Determinants of bank performance in a developing economy does bank origins matters

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DOI: 10.1177/097215091101300101
2012 13: 1Global Business Review
Fadzlan Sufian and Mohamad Akbar Noor Mohamad Noor
Matters?
Determinants of Bank Performance in a Developing Economy: Does Bank Origins


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Military-Madrasa-Mullah Complex 1
India Quarterly, 66, 2 (2010): 133–149
A Global Threat 1
Article
Determinants of Bank
Performance in a
Developing Economy:
Does Bank Origins Matters?
Fadzlan Sufian
Mohamad Akbar Noor Mohamad Noor
Abstract
The article seeks to examine the internal and external factors that influenced the performance of banks
operating in the Indian banking sector during the period 2000–08. The empirical findings from this study
suggest that credit risk, network embeddedness, operating expenses, liquidity and size have statistically
significant impact on the profitability of Indian banks. However, the impact is not uniform across banks
of different nations of origin. During the period under study, the empirical findings do not lend support
for the ‘limited form’ of global advantage hypothesis. Likewise, the liability of unfamiliarness hypothesis
is also rejected, since we do not find significant advantage accruing to foreign banks from other Asian
countries.
Keywords
Banks, profitability, origins, panel regression analysis, India
Introduction
Financial markets deregulation throughout the world has substantially transformed the economic behav-
iour of many developing countries. New economic policies, which promote free movement of capital,
played a pivotal role in almost every country in the world. The Indian financial sector is no exception and
has been changing substantially, just like in the other parts of the world. Although India began the dere-
gulation process later than many other countries, significant deregulatory measures have been imple-
mented in many aspects of the financial markets.
The financial sector is the backbone of the Indian economy and plays an important financial inter-
mediary role. Therefore, its health is critical to the health of the economy at large. Given the relation

Fadzlan Sufi an, Ph.D., is Professor at the IIUM Institute of Islamic Banking and Finance, International Islamic
University Malaysia. Mailing address: 205A Jalan Damansara, Damansara Heights, 50480, Kuala Lumpur, Malaysia.
E-mail: ; fadzlan.sufi
Mohamad Akbar Noor Mohamad Noor, Ph.D., is Executive Assistant to the President & CEO. Mailing
address: Offi ce of the President & CEO, No 7 Jalan Tasik, The Mines Resort City, 43300 Seri Kembangan, Selangor
Darul Ehsan, Malaysia. E-mail:
Global Business Review
13(1) 1–23
© 2012 IMI
SAGE Publications
Los Angeles, London,
New Delhi, Singapore,
Washington DC
DOI: 10.1177/097215091101300101

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Global Business Review, 13, 1 (2012): 1–23
2 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
between the well-being of the banking sector and the growth of the economy (Levine, 1998; Rajan and
Zingales, 1998), knowledge of the underlying factors that influence the performance of the banking sec-
tor is essential not only for the managers of the banks, but for numerous stakeholders such as the central
banks, bankers associations, governments and other financial authorities. Knowledge of these factors
would also help regulatory authorities to formulate going forward policies to improve the performance
of the Indian banking sector.
Over the last few years, a number of significant changes have occurred in the Indian financial system
as a result of its adaptation to new conditions, such as the deregulation of the national markets and the
internationalization of competition. At the regional level, the South Asian Association for Regional
Cooperation (SAARC) attempts to encourage cross-border trade and competition in financial services.
1

The seven SAARC member countries are also signatories to the South Asian Preferential Trading
Arrangement (SAPTA), Comprehensive Economic Partnership Agreement (CEPA), Indo-Lanka Bilateral
Free Trade Agreement (ILBFTA), Sri Lanka–Pakistan Free Trade Agreement (SLPFTA), etc. Furthermore,
negotiations are also underway to create a bilateral free trade agreement between Bangladesh and
India.
At the international level, the World Trade Organization (WTO) has encouraged the South Asian
countries to ensure fair and even-handed treatment from all market participants by stimulating economic
activity through certain policy bindings (WTO, 2005). These require member countries to remove dis-
criminatory policies against foreign banks and ensure level playing fields in financial services. However,
at present, restrictions remain on foreign banks in India. For example, foreign banks can be denied new
licences if the ratio of applicant’s assets to India’s total financial industry’s assets exceeds 15 per cent
(WTO, 2005). Furthermore, foreign banks must have local representative on their board with the approval
from the central bank of India (Reserve Bank of India).
It is reasonable to assume that these developments posed great challenges to financial institutions in
India as the environment in which they had been operating in changed rapidly, a fact that consequently
had an impact on the determinants of profitability of banks operating in the Indian banking sector. As
Golin (2001) points out, adequate earnings are required in order for banks to maintain solvency, to sur-
vive, grow and prosper in a competitive environment.
The purpose of the present study is to extend on the earlier works on the Indian banking sector and
examine the impact of origin on the performance of foreign banks operating in the Indian banking sector.
The article also investigates to what extent the performance of banks operating in India is influenced by
internal factors (that is, bank-specific characteristics) and to what extent by external factors (that is,
macroeconomic conditions and financial market structure). Although there exist a few microeconomic
studies which have examined the performance of the Indian banking sector (for example, Ataullah and
Le, 2006; Bhattacharyya et al., 1997a, 1997b; Bodla and Verma, 2007; Das and Ghosh, 2009; Das and
Shanmugam, 2004; Sarkar et al., 1998; Sathye, 2003), to the best of our knowledge, studies examining
the impact of origin on the performance of foreign banks operating in the Indian banking sector is com-
pletely missing from the literature.
Furthermore, apart from the few above-mentioned studies, virtually nothing has been published to
examine the global advantage hypothesis among the foreign banks operating in the Indian banking sec-

tor. In light of these knowledge gaps, the present article seeks to provide, for the first time, empirical evi-
dence on the impact of origin on the performance of the foreign-owned banks in India.
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Determinants of Bank Performance in a Developing Economy 3
This article is structured as follows. The next section reviews the related studies in the literature, fol-
lowed by a section that outlines the econometric framework. The section after that reports the empirical
findings. Finally, the last section concludes and offers avenues for future research.
Related Studies
The performance of the banking sector is a subject that has received a lot of attention in recent years. In
essence, empirical studies have mainly followed two alternative approaches, namely, the dealership and/
or the firm theoretic approach. On the one hand, the dealership approach, which was first proposed by
Ho and Saunders (1981) and further extended by McShane and Sharpe (1985), Allen (1988) and Angbazo
(1997), views banks as a dynamic dealer, setting interest rates on loans and deposits to balance the asym-
metric arrival of loan demands and deposit supplies. On the other hand, the firm theoretic approach,
originally developed by Klein (1971) and Monti (1972), views banking firms in a static setting where
demands and supplies of deposits and loans simultaneously clear both markets (see, among others, Wong,
1997; Zarruck, 1989).
Although the dealership approach reckons markets and institutions distortional effects, these factors
could not be directly incorporated into the model. To address this concern, the more recent studies have
also examined the influence of other internal (bank specific) and external (macroeconomic and market
specific) factors on bank profitability. Furthermore, the dealership approach assumes that regardless
of their ownership, banks apply similar business strategies and are exposed to a similar set of profit-
ability determinants. However, the assumption appears to be inappropriate, particularly for the develop-
ing countries, which have continuously embraced reforms and liberalization of the financial sector. To
overcome the shortcomings, some studies augment the empirical specification of the dealership approach
to capture the impact of bank ownership by introducing dummy variables into the estimation models
(Micco et al., 2007).
There is now a large literature which has examined the role played by management of resources in
determining bank performance. It is generally agreed that better quality management of resources is the

main factor contributing to bank performance, as evidenced by numerous studies focusing on the United
States (US) banking sector (Bhuyan and Williams, 2006; DeYoung and Rice, 2004; Hirtle and Stiroh,
2007; Stiroh and Rumble, 2006) and the banking sectors of the Western and developed countries
(Albertazzi and Gambacorta, 2009; Athanasoglou et al., 2008; Kosmidou and Zopounidis, 2008;
Kosmidou et al., 2007; Pasiouras and Kosmidou, 2007; To and Tripe, 2002; Williams, 2003).
By contrast, fewer studies have examined the performance of the banking sectors in developing coun-
tries. Chantapong (2005) investigates the performance of domestic and foreign banks in Thailand during
the period 1995–2000. All banks were found to have reduced their credit exposures during the crisis
years and have gradually improved their profitability levels during the post-crisis years. The results indi-
cate that the profitability of the foreign banks is higher than the average profitability of the domestic
banks. Despite that, during the post-crisis period, the gap between the foreign and domestic banks’ pro-
fitability has narrowed.
Fu and Heffernan (2010) examine the performance of different types of Chinese banks during the
period 1999–2006. The results suggest that economic value added and net interest margin (NIM) do bet-
ter than the more conventional measures of profitability, namely, return on average assets (ROAA) and
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4 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
return on average equity (ROAE). Some macroeconomic variables and financial ratios are significant
with the expected signs. Though the type of bank is influential, bank size is not. Neither the percentage
of foreign ownership nor bank listings have a discernable effect.
Ben Naceur and Goaied (2008) examine the impact of bank characteristics, financial structure and
macroeconomic conditions on Tunisian banks’ NIM and profitability during the period 1980–2000. They
suggest that banks that hold a relatively high amount of capital and higher overhead expenses tend to
exhibit higher NIM and profitability levels, while size is negatively related to bank profitability. During
the period under study, they find that stock market development has positive impact on bank profitabil-
ity. The empirical findings suggest that private banks are relatively more profitable than their state-
owned counterparts. The results suggest that macroeconomic conditions have no significant impact on
Tunisian banks’ profitability.
More recently, Sufian and Habibullah (2009) investigated the determinants of the profitability of the

Chinese banking sector during the post-reform period of 2000–05. They found that liquidity, credit risk
and capitalization have positive impacts on the state-owned commercial banks’ profitability, while the
impact of overhead cost is negative. They suggest that the joint stock commercial banks with higher
credit risk tend to be more profitable, while higher overhead cost results in lower joint stock commercial
banks’ profitability levels. They find that larger size and higher overhead costs result in a lower city com-
mercial banks’ profitability, while the more diversified and relatively better capitalized city commercial
banks exhibit higher profitability levels. The impact of economic growth is positive, while growth in
money supply is negatively related to the state-owned commercial banks and city commercial banks’
profitability levels.
The empirical literature on Indian banking sector has largely examined the differences in efficiency
and profitability across private and state-owned banks as opposed to differences across foreign and
domestic banks (for example, Ataullah and Le, 2006; Bhattacharyya et al., 1997b; Bodla and Verma,
2007; Das and Ghosh, 2009; Das and Shanmugam, 2004; Sarkar et al., 1998; Sathye, 2003). Overall, the
empirical findings indicate that the private-owned banks in India have been relatively more profitable
than their public sector bank counterparts (for example, De, 2003).
Data and Methodology
We use annual bank-level data over the period 2000–08. The variables are obtained from various issues
of Report on Trend and Progress of Banking in India and Statistical Tables Relating to Banks in India.
The macroeconomic variables are retrieved from International Monetary Fund (IMF) Financial Statistics
(IFS) database. The number of observations varied across time due to entry and exit of banks and missing
observations for certain banks during the sample period. The sample represents the whole gamut of the
industry’s total assets.
Performance Measure
Following Goddard et al. (2004b), Kosmidou (2008) and Sufian and Habibullah (2009), among others,
the dependent variable used in this study is return on asset (ROA). The ROA shows the profit earned per
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Determinants of Bank Performance in a Developing Economy 5
dollar of assets and most importantly, reflects the management’s ability to utilize the bank’s financial and
real investment resources to generate profits (Hassan and Bashir, 2003). For any bank, ROA depends on

the bank’s policy decisions as well as uncontrollable factors relating to the economy and government
regulations. Rivard and Thomas (1997) suggest that bank profitability is best measured by ROA, as the
ratio is not distorted by high equity multipliers. Moreover, ROA represents a better measure of the ability
of the firm to generate returns on its portfolio of assets.
Essentially, the ROE–ROA relationship illustrates the fundamental trade-off banks face between risk
and return, whereas the equity multiplier reflects the leverage or financing policies, that is, the sources
(debt or equity) chosen to fund the bank. Banks with lower leverage and thus higher equity, generally
report higher ROA, but lower return on equity (ROE). Athanasoglou et al. (2008) argue that an analysis
based on ROE disregards the risks associated with leverage, often a consequence of regulation. On the
other hand, Goddard et al. (2004b) employ ROE as a profitability measure, arguing that for many
European banks, the off-balance sheet business makes a significant contribution to total profit. The earn-
ings generated from these activities are excluded from the denominator of ROA.
Internal Determinants
The bank-specific variables included in the regression models are: loans loss provisions divided by total
loans (LLP/TL); log of total deposits (LNDEPO); book value of stockholders’ equity as a fraction of
total assets (EQASS); total overhead expenses divided by total assets (NIE/TA); non-interest income
divided by total assets (NII/TA); total loans divided by total assets (LOANS/TA); and log of total assets
(LNTA).
The ratio of loan loss provisions to total loans, LLP/TL, is incorporated as an independent variable in
the regression analysis as a proxy of credit risk. The coefficient of LLP/TL is expected to be negative. In
this direction, Miller and Noulas (1997) suggest that the greater the exposure of banks to high risk loans,
the higher would be the accumulation of unpaid loans and profitability would be lower. Miller and
Noulas (1997) point out that decline in loan loss provisions are, in many instances, the primary catalyst
for increases in profit margins. Furthermore, Thakor (1987) also suggests that the level of loan loss pro-
visions is an indication of banks asset quality and signals changes in the future performance.
The variable LNDEPO is included in the regression models as a proxy variable for network embed-
dedness. It would be reasonable to assume that banks with large branch networks are able to attract more
deposits, which is a cheaper source of funds. The earlier studies by, among others, Chu and Lim (1998)
point out that the large banks may attract more deposits and loan transactions and in the process, com-
mand larger interest rate spreads. On the other hand, Randhawa and Lim (2005) suggest that the smaller

banks with smaller depositors base might have to resort to purchasing funds in the inter-bank market,
which is costlier.
The EQASS variable is included in the regression models to examine the relationship between profit-
ability and bank capitalization. Strong capital structure is essential for banks in developing economies,
since it provides additional strength to withstand financial crises and increased safety for depositors dur-
ing unstable macroeconomic conditions (Sufian, 2009). Furthermore, lower capital ratios in banking
imply higher leverage and risk, therefore greater borrowing costs. Thus, the profitability level should be
higher for the better capitalized banks.
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6 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
The ratio of non-interest expenses to total assets, NIE/TA, is used to provide information on the vari-
ations of banks operating costs. The variable represents total amount of wages and salaries, as well as the
costs of running branch office facilities. The relationship between the NIE/TA variable and profitability
levels is expected to be negative, because the more productive and efficient banks should keep their
operating costs low. Furthermore, the usage of new electronic technology, like automated teller machines
(ATMs) and other automated means of delivering services, may have caused expenses on wages to fall
(as capital is substituted for labour).
To recognize that financial institutions in recent years have increasingly been generating income
from ‘off-balance sheet’ and fee-generating business, the ratio of non-interest income over total assets
(NII/TA) is entered in the regression analysis as a proxy of non-traditional activities. Non-interest income
consists of commission, service charges and fees; net profit from sale of investment securities; and for-
eign exchange profits. The variable is expected to exhibit positive relationship with bank profitability
levels.
An important decision that the managers of commercial banks must take refers to the liquidity man-
agement, and specifically to the measurement of their needs related to the process of deposits and loans.
For that reason, the ratio of total loans to total assets (LOANS/TA) is used as a measure of liquidity. It
should be noted that higher figures denote lower liquidity. Without the required liquidity and funding to
meet obligations, a bank may fail. Thus, in order to avoid insolvency problems, banks often hold liquid
assets, which can be easily converted to cash. However, liquid assets are usually associated with lower

rates of return. It would therefore be reasonable to expect higher liquidity to be associated with lower
bank profitability.
The LNTA variable is included in the regression models to capture for the possible cost advantages
associated with size (economies of scale). In the literature, mixed relationships have been found between
size and profitability. The LNTA variable is also used to control for cost differences related to bank size
and for the greater ability of the larger banks to diversify. In essence, LