.007*** 0.003 (-2.86) (-18.31) (-0.43) (-1.79) (-13.45) (-1.52) (-0.75) (-4.96) (0.38)

PLGL 0.102*** 0.964*** 0.052* 0.873*** 0.011 1.276*** (5.63) (11.12) (1.95) (7.10) (0.51) (10.00)

Equity to Asset Ratio 0.102** -1.101*** 0.068 -1.338*** -0.128** -0.362* (2.42) (-8.73) (0.89) (-6.86) (-2.47) (-1.87)

Inefficiency 0.097*** -0.047*** 0.115*** -0.049*** 0.095*** -0.019*** (10.84) (-8.35) (6.80) (-5.00) (9.87) (-3.30)

Loans Growth (%) -0.030*** -0.034*** -0.027*** (-5.22) (-3.33) (-4.40)

Liquid Asset to Deposit & Short-Term Funding Ratio -0.012 -0.028* -0.007 (-1.37) (-1.95) (-0.73)

ROAA 1.735*** 1.905*** 1.473*** (17.08) (10.73) (14.57)

Net Com. Trad. To Total Operating Income Ratio -0.007 0.055 -0.037 (-0.12) (0.58) (-0.50)

State-Owned Bank Dummy 0.013 0.004 0.039 0.033 0.012 0.030 0.011 0.007 0.007 (1.62) (0.72) (1.51) (1.55) (1.02) (0.51) (1.58) (1.58) (0.25)

Foreign-Owned Bank Dummy 0.029*** -0.001 -0.091*** 0.069*** 0.006 -0.052 -0.011 -0.007 -0.075** (2.94) (-0.17) (-2.89) (3.47) (0.55) (-0.94) (-1.10) (-1.09) (-2.02)

Subsidiary Bank Dummy 0.000 0.014*** -0.084*** 0.026* 0.038*** -0.153*** -0.002 0.004 -0.007 (0.03) (3.45) (-4.09) (1.89) (5.07) (-4.24) (-0.37) (0.86) (-0.28)

Young Bank Dummy -0.010 0.012 0.106** -0.038 -0.014 0.021 -0.015 0.007 0.155*** (-0.74) (1.43) (2.53) (-1.40) (-0.90) (0.30) (-1.12) (0.80) (3.13)

Middle-Age Bank Dummy 0.008 0.017*** -0.039 0.023 0.015 -0.104** -0.005 0.006 0.051 (0.81) (2.58) (-1.23) (1.33) (1.51) (-2.18) (-0.46) (0.80) (1.21)

Constant 0.000 0.000 1.634*** 0.000 0.000 0.000 0.000 0.000 1.573*** (.) (.) (11.94) (.) (.) (.) (.) (.) (7.77)

Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,230 1,230 1,230 451 451 451 779 779 779

R-squared 0.310 0.504 0.435 0.332 0.618 0.506 0.418 0.554 0.496

z-statistics in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%

31

Table 3 - Regression Estimates of Loan Risk Model using Loans Loss Provisions on Average Gross Loans (LLPAGL) as the Proxy (SUR Simultaneous Approach)


Variables LLPAGL Equity to Asset Ratio Inefficiency LLPAGL Equity to

Asset Ratio Inefficiency LLPAGL Equity to Asset Ratio Inefficiency

Islamic Bank Dummy -0.002 -0.017*** -0.032 -0.008** -0.028*** -0.091** 0.001 -0.002 0.029 (-1.06) (-3.32) (-1.36) (-2.20) (-2.63) (-2.18) (0.75) (-0.40) (1.07)

Islamic Window Dummy -0.000 -0.020*** -0.101*** 0.000 -0.016 -0.084* -0.001 -0.012*** -0.093*** (-0.27) (-4.49) (-5.14) (0.03) (-1.39) (-1.88) (-0.60) (-3.67) (-4.52)

Log of Total Asset 0.001** -0.023*** -0.009 0.001 -0.059*** -0.029 0.001** -0.008*** -0.003 (2.07) (-18.66) (-1.48) (0.94) (-14.13) (-1.63) (2.21) (-5.55) (-0.35)

LLPAGL 0.481*** 3.602*** 0.434*** 2.479*** 0.553*** 4.483*** (5.81) (10.15) (3.38) (4.93) (6.60) (9.17)

Equity to Asset Ratio 0.018** -1.279*** 0.026** -1.249*** 0.003 -1.084*** (2.26) (-12.43) (2.07) (-8.90) (0.26) (-6.01)

Inefficiency 0.019*** -0.069*** 0.018*** -0.076*** 0.020*** -0.039*** (10.40) (-12.17) (5.31) (-7.40) (8.96) (-7.53)

Loans Growth (%) -0.000 0.001 -0.001 (-0.12) (0.30) (-0.93)

Liquid Asset to Deposit & Short-Term Funding Ratio -0.004** -0.008*** -0.000

(-2.09) (-2.71) (-0.20)

ROAA 1.610*** 1.940*** 1.670*** (15.86) (10.67) (17.73)

Net Com. Trad. To Total Operating Income Ratio

-0.031 0.032 -0.042

(-0.56) (0.40) (-0.55)

State-Owned Bank Dummy 0.001 0.009* 0.050** 0.006 0.005 0.022 0.000 0.013*** 0.039 (0.58) (1.68) (2.08) (1.36) (0.38) (0.42) (0.02) (3.20) (1.52)

Foreign-Owned Bank Dummy 0.002 0.011* -0.092*** 0.009** 0.036*** -0.064 -0.005* -0.005 -0.085** (1.14) (1.75) (-3.30) (2.52) (3.41) (-1.52) (-1.88) (-0.86) (-2.24)

Subsidiary Bank Dummy 0.001 0.011** -0.059*** 0.003 0.030*** -0.121*** 0.001 0.002 0.022 (0.65) (2.52) (-3.14) (1.18) (3.87) (-4.05) (0.51) (0.41) (0.89)

Young Bank Dummy 0.005** 0.008 0.004 0.002 -0.012 -0.048 0.006** 0.001 0.016 (2.22) (1.05) (0.13) (0.38) (-0.98) (-0.97) (2.25) (0.23) (0.38)

Middle-Age Bank Dummy 0.005** -0.003 -0.066** 0.008** -0.020** -0.134*** 0.001 0.011* 0.013 (2.53) (-0.52) (-2.48) (2.52) (-2.12) (-3.59) (0.53) (1.95) (0.36)

Constant -0.036* 0.000 0.000 -0.026 0.000 0.000 0.000 0.000 0.000 (-1.86) (.) (.) (-1.05) (.) (.) (.) (.) (.)

Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,506 1,506 1,506 601 601 601 905 905 905

R-squared 0.151 0.469 0.409 0.191 0.542 0.461 0.185 0.581 0.473


32

Table 4 – Regression Estimates of Stability Model Using Logarithm of Z-Score and its Components as the Proxy


Variables Log Zscore Log SD(ROAA)

Log Zscore P1

Log Zscore P2 Log Zscore Log

SD(ROAA) Log

Zscore P1 Log

Zscore P2 Log Zscore Log SD(ROAA)

Log Zscore P1

Log Zscore P2

Islamic Bank Dummy 0.064 0.039 0.171 0.011 0.786 -0.766 0.460 0.703 -0.170 0.184 -0.027 -0.180 (0.37) (0.23) (0.94) (0.06) (1.34) (-0.98) (0.79) (0.92) (-0.90) (0.99) (-0.14) (-0.95)

Islamic Window Dummy 0.012 -0.065 0.154 -0.018 0.214 0.306 0.202 0.023 -0.078 -0.046 0.041 -0.095 (0.07) (-0.40) (0.79) (-0.11) (0.47) (0.58) (0.39) (0.04) (-0.46) (-0.29) (0.20) (-0.59)

Log of Mean of Total Asset 0.014 -0.126** 0.177*** 0.025 -0.003 -0.324 0.441* 0.116 0.048 -0.130** 0.085 0.030 (0.28) (-2.60) (3.61) (0.41) (-0.01) (-1.67) (1.95) (0.41) (0.67) (-2.18) (1.25) (0.41)

Mean of Total Assets Growth -0.329 0.408 0.366 -0.244 0.009 -1.384 -0.376 1.428 -0.195 0.549 0.439 -0.281 (-0.92) (1.16) (1.01) (-0.64) (0.01) (-1.44) (-0.45) (0.96) (-0.49) (1.60) (1.10) (-0.75)

Mean of Liquid Asset to Deposit & Short-Term Funding Ratio 0.641* -0.085 0.420* 0.802* 0.417 -0.032 0.986** 0.854 0.989 -0.705 0.278 0.929 (1.73) (-0.24) (1.73) (1.96) (1.18) (-0.07) (2.41) (1.40) (1.31) (-1.23) (0.56) (1.29)

Mean of Net Com. Trad. To Total Operating Income Ratio -1.355** 1.242 -0.771 -2.122* -1.087 2.967 -0.993 -3.969 -2.446** 1.258 -1.114 -2.254** (-2.00) (1.60) (-1.13) (-1.93) (-0.96) (1.61) (-0.73) (-1.35) (-2.61) (1.38) (-1.25) (-2.51)

Mean of Inefficiency -0.836*** 0.559** -0.557** -0.982*** 0.448 -0.460 0.031 0.163 -1.419*** 1.072*** -0.917*** -1.433*** (-3.12) (2.37) (-2.34) (-3.30) (0.99) (-1.05) (0.07) (0.29) (-4.09) (3.71) (-2.73) (-4.31)

State-Owned Bank Dummy -0.042 0.084 -0.160 -0.037 -0.186 0.214 -0.288 -0.182 (-0.23) (0.47) (-0.86) (-0.20) (-0.96) (1.09) (-1.33) (-0.96)

Foreign-Owned Bank Dummy -0.643*** 0.683*** -1.124*** -0.521** -0.699* 0.697** -1.054* -0.339 -0.667* 0.746*** -1.287*** -0.630* (-2.88) (4.24) (-3.81) (-2.37) (-1.89) (2.50) (-1.92) (-0.83) (-1.92) (2.78) (-2.94) (-1.87)

Subsidiary Bank Dummy -0.113 0.235 0.295** -0.143 0.559* -0.098 0.645** 0.538 -0.361 0.379* 0.051 -0.408 (-0.55) (1.34) (2.07) (-0.66) (1.98) (-0.35) (2.20) (1.53) (-1.35) (1.78) (0.26) (-1.41)

Constant 3.942*** -4.517*** -1.400 4.259*** 1.799 0.003 -6.701* 0.475 4.426*** -5.120*** 0.122 4.533*** (4.00) (-5.14) (-1.20) (4.08) (0.50) (0.00) (-2.00) (0.12) (3.06) (-4.34) (0.10) (3.17)

Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 203 205 194 204 61 63 57 62 142 142 137 142 R-squared 0.375 0.443 0.347 0.374 0.633 0.577 0.571 0.503 0.382 0.504 0.349 0.403 adj. R-sq 0.270 0.350 0.236 0.269 0.420 0.345 0.314 0.223 0.262 0.407 0.217 0.287 F-statistics . . . . . . . . 4.756 7.339 4.121 5.432 Acquiring banks are excluded from the sample, since the volatility on their assets returns can be due to the acquisition. Banks need to have at least five consecutive observations. Zscore=(M_ROAA+M_ETA)/SDROAA, M_ROAA = Mean of ROAA over the sample period, M_ETA=Mean of ETA over the sample period, SDROAA= standard deviation of ROAA over the sample period, Zscorep1=M_ROAA/SDROAA, Zscorep2=M_ETA/SDROAA, Robust t-statistics in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%.

33

Table 5 – Regression Estimates of Insolvency Risk Model using Unreserved Impaired Loans to Equity as the proxy (Dependent Variable)

Variables Full Sample Small Banks Sample Large Bank Sample

Islamic Bank Dummy 0.028 0.031 0.024 0.034 0.043 -0.095 -0.099 -0.114 -0.041 -0.015 0.038 0.053 0.050 0.035 0.038 (0.48) (0.54) (0.41) (0.60) (0.77) (-0.81) (-0.84) (-0.91) (-0.33) (-0.12) (0.66) (0.84) (0.80) (0.54) (0.58)

Islamic Window Dummy -0.043 -0.060 -0.064 -0.055 -0.041 -0.038 -0.041 -0.048 -0.009 0.039 -0.110 -0.118 -0.118 -0.098 -0.076 (-0.54) (-0.70) (-0.75) (-0.67) (-0.51) (-0.26) (-0.27) (-0.32) (-0.06) (0.26) (-1.44) (-1.53) (-1.53) (-1.31) (-1.11)

Log of Total Asset 0.022 0.020 0.027 0.035 0.037 0.037 0.020 0.012 0.024 0.022 0.015 0.028 (1.01) (0.89) (1.29) (1.64) (0.74) (0.63) (0.33) (0.20) (0.93) (0.87) (0.56) (1.11)

Liquid Asset to Deposit & Short-Term Funding Ratio

-0.047 -0.036 -0.077 -0.035 -0.021 -0.061 -0.003 -0.017 -0.082

(-0.82) (-0.62) (-1.32) (-0.44) (-0.25) (-0.67) (-0.03) (-0.17) (-1.10)

Inefficiency 0.137 0.126 0.183 0.132 0.216* 0.227* (1.47) (1.32) (1.23) (0.97) (1.89) (1.89)

Loans Growth (%) -0.108*** -0.152*** -0.076** (-3.36) (-2.71) (-2.06)

State-Owned Bank Dummy 0.076 0.077 0.095 0.087 0.106 -0.040 -0.031 -0.010 -0.064 -0.081 0.171 0.172 0.172 0.148 0.186* (0.80) (0.81) (0.99) (0.95) (1.14) (-0.33) (-0.26) (-0.08) (-0.51) (-0.65) (1.61) (1.62) (1.61) (1.39) (1.73)

Foreign-Owned Bank Dummy -0.015 -0.010 -0.009 0.090 0.144 0.057 0.060 0.045 0.117 0.170 0.016 0.010 0.008 0.059 0.103 (-0.11) (-0.07) (-0.06) (0.62) (0.81) (0.39) (0.40) (0.29) (0.70) (0.68) (0.07) (0.04) (0.03) (0.24) (0.39)

Subsidiary Bank Dummy -0.057 -0.040 -0.037 -0.057 -0.052 -0.107 -0.103 -0.084 -0.051 -0.110 -0.017 -0.004 -0.007 0.002 0.025 (-0.83) (-0.55) (-0.50) (-0.79) (-0.70) (-1.00) (-0.95) (-0.74) (-0.43) (-0.90) (-0.24) (-0.05) (-0.09) (0.02) (0.29)

Young Bank Dummy -0.158** -0.142* -0.130* -0.073 0.021 -0.248* -0.223* -0.223 -0.143 0.026 -0.043 -0.030 -0.031 -0.028 0.033 (-2.13) (-1.90) (-1.70) (-0.96) (0.29) (-1.89) (-1.71) (-1.60) (-0.96) (0.18) (-0.86) (-0.56) (-0.57) (-0.50) (0.61)

Middle-Age Bank Dummy -0.072 -0.067 -0.051 -0.039 0.027 -0.067 -0.059 -0.033 -0.020 0.003 -0.025 -0.022 -0.022 -0.017 0.088 (-1.48) (-1.40) (-1.00) (-0.74) (0.46) (-0.86) (-0.78) (-0.39) (-0.23) (0.04) (-0.44) (-0.38) (-0.37) (-0.28) (1.31)

Constant 0.647*** 0.058 0.494* -0.092 0.111 0.620*** -0.103 0.311 0.000 0.531 0.449** 0.000 0.000 -0.136 0.000 (4.49) (0.20) (1.75) (-0.22) (0.30) (4.08) (-0.18) (0.50) (.) (0.64) (2.16) (.) (.) (-0.28) (.)

Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 1,043 1,043 1,031 984 827 452 452 442 423 341 591 591 589 561 486 Number of Banks 264 264 262 253 241 142 142 139 135 120 161 161 160 155 152 R-sq. within group 0.185 0.187 0.184 0.187 0.202 0.173 0.176 0.178 0.187 0.258 0.227 0.229 0.229 0.223 0.189 R-sq. between group 0.210 0.207 0.214 0.237 0.249 0.280 0.279 0.273 0.183 0.217 0.281 0.281 0.281 0.301 0.330 R-sq. overall 0.193 0.191 0.192 0.225 0.245 0.183 0.186 0.181 0.187 0.231 0.314 0.311 0.312 0.330 0.347

Unreserved Impaired Loans = Impaired Loans – Loan Loss Reserves. Robust z-statistics in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%.

34

Figure (2) – Pay off to Equity holders and Depositors across different banks

(a) (b)

(c) (d)

(e) (f)

35

Table 6 - Correlation between return on average equity and Implicit Interest Expense Rate

across different banks

Observations at 25 Percentile

Observations between 25 & 75 Percentiles

Observations above 75 Percentile

Small Islamic Banks -0.042 (0.77)

-0.025 (0.80)

0.516*** (0.00)

Small Conventional Banks -0.076 (0.26)

0.017 (0.72)

-0.072 (0.28)

Small ISW 0.001 (0.99)

0.219* (0.07)

-0.123 (0.49)

Large Islamic Banks -0.457*** (0.00)

0.181* (0.06)

0.264** (0.05)

Large Conventional Banks -0.120 (0.10)

-0.092* (0.07)

0.040 (0.59)

Large ISW -0.307*** (0.01)

0.008 (0.92)

0.063 (0.57)

Islamic Banks -0.050 (0.61)

0.139** (0.04)

0.378*** (0.00)

Conventional Banks -0.102** (0.04)

-0.050 (0.15)

-0.004 (0.94)

ISW -0.219** (0.02)

0.044 (0.51)

0.029 (0.75)

Significance level in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%

36

Annex 1 – Description of Variables Used in the Analysis

Loans’ Risk Proxy Description

Problem Loans / Gross Loans

Problem Loans (PL) increases when a bank classifies a specific loan or a part of loans portfolio as the bad loans. It decreases when either a bank re-assesses a problem loan or a part of the portfolio of loans as the good loans or when a bank writes off a loan or a part of portfolio of loans.

Loan Loss Reserves / Gross Loans

Loan Loss Reserves (LLR) is considered for the whole loans portfolio, and not only for the Problems Loans. The managers assess the quality of the loans portfolio and determine the required reserves. Then the current level of LLR will be adjusted to reach to the required level. The adjustment will be reflected in the Loan Loss Provision stipulated in the income statement. When a bank decides to write off a loan, the loan amount would be deducted from the LLR.

Loan Loss Provisions / Average Gross Loans

Loan Loss Provision (LLP) is the incurred cost by banks as a result of adjusting the LLR or writing off a loan. Hence, despite of PL and LLR which are stock. LLP is flow and is stipulated in the income statement. It is possible to have a negative LLP in one period, when the required loan loss reserve is lower than the current reserve.

Insolvency Risk Proxy

Z-Score (ROAA+ETA)/SD(ROAA), wherein ROAA stands for Returns on Average Assets and ETA stands for Equity to Asset ratio. SD represents volatility of ROAA.

Unreserved Impaired Loans to Equity Ratio

Unreserved impaired loans is obtained by deducting loan loss reserves from the impaired loans.

Size Logarithm of Total Assets controls for size

Financial Ratios

Capital Equity to Asset Ratio is the proxy

Inefficiency Using the stochastic frontier approach, the cost inefficiency is estimated for each bank. The model and methodology is briefly presented in Annex 2.

Liquidity Represented by Liquid Asset to Deposit & Short-Term Funding Ratio

Asset Growth Calculated based on the annual growth of total assets

Growth of Gross Loans Calculated based on the annual growth of gross loans

Non-Interest Income Represented by Net Comm. & Trading Income to Total Operating Income Ratio

ROAA Stands for Returns on Average Assets

Ownership Structure

State-Owned Bank Dummy Takes one, in case the bank is state-owned, and zero otherwise.

Foreign-Owned Bank Dummy Takes one, in case the bank is Foreign-owned, and zero otherwise.

Subsidiary Dummy Takes one, in case the bank is subsidiary, and zero otherwise.

Banks’ Age Dummy

Young Bank Dummy Takes one, in case the bank operates for at most three years, and zero otherwise.

Middle-Aged Bank Dummy Takes one, in case the bank operates between three to seven years, and zero otherwise.

Year Dummies Seven dummy variables are supposed to control for the years effects.

Country Dummies Cross country variations are controlled by twenty one dummy variables.

37

Annex 2 – Estimating Cost Inefficiency

To estimate the cost inefficiency, we adopt the translog cost function which is the same

as the model applied by Altunbas et al. (2007). Two outputs (loans and other earning

assets) and three input prices (wage. interest expense rate and other operating expenses

price) are considered in the model. The empirical specification which is used to estimate

the cost inefficiency can be expressed as follows:

2 32

, , ,1 1 , ,1 1

2 2 3 3 2 3

, ,, , , , , ,, , , , , ,1 1 1 1 1 1

1ln ( ) ln ( ) ln2

1 ln ln ln ln ln ln2

i t k i tj kj kj i tj k

i t i tk i t m i t m i tjl km jmj i t l i t j i tj l k m j m

TC C t t t

V U

Qt P

Q Q QP P P

β γτ τ α δ

ψσ θ

= =

= = = = = =

= + + + + + +

+ + + + +

∑ ∑

∑∑ ∑∑ ∑∑

Wherein ,ln i tTC is the natural logarithm of total cost of the bank i at the time of t;

, ,ln

j i tQ is the natural logarithm of output vector of the bank i at the time of t. Loans and

Other Earning Assets are considered as the output;

, ,ln k i tP is the natural logarithm of input prices vector of the bank i at the time of t, and

consists of wage, interest expense rate and other operating expenses price; wage is

obtained by dividing personnel expenses on fixed assets, as a proxy for the number of

employees. Interest expense rate is the interest expense divided by deposits, short term

funding and other funding. Other operating expenses price is calculated as the ratio of

other operating expenses on total assets.

,i tV is the random variable which are assumed to be normally distributed with the

expected value of zero. 2(0, )vN σ .

,i tU is assumed to be equal to exp( ( ))i t TU η− − as proposed by Battese and Coelli (1992),

wherein iU is the non-negative random variable representing the cost inefficiency. It is

assumed to have a normal distribution of 2( , )uN µ σ truncated at zero. η is a parameter to

be estimated.

38

Annex 3 – Cross Country and Banks’ Types Sample Specification

Table A – Cross-Country Sample Specification

The table presents the number of observations for Islamic banks, Conventional banks with Islamic window or branch and Conventional banks across 22 countries, for the years 2001-2008.

Country Islamic bank Conventional Bank with Islamic Window Conventional Bank Total

Algeria 8 10 52 70 Bahrain 60 52 8 120 Bangladesh 37 55 163 255 Egypt 16 31 134 181 Gambia 6 0 25 31 Indonesia 12 51 324 387 Iran 67 0 0 67 Jordan 15 0 66 81 Kuwait 14 8 40 62 Lebanon 7 16 217 240 Malaysia 36 69 115 220 Mauritania 8 16 23 47 Pakistan 30 75 74 179 Qatar 16 9 38 63 Saudi Arabia 17 55 0 72 Senegal 4 0 59 63 Syria 2 0 32 34 Sudan 87 0 0 87 Tunisia 8 0 82 90 Turkey 13 0 96 109 UAE 28 20 107 155 Yemen 26 0 35 61 Total 517 467 1690 2674

Table B – Types of Banks Data Specification

This table presents the number of observations in terms of banks’ ownership and age (experience). The information is mainly obtained from banks web-sites.

Islamic bank Conventional Bank with Islamic Window Conventional Bank Total

State-owned Banks 64 51 240 355 Foreign-owned Banks 88 35 98 221 Subsidiaries 74 75 437 586 Private-owned Banks 291 306 915 1512 Total 517 467 1690 2674 Young Banks 104 30 106 240 Middle Aged Banks 76 36 136 248 Matured Banks 337 401 1448 2186 Total 517 467 1690 2674

State-owned banks: state ownership > 50%. Foreign-owned banks: foreign ownership > 50%. Subsidiaries: parent ownership = 100%. Private-owned banks: domestic private ownership > 50%. Young banks: operating less than 3 years. Middle aged banks: operating between 3 to 7 years. Matured banks: operating more than 7 years.

39

Annex 4 – Correlation Matrix

a b c d e f g h i j k l m n o p q r s

(a) Problem Loans on Gross Loans 1 (b) Loan Loss Reserves on Gross Loans 0.74 1

(c) Loan Loss Provisions on Average Gross Loans 0.23 0.30 1

(d) Unreserved Impaired Loans to Equity 0.57 0.37 0.17 1

(e) Islamic Bank Dummy -0.12 -0.18 0.02 -0.10 1 (f) Islamic Window Dummy -0.10 -0.07 -0.06 -0.03 -0.22 1 (g) Log of Asset -0.18 -0.19 -0.12 -0.12 -0.06 0.23 1 (h) Asset Growth -0.06 -0.05 -0.02 -0.04 0.05 -0.03 -0.03 1 (i) Equity Asset Ratio -0.06 0.00 -0.02 -0.32 0.09 -0.07 -0.30 0.03 1 (j) Inefficiency 0.22 0.22 0.15 0.20 -0.01 -0.09 0.11 -0.04 -0.25 1 (k) Liquid Assets to Deposit & Short Term Funding 0.09 0.14 -0.02 -0.10 0.02 -0.14 -0.24 0.00 0.35 0.00 1

(l) Growth of Gross Loans -0.26 -0.22 0.03 -0.19 0.15 -0.04 -0.04 0.56 0.06 -0.10 -0.01 1 (m) ROAA -0.33 -0.23 -0.22 -0.35 0.08 0.06 0.08 0.02 0.31 -0.24 0.07 0.11 1 (n) Net Comm. & Trading Rev. to Total Operating Income -0.02 0.12 0.19 0.06 0.03 -0.04 -0.14 0.00 -0.04 0.00 0.03 0.01 -0.09 1

(o) State-Owned Bank Dummy 0.11 0.06 0.08 0.06 -0.01 -0.04 0.14 -0.03 -0.09 -0.02 -0.04 -0.03 -0.02 0.01 1 (p) Foreign-Owned Bank Dummy 0.12 0.15 0.04 0.00 0.14 -0.01 -0.08 0.06 0.05 0.09 0.01 -0.01 -0.06 0.08 -0.09 1 (q) Subsidiary Dummy -0.05 0.04 0.00 -0.04 -0.09 -0.07 -0.16 0.00 0.11 -0.06 0.09 -0.02 0.01 0.10 -0.21 -0.16 1 (r) Young Bank Dummy -0.10 -0.08 0.04 -0.07 0.18 -0.04 -0.17 0.13 0.20 -0.03 0.09 0.33 0.00 0.02 -0.10 0.03 0.06 1 (s) Middle-Age Bank Dummy -0.03 -0.07 0.07 -0.03 0.09 -0.02 -0.15 0.01 0.01 -0.10 -0.03 0.08 0.00 0.09 -0.05 0.00 0.03 -0.10 1

40

Annex 5 - Regression Estimates of Loan Risk Model Using Loan Loss Reserves on Gross Loans (LLRGL) as the Proxy (SUR Simultaneous Approach)

Full Sample Small Banks Large Banks

Variables LLRGL Equity to Asset Ratio Inefficiency LLRGL Equity to Asset Ratio Inefficiency LLRGL Equity to Asset Ratio Inefficiency

Islamic Bank Dummy -0.030*** 0.002 0.039 -0.062*** 0.010 0.026 -0.013*** 0.004 0.047* (-5.63) (0.29) (1.60) (-5.06) (0.88) (0.50) (-2.59) (0.96) (1.80) Islamic Window Dummy -0.004 -0.015*** -0.072*** -0.027** 0.006 0.021 0.001 -0.014*** -0.083*** (-0.82) (-3.55) (-3.68) (-2.38) (0.57) (0.45) (0.30) (-4.18) (-4.21) Log of Total Asset -0.002* -0.019*** -0.002 -0.007 -0.046*** -0.001 -0.002 -0.007*** 0.004 (-1.84) (-15.90) (-0.28) (-1.51) (-11.42) (-0.03) (-1.22) (-5.11) (0.46) LLRGL 0.326*** 2.071*** 0.342*** 1.966*** 0.115*** 2.292*** (12.98) (18.25) (8.64) (11.55) (4.04) (14.19) Equity to Asset Ratio 0.232*** -1.652*** 0.261*** -1.746*** 0.054 -0.802*** (8.89) (-14.72) (6.01) (-10.40) (1.52) (-4.44) Inefficiency 0.098*** -0.079*** 0.113*** -0.082*** 0.086*** -0.031*** (17.50) (-14.69) (11.38) (-8.60) (13.60) (-5.59) Loans Growth (%) -0.014*** -0.014** -0.012*** (-4.25) (-2.46) (-2.90) Liquid Asset to Deposit & Short-Term Funding Ratio 0.019*** 0.021** 0.010*

(3.78) (2.41) (1.74) ROAA 1.476*** 1.810*** 1.400*** (16.17) (11.26) (15.37) Net Com. Trad. To Total Operating Income Ratio

-0.038 -0.018 -0.026

(-0.67) (-0.21) (-0.36) State-Owned Bank Dummy 0.005 0.007 0.029 0.009 0.003 0.015 0.010** 0.012*** -0.006 (0.91) (1.38) (1.17) (0.62) (0.21) (0.25) (2.06) (3.01) (-0.24) Foreign-Owned Bank Dummy 0.048*** -0.016*** -0.142*** 0.060*** -0.007 -0.091* 0.031*** -0.013** -0.155*** (7.66) (-2.69) (-4.91) (5.44) (-0.67) (-1.91) (4.32) (-2.12) (-4.30) Subsidiary Bank Dummy -0.003 0.017*** -0.031 -0.001 0.033*** -0.063* 0.003 0.003 0.006 (-0.78) (4.16) (-1.62) (-0.17) (4.57) (-1.89) (0.67) (0.71) (0.25) Young Bank Dummy 0.005 0.014** 0.036 -0.026* 0.020 0.033 0.021*** -0.000 0.019 (0.67) (2.04) (1.08) (-1.79) (1.51) (0.56) (2.66) (-0.01) (0.46) Middle-Age Bank Dummy -0.005 0.010* 0.011 0.001 0.012 -0.062 -0.003 0.005 0.051 (-0.84) (1.67) (0.37) (0.13) (1.16) (-1.37) (-0.44) (0.90) (1.40) Constant 0.000 0.462*** 0.000 0.003 0.000 1.287*** -0.042 0.000 1.622*** (.) (8.54) (.) (0.04) (.) (4.09) (-0.83) (.) (6.51) Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 1,402 1,402 1,402 518 518 518 884 884 884 R-squared 0.335 0.490 0.420 0.437 0.589 0.495 0.322 0.566 0.471


41

Annex 6 Table 6-1 – Regression Estimates of Loan Risk Model Using Problem Loans on Gross Loans (PLGL) as the Proxy (3SLS Approach)

Full Sample Small Banks Large Banks Variables PLGL Equity to Asset Ratio inefficiency PLGL Equity to Asset Ratio inefficiency PLGL Equity to Asset Ratio inefficiency

Islamic Bank Dummy -0.028** -0.052*** 0.085** -0.098* -0.290*** 0.152 0.013 0.004 -0.016 (-2.29) (-3.92) (2.21) (-1.71) (-3.86) (1.35) (1.13) (0.73) (-0.42) Islamic Window Dummy -0.010 0.022* -0.043 -0.001 0.081 -0.057 -0.010 -0.003 -0.052 (-0.38) (1.82) (-1.43) (-0.01) (1.63) (-0.92) (-0.41) (-0.63) (-1.49) Log of Total Asset -0.022*** -0.039*** 0.056*** -0.062 -0.159*** 0.046 -0.006 -0.008*** 0.023* (-6.30) (-12.94) (3.82) (-0.95) (-5.57) (0.78) (-1.64) (-5.06) (1.88) PLGL -1.292*** 2.670*** -4.193*** 2.536*** -0.345** 2.533** (-7.24) (4.63) (-5.37) (2.68) (-2.52) (2.48) Equity to Asset Ratio -0.706** 1.101** -1.112 -0.328 -0.828*** 2.530*** (-2.33) (2.37) (-0.60) (-0.42) (-6.23) (2.71) Inefficiency 0.154 0.569*** -0.115 1.684*** 0.130 0.112*** (0.69) (8.58) (-0.16) (3.46) (0.64) (5.90) Loans Growth (%) -0.013 -0.037 -0.018** (-1.01) (-0.78) (-2.04) Liquid Asset to Deposit & Short-Term Funding Ratio 0.039 0.047 0.009

(0.91) (0.50) (0.17) ROAA 1.061*** 3.955** 0.970*** (3.57) (2.03) (5.41) Net Com. Trad. To Total Operating Income Ratio

-0.034 -0.056 -0.008

(-1.19) (-1.25) (-0.11) State-Owned Bank Dummy 0.016 0.003 -0.013 0.057 0.102* -0.056 0.017** 0.010** -0.039 (1.01) (0.22) (-0.35) (1.03) (1.75) (-0.65) (2.18) (2.01) (-1.02) Foreign-Owned Bank Dummy 0.039** 0.077*** -0.141*** 0.079*** 0.329*** -0.191* -0.016 -0.003 -0.015 (2.03) (4.79) (-3.24) (3.47) (4.28) (-1.83) (-0.96) (-0.47) (-0.29) Subsidiary Bank Dummy 0.028 0.082*** -0.132*** 0.038 0.445*** -0.224*** 0.002 0.005 -0.019 (1.14) (6.99) (-4.72) (0.56) (4.22) (-3.20) (0.16) (1.07) (-0.62) Young Bank Dummy -0.023 -0.066*** 0.129** -0.049 -0.260*** 0.148 -0.023 -0.016* 0.163*** (-0.78) (-3.25) (2.39) (-1.60) (-3.17) (1.33) (-0.73) (-1.76) (2.68) Middle-Age Bank Dummy 0.011 0.040*** -0.053 0.009 0.250*** -0.122** -0.016 -0.007 0.081 (0.65) (2.59) (-1.29) (0.15) (2.98) (-2.00) (-1.22) (-0.88) (1.52) Constant 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 (.) (.) (.) (.) (.) (.) (.) (.) (.) Year Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,230 1,230 1,230 451 451 451 779 779 779


42

Annex 6 Table 6-2 – Regression Estimates of Loan Risk Model Using Loan Loss Reserves on Gross Loans (LLRGL) as the Proxy (3SLS Approach) Full Sample Small Banks Large Banks Variables LLRGL Equity to Asset Ratio Inefficiency LLRGL Equity to Asset Ratio inefficiency LLRGL Equity to Asset Ratio Inefficiency

Islamic Bank Dummy -0.022*** -0.412** 0.056* -0.079** 0.021 0.034 -0.008 -0.006 0.048 (-2.88) (-2.28) (1.82) (-2.42) (0.33) (0.46) (-1.18) (-1.07) (1.25) Islamic Window Dummy 0.016 0.407** -0.051** -0.023 0.012 0.020 -0.007 -0.002 -0.027 (1.10) (2.06) (-2.27) (-1.40) (0.28) (0.36) (-0.20) (-0.51) (-0.84) Log of Total Asset -0.014*** -0.328*** 0.037*** -0.038** 0.005 0.014 -0.005 -0.010*** 0.034** (-6.16) (-2.66) (3.87) (-2.43) (0.16) (0.46) (-1.52) (-5.55) (2.34) LLRGL -19.441*** 2.627*** 1.284 2.076*** -0.886*** 5.490*** (-2.63) (5.50) (1.54) (3.14) (-3.20) (3.75) Equity to Asset Ratio -0.005 -0.088 -0.629 -1.515*** -0.408*** 2.176*** (-0.02) (-0.33) (-0.99) (-4.37) (-4.10) (2.82) Inefficiency 0.343** 8.040** -0.068 -0.621** 0.051 0.170*** (2.52) (2.39) (-0.27) (-2.55) (0.14) (4.55) Loans Growth (%) -0.002 -0.009 -0.004 (-0.64) (-1.09) (-0.79) Liquid Asset to Deposit & Short-Term Funding Ratio 0.005 0.071* 0.022

(0.22) (1.88) (0.31) ROAA 4.421 0.157 0.892*** (1.45) (0.47) (5.16) Net Com. Trad. To Total Operating Income Ratio

0.007 -0.023 -0.021

(0.77) (-0.72) (-0.32) State-Owned Bank Dummy 0.000 -0.037 0.003 0.022 0.006 0.008 0.017*** 0.024*** -0.093** (0.02) (-0.39) (0.11) (1.02) (0.12) (0.12) (3.06) (3.88) (-2.08) Foreign-Owned Bank Dummy 0.062*** 1.281** -0.168*** 0.081*** -0.063 -0.104 0.022 0.024** -0.176*** (6.14) (2.50) (-4.53) (3.98) (-0.93) (-1.50) (0.80) (2.29) (-3.14) Subsidiary Bank Dummy 0.025*** 0.626** -0.073*** 0.014 -0.046 -0.077* 0.005 0.006 -0.024 (2.65) (2.42) (-3.37) (0.86) (-0.97) (-1.94) (0.94) (1.15) (-0.68) Young Bank Dummy -0.008 -0.185 0.027 -0.036* 0.024 0.039 0.020 0.007 -0.049 (-0.64) (-1.31) (0.73) (-1.73) (0.43) (0.57) (0.65) (0.76) (-0.74) Middle-Age Bank Dummy -0.004 -0.051 0.011 -0.009 -0.038 -0.062 -0.004 -0.007 0.055 (-0.50) (-0.48) (0.33) (-0.38) (-0.91) (-1.29) (-0.25) (-0.90) (1.07) Constant 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 (.) (.) (.) (.) (.) (.) (.) (.) (.) Year Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Observations 1,402 1,402 1,402 518 518 518 884 884 884


43

Annex 6 Table 6-3 – Regression Estimates of Loan Risk Model Using Loans Loss Provision on Average Gross Loans (LLPAGL) as the Proxy

(3SLS Approach) Full Sample Small Banks Large Banks

Variables LLPAGL Equity to Asset Ratio Inefficiency LLPAGL Equity to Asset Ratio Inefficiency LLPAGL Equity to Asset Ratio Inefficiency

Islamic Bank Dummy -0.150 -0.010 0.022 0.236 -0.045*** -0.116 0.012 0.003 -0.162 (-0.14) (-0.91) (0.63) (1.18) (-3.03) (-1.41) (0.49) (0.39) (-0.96) Islamic Window Dummy -0.533 0.019 -0.026 0.173 -0.021 -0.086* -0.030 -0.008* 0.133 (-0.14) (1.61) (-0.82) (1.19) (-1.36) (-1.75) (-0.41) (-1.85) (0.80) Log of Total Asset 0.001 -0.033*** 0.073*** 0.104 -0.051*** -0.045 0.002 -0.006*** 0.047 (0.02) (-9.67) (5.83) (1.06) (-6.81) (-0.94) (0.24) (-3.18) (1.07) LLPAGL -0.595 20.171*** -0.894 0.488 -1.089 46.249 (-0.59) (6.00) (-1.51) (0.06) (-0.96) (1.57) Equity to Asset Ratio -7.572 2.880*** 3.685 -1.582 -0.420 12.736 (-0.15) (5.56) (0.99) (-1.54) (-1.04) (1.54) Inefficiency -4.955 0.381*** 2.062 -0.161 -0.259 0.040 (-0.14) (4.86) (1.18) (-1.46) (-0.34) (1.30) Loans Growth (%) -0.047 0.053 -0.003 (-0.04) (0.95) (-0.25) Liquid Asset to Deposit & Short-Term Funding Ratio 0.939 -0.079 0.060 (0.17) (-0.36) (0.37) ROAA 2.167*** 1.214*** 1.040** (5.08) (2.67) (2.19) Net Com. Trad. To Total Operating Income Ratio

-0.491*** 0.011 -0.264

(-3.91) (0.04) (-0.66) State-Owned Bank Dummy 0.209 -0.010 -0.016 -0.069 0.017 0.037 0.007 0.012*** -0.160 (0.14) (-0.83) (-0.44) (-1.02) (1.04) (0.39) (0.57) (2.62) (-1.01) Foreign-Owned Bank Dummy -0.405 0.063*** -0.164*** 0.056 0.038** -0.031 -0.030 -0.012 0.399 (-0.13) (3.92) (-3.90) (1.12) (2.13) (-0.27) (-0.50) (-1.06) (1.12) Subsidiary Bank Dummy -0.362 0.049*** -0.131*** 0.205 0.018 -0.102* 0.004 0.002 -0.053 (-0.14) (4.38) (-4.52) (1.18) (0.86) (-1.68) (0.38) (0.45) (-0.47) Young Bank Dummy 0.162 0.007 -0.096* 0.060 -0.014 -0.049 0.019 0.009 -0.308 (0.10) (0.43) (-1.90) (0.99) (-0.96) (-0.85) (0.51) (0.87) (-1.08) Middle-Age Bank Dummy -0.259 0.025* -0.098** 0.251 -0.022 -0.125*** 0.003 0.012* -0.175 (-0.15) (1.86) (-2.47) (1.20) (-1.32) (-3.02) (0.29) (1.84) (-0.92) Constant 0.000 -0.149 0.000 0.000 0.000 0.000 0.000 0.000 0.000 (.) (-0.87) (.) (.) (.) (.) (.) (.) (.) Year Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 1,506 1,506 1,506 601 601 601 905 905 905


44

Annex 7 Table 7-1 – Regression Estimates of Loan Risk Model using Problem Loans on Gross Loans (PLGL) as the proxy

(Dependent Variable: PLGL) Variables Full Sample Small Banks Sample Large Banks Sample

Islamic Bank Dummy -0.030*** -0.030*** -0.031*** -0.032*** -0.024** -0.071*** -0.071*** -0.071*** -0.075*** -0.064*** -0.003 -0.003 -0.002 -0.002 0.006 (-2.68) (-2.59) (-2.64) (-2.63) (-2.01) (-3.53) (-3.53) (-3.43) (-3.55) (-2.90) (-0.32) (-0.24) (-0.17) (-0.15) (0.55) Islamic Window Dummy -0.021 -0.022* -0.020 -0.024* -0.026** -0.022 -0.023 -0.016 -0.023 -0.029 -0.018* -0.018* -0.015 -0.015 -0.015 (-1.58) (-1.67) (-1.49) (-1.75) (-1.96) (-0.81) (-0.85) (-0.57) (-0.82) (-1.02) (-1.81) (-1.84) (-1.56) (-1.58) (-1.48) Log of Total Asset -0.012*** -0.012*** -0.013*** -0.013*** -0.009** -0.030*** -0.033*** -0.034*** -0.031*** -0.027** -0.004 -0.006 -0.006 -0.006 -0.002 (-3.27) (-3.15) (-3.23) (-3.22) (-2.14) (-2.98) (-2.91) (-3.01) (-2.70) (-2.19) (-1.02) (-1.64) (-1.51) (-1.48) (-0.58) Equity to Asset Ratio -0.040 -0.024 -0.137* -0.063 -0.055 -0.010 -0.170 -0.044 -0.202*** -0.215*** -0.214*** -0.190** (-0.48) (-0.27) (-1.72) (-0.75) (-0.45) (-0.08) (-1.31) (-0.31) (-2.80) (-2.90) (-2.90) (-2.53) Inefficiency 0.038** 0.029* 0.033* 0.070** 0.054* 0.060* 0.041*** 0.041*** 0.035*** (2.08) (1.65) (1.82) (2.44) (1.84) (1.85) (3.38) (3.33) (2.72) Liquid Asset to Deposit & Short-Term Funding Ratio

-0.004 -0.016 -0.006 -0.027 0.001 -0.001

(-0.38) (-1.10) (-0.28) (-1.10) (0.08) (-0.12) Loans Growth (%) -0.036*** -0.036*** -0.029*** (-6.30) (-4.52) (-3.21) State-Owned Bank Dummy 0.031** 0.027* 0.016 0.020 0.021 0.017 0.018 -0.007 0.005 0.012 0.018 0.016 0.018 0.018 0.017 (2.02) (1.78) (1.11) (1.43) (1.60) (0.65) (0.64) (-0.28) (0.21) (0.51) (1.47) (1.33) (1.57) (1.59) (1.46) Foreign-Owned Bank Dummy 0.022 0.020 0.021 0.022 0.009 0.048* 0.048* 0.056* 0.060* 0.045 -0.001 -0.003 -0.009 -0.009 -0.025 (1.09) (0.97) (0.99) (0.95) (0.47) (1.78) (1.72) (1.82) (1.86) (1.35) (-0.05) (-0.12) (-0.33) (-0.34) (-1.14) Subsidiary Bank Dummy -0.005 -0.003 -0.003 0.001 0.001 0.013 0.015 0.023 0.029 0.018 -0.012 -0.011 -0.011 -0.011 -0.003 (-0.39) (-0.28) (-0.24) (0.11) (0.08) (0.70) (0.79) (1.16) (1.40) (0.86) (-1.18) (-1.11) (-1.10) (-1.10) (-0.34) Young Bank Dummy -0.056*** -0.057*** -0.056*** -0.058*** -0.039*** -0.081*** -0.082*** -0.085*** -0.084*** -0.059*** -0.017* -0.017* -0.014* -0.014* -0.007 (-5.47) (-5.77) (-5.64) (-5.77) (-3.37) (-4.91) (-5.26) (-5.30) (-5.32) (-3.38) (-1.96) (-1.91) (-1.65) (-1.66) (-0.86) Middle-Age Bank Dummy -0.004 -0.006 -0.005 -0.006 -0.004 0.001 -0.001 0.001 -0.000 -0.005 -0.017** -0.018** -0.016* -0.016* -0.005 (-0.38) (-0.63) (-0.51) (-0.59) (-0.43) (0.05) (-0.09) (0.04) (-0.02) (-0.35) (-2.16) (-2.19) (-1.93) (-1.90) (-0.58) Constant 0.209*** 0.217*** 0.217*** 0.191*** 0.135* 0.452*** 0.495*** 0.351*** 0.314** 0.325 0.109** 0.165*** 0.000 0.000 0.035 (3.76) (3.55) (4.28) (3.38) (1.74) (3.27) (3.12) (2.92) (2.25) (1.59) (2.00) (2.69) (.) (.) (0.56) Year Dummies ? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 1,756 1,748 1,673 1,669 1,419 777 769 733 729 593 979 979 940 940 826 Number of Banks 324 323 312 312 301 180 179 173 173 159 200 200 194 194 190 R-Sq: Within Group 0.155 0.152 0.162 0.189 0.222 0.113 0.104 0.120 0.151 0.175 0.239 0.253 0.251 0.251 0.278 R-Sq: Between Group 0.326 0.324 0.322 0.290 0.305 0.261 0.255 0.297 0.246 0.268 0.461 0.465 0.460 0.459 0.473 R-Sq: Overall 0.257 0.255 0.257 0.254 0.293 0.215 0.206 0.234 0.227 0.282 0.427 0.434 0.422 0.422 0.448

Robust z-statistics in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%

45

Annex 7 Table 7-2 – Regression Estimates of Loan Risk Model Using Loan Loss Reserve on Gross Loans (LLRGL) as the Proxy

(Dependent Variable: LLRGL) Variables Full Sample Small Banks Sample Large Banks Sample

Islamic Bank Dummy -0.033*** -0.032*** -0.033*** -0.032*** -0.029*** -0.074*** -0.071*** -0.063*** -0.062*** -0.060*** -0.011 -0.010 -0.009 -0.009 -0.009 (-4.48) (-4.23) (-4.21) (-4.08) (-3.88) (-5.95) (-5.44) (-4.76) (-4.66) (-4.41) (-1.55) (-1.43) (-1.23) (-1.23) (-1.23) Islamic Window Dummy -0.008 -0.008 -0.013* -0.013 -0.012 -0.020 -0.020 -0.021 -0.021 -0.025 -0.006 -0.006 -0.003 -0.003 -0.001 (-0.88) (-0.91) (-1.67) (-1.63) (-1.45) (-1.14) (-1.17) (-1.34) (-1.27) (-1.31) (-0.92) (-0.89) (-0.56) (-0.45) (-0.22) Log of Total Asset -0.013*** -0.014*** -0.010*** -0.009*** -0.008*** -0.024*** -0.028*** -0.024*** -0.022*** -0.019** -0.006** -0.008*** -0.006** -0.006** -0.004 (-4.12) (-4.57) (-3.39) (-3.09) (-2.63) (-3.47) (-3.51) (-2.85) (-2.63) (-2.25) (-2.45) (-2.98) (-2.51) (-2.35) (-1.50) Equity to Asset Ratio -0.044 -0.057 -0.075 -0.036 -0.083 -0.055 -0.064 -0.066 -0.125* -0.149** -0.164** -0.065 (-0.83) (-1.13) (-1.39) (-0.75) (-1.33) (-0.78) (-0.83) (-0.91) (-1.74) (-2.00) (-2.18) (-1.45) Inefficiency 0.039*** 0.037*** 0.038*** 0.054*** 0.054*** 0.055*** 0.039*** 0.035*** 0.033*** (3.94) (3.73) (3.52) (3.37) (3.34) (3.11) (4.60) (3.99) (3.33) Liquid Asset to Deposit & Short-Term Funding Ratio

0.014** 0.015*** 0.010 0.015** 0.019*** 0.018**

(2.29) (2.67) (1.18) (2.08) (2.74) (2.45) Loans Growth (%) -0.013*** -0.011** -0.007 (-3.16) (-2.56) (-1.17) State-Owned Bank Dummy 0.013* 0.015* 0.009 0.008 0.008 -0.002 0.005 -0.003 -0.005 0.001 0.011 0.011 0.009 0.008 0.007 (1.67) (1.82) (1.09) (0.98) (1.02) (-0.19) (0.45) (-0.27) (-0.38) (0.08) (1.23) (1.21) (1.11) (0.97) (0.84) Foreign-Owned Bank Dummy 0.034*** 0.034*** 0.036*** 0.038*** 0.037*** 0.039*** 0.043*** 0.042*** 0.043*** 0.042** 0.025 0.024 0.028 0.031 0.030 (3.10) (3.03) (2.93) (3.08) (2.87) (2.81) (2.86) (2.63) (2.67) (2.31) (1.50) (1.43) (1.47) (1.60) (1.46) Subsidiary Bank Dummy 0.001 0.003 0.006 0.006 0.005 0.007 0.011 0.015 0.015 0.009 -0.001 -0.001 0.002 0.001 0.004 (0.18) (0.34) (0.73) (0.77) (0.60) (0.57) (0.86) (1.16) (1.17) (0.66) (-0.19) (-0.21) (0.25) (0.14) (0.53) Young Bank Dummy -0.032*** -0.030*** -0.027*** -0.028*** -0.021*** -0.042*** -0.037*** -0.036*** -0.038*** -0.032*** -0.006 -0.005 -0.002 -0.002 0.007 (-4.70) (-4.23) (-4.21) (-4.32) (-2.82) (-4.10) (-3.57) (-3.56) (-3.66) (-2.79) (-0.88) (-0.78) (-0.25) (-0.32) (0.86) Middle-Age Bank Dummy -0.015*** -0.014*** -0.007 -0.008 -0.008 -0.008 -0.006 -0.000 -0.001 -0.003 -0.018*** -0.018*** -0.014*** -0.015*** -0.011* (-2.84) (-2.61) (-1.37) (-1.51) (-1.38) (-1.02) (-0.77) (-0.03) (-0.11) (-0.33) (-3.65) (-3.57) (-2.60) (-2.63) (-1.96) Constant 0.247*** 0.240*** 0.170*** 0.126** 0.133*** 0.336*** 0.378*** 0.328*** 0.000 0.214** 0.157*** 0.165*** 0.129*** 0.097** 0.085* (5.28) (4.99) (3.26) (2.56) (2.65) (3.91) (3.60) (2.60) (.) (2.07) (4.51) (3.93) (2.85) (2.27) (1.95) Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Number of Observations 2,196 2,184 1,891 1,885 1,586 987 975 835 829 660 1,209 1,209 1,056 1,056 926 Number of Banks 400 397 354 354 342 229 226 201 201 182 249 249 217 217 214 R-Sq: Within Group 0.119 0.121 0.145 0.148 0.182 0.065 0.075 0.111 0.104 0.117 0.165 0.180 0.217 0.236 0.252 R-Sq: Between Group 0.358 0.352 0.331 0.337 0.368 0.387 0.368 0.367 0.376 0.428 0.369 0.359 0.329 0.314 0.341 R-Sq: Overall 0.295 0.294 0.262 0.271 0.324 0.338 0.340 0.302 0.315 0.393 0.310 0.307 0.292 0.292 0.325


46

Annex 7 Table 7-3 – Regression Estimates of Loan Risk Model Using Loan Loss Provisions on Average Gross Loans (LLPAGL) as the Proxy

(Dependent Variable: LLPAGL) Variables Full Sample Small Banks Sample Large Banks Sample

Islamic Bank Dummy -0.001 -0.001 -0.001 -0.001 -0.002 -0.002 -0.002 -0.003 -0.003 -0.005 0.002 0.002 0.001 0.001 0.001 (-0.44) (-0.37) (-0.38) (-0.45) (-1.29) (-0.75) (-0.67) (-1.06) (-1.18) (-1.61) (1.16) (1.21) (0.76) (0.76) (0.87) Islamic Window Dummy -0.002 -0.002 -0.002 -0.002 -0.001 -0.002 -0.002 -0.001 -0.001 0.001 -0.002 -0.002 -0.002 -0.002 -0.002 (-0.87) (-0.90) (-0.94) (-0.96) (-0.62) (-0.46) (-0.52) (-0.20) (-0.21) (0.14) (-1.44) (-1.47) (-1.28) (-1.31) (-1.36) Log of Total Asset -0.000 -0.001 0.000 0.000 -0.000 0.000 -0.001 0.001 0.001 0.000 0.002*** 0.001** 0.001 0.001 0.001** (-0.22) (-1.13) (0.16) (0.02) (-0.11) (0.02) (-0.75) (0.67) (0.51) (0.18) (3.06) (2.07) (1.32) (1.17) (2.14) Equity to Asset Ratio -0.026** -0.023** -0.021* -0.029** -0.018 -0.011 -0.008 -0.020 -0.030 -0.045* -0.044* -0.037 (-2.41) (-2.19) (-1.94) (-2.12) (-1.60) (-0.92) (-0.67) (-1.17) (-1.07) (-1.70) (-1.66) (-1.40) Inefficiency 0.011*** 0.011*** 0.010*** 0.011*** 0.011*** 0.010* 0.011*** 0.012*** 0.010*** (4.02) (4.13) (3.30) (2.68) (2.71) (1.94) (3.48) (3.65) (3.06) Liquid Asset to Deposit & Short-Term Funding Ratio

-0.002 -0.002 -0.003 -0.003 -0.003 -0.002

(-1.47) (-1.33) (-1.03) (-1.09) (-1.40) (-0.93) Loans Growth (%) -0.001 -0.001 -0.001 (-0.41) (-0.57) (-0.46) State-Owned Bank Dummy 0.002 0.001 0.000 0.000 0.001 0.002 0.002 0.000 0.000 0.002 0.001 0.001 0.001 0.001 0.002 (0.67) (0.52) (0.13) (0.17) (0.58) (0.63) (0.49) (0.10) (0.16) (0.59) (0.67) (0.72) (0.50) (0.58) (0.90) Foreign-Owned Bank Dummy -0.002 -0.001 0.002 0.002 0.001 -0.003 -0.001 0.005 0.005 0.006 -0.004 -0.004 -0.004 -0.005 -0.007*** (-0.82) (-0.46) (0.75) (0.72) (0.34) (-0.70) (-0.27) (1.44) (1.45) (1.37) (-1.42) (-1.42) (-1.35) (-1.48) (-3.56) Subsidiary Bank Dummy -0.003 -0.003 -0.001 -0.001 0.001 -0.004 -0.003 -0.000 -0.000 0.000 -0.001 -0.001 -0.001 -0.001 0.002 (-1.44) (-1.22) (-0.47) (-0.41) (0.26) (-1.21) (-0.89) (-0.18) (-0.10) (0.11) (-0.61) (-0.61) (-0.42) (-0.38) (0.58) Young Bank Dummy 0.001 0.001 0.002 0.002 0.004 -0.002 -0.002 0.000 0.000 0.002 0.006** 0.006** 0.008*** 0.008*** 0.008*** (0.32) (0.56) (1.14) (1.14) (1.43) (-0.59) (-0.68) (0.07) (0.02) (0.38) (2.44) (2.44) (3.29) (3.32) (3.10) Middle-Age Bank Dummy 0.003 0.003 0.004* 0.004* 0.004* 0.005 0.004 0.006** 0.005* 0.006* 0.003 0.003 0.002 0.002 0.003 (1.60) (1.42) (1.91) (1.89) (1.88) (1.56) (1.31) (2.01) (1.93) (1.79) (0.88) (0.96) (1.04) (1.11) (1.05) Constant 0.007 0.060*** -0.015 -0.002 -0.001 0.004 0.064*** -0.026 0.000 0.007 -0.026*** -0.001 -0.029** -0.010 -0.020 (0.58) (5.33) (-1.31) (-0.18) (-0.07) (0.15) (3.08) (-0.98) (.) (0.19) (-2.67) (-0.05) (-2.40) (-0.75) (-1.52)

Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Number of Observations 2,418 2,405 2,069 2,064 1,725 1,159 1,146 965 960 759 1,259 1,259 1,104 1,104 966 Number of Banks 436 432 380 380 370 261 256 223 223 204 260 260 227 227 224 R-Sq: Within Group 0.008 0.020 0.028 0.031 0.042 0.012 0.017 0.028 0.030 0.050 0.010 0.028 0.052 0.055 0.048 R-Sq: Between Group 0.297 0.301 0.371 0.369 0.356 0.286 0.308 0.388 0.389 0.354 0.232 0.208 0.306 0.305 0.359 R-Sq: Overall 0.150 0.151 0.127 0.128 0.141 0.183 0.188 0.155 0.157 0.163 0.108 0.110 0.138 0.139 0.172


47

Annex 8 Regression Estimates of Stability Model Using Logarithm of 3-Year Rolling Window Zscore and its Components as the Proxy


Variables Log Zscore Log SDROAA

Log Zscore P1

Log Zscore P2 Log Zscore Log

SDROAA Log

Zscore P1 Log

Zscore P2 Log Zscore Log SDROAA

Log Zscore P1

Log Zscore P2

Islamic Bank Dummy 0.035 0.010 0.209 -0.015 0.329 -0.181 0.430 0.144 0.018 0.083 0.124 0.042 (0.23) (0.07) (1.36) (-0.10) (1.01) (-0.59) (1.63) (0.44) (0.11) (0.52) (0.67) (0.24) Islamic Window Dummy 0.038 -0.109 0.195 0.023 -0.074 0.213 -0.119 -0.113 0.094 -0.185 0.293* 0.114 (0.27) (-0.80) (1.13) (0.16) (-0.29) (0.76) (-0.32) (-0.44) (0.63) (-1.30) (1.68) (0.75) Log of Total Assets (-1) 0.009 -0.158*** 0.151*** -0.014 0.189* -0.436*** 0.456*** 0.169 0.032 -0.101** 0.070 0.015 (0.25) (-4.26) (3.10) (-0.37) (1.78) (-4.09) (3.09) (1.59) (0.59) (-1.96) (1.08) (0.27) Total Assets Growth (-1) -0.227** 0.136 -0.140 -0.248** -0.308** 0.201* -0.447*** -0.310** -0.197 0.157 0.030 -0.223* (-2.25) (1.47) (-1.19) (-2.45) (-2.09) (1.66) (-2.67) (-2.12) (-1.56) (1.28) (0.20) (-1.78) Liquid Assets to Deposits & Short-Term Funding Ratio (-1) 0.137 0.093 0.114 0.133 0.397** -0.210 0.567*** 0.383** -0.127 0.250 -0.247 -0.125 (1.19) (0.83) (0.97) (1.13) (2.48) (-1.38) (3.18) (2.43) (-0.85) (1.62) (-1.56) (-0.76) Net Com. Trad. to Total Operating Income Ratio (-1) -1.033*** 0.922*** -0.882** -0.970*** -0.241 0.345 -0.024 -0.242 -1.505*** 1.276*** -1.419*** -1.434*** (-3.07) (3.05) (-2.24) (-2.92) (-0.54) (0.76) (-0.05) (-0.55) (-3.59) (3.36) (-2.75) (-3.44) Inefficiency (-1) -0.216 0.064 -0.239 -0.246 0.005 -0.159 -0.286 0.043 -0.422 0.355 -0.286 -0.517* (-1.09) (0.34) (-1.08) (-1.24) (0.02) (-0.57) (-0.88) (0.16) (-1.56) (1.39) (-1.01) (-1.87) State-Owned Bank Dummy 0.043 -0.017 -0.015 0.036 0.083 0.044 0.090 0.012 0.065 -0.053 0.002 0.057 (0.25) (-0.10) (-0.07) (0.20) (0.28) (0.17) (0.25) (0.04) (0.34) (-0.29) (0.01) (0.28) Foreign-Owned Bank Dummy -0.438** 0.525*** -0.609** -0.424* -0.213 0.757*** -0.325 -0.180 -0.731** 0.443 -1.021** -0.701** (-2.01) (3.02) (-2.21) (-1.90) (-0.69) (3.10) (-0.99) (-0.56) (-2.16) (1.60) (-2.36) (-2.02) Subsidiary Bank Dummy -0.054 0.171 0.159 -0.064 -0.083 0.379** 0.095 -0.012 -0.124 0.170 -0.076 -0.182 (-0.40) (1.36) (1.02) (-0.47) (-0.39) (2.03) (0.37) (-0.06) (-0.72) (1.02) (-0.38) (-1.02) Constant 3.949*** -3.859*** -0.463 3.671*** 0.353 0.531 -5.134*** 0.374 5.634*** -5.462*** 2.486** 4.154*** (5.82) (-5.62) (-0.54) (5.29) (0.26) (0.36) (-2.70) (0.24) (5.87) (-5.64) (2.30) (4.21)

Year Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Country Dummies? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Number of Observations 1,191 1,207 1,151 1,197 447 457 420 449 744 750 731 748 Number of Bank 315 318 306 316 152 155 144 153 205 205 204 205 R-sq. within group 0.033 0.040 0.017 0.033 0.050 0.037 0.034 0.054 0.052 0.059 0.032 0.050 R-sq. between group 0.239 0.315 0.182 0.268 0.323 0.357 0.310 0.371 0.258 0.336 0.222 0.261 R-sq. overall 0.198 0.252 0.127 0.214 0.283 0.271 0.181 0.312 0.215 0.294 0.164 0.217

Acquiring banks are excluded from the sample, since the volatility on their assets returns can be due to the acquisition. Banks need to have three consecutive observations. Zscore=(ROAA+ETA)/SD(ROAA). ROAA = ROAA at time t. ETA = Equity to Asset Ratio at time t. SD(ROAA) = standard deviation of ROAA at time t. Zscorep1 = ROAA/SD(ROAA). Zscorep2 = ETA/SD(ROAA). Robust z-statistics in parentheses, * significant at 10%, ** significant at 5%, *** significant at 1%

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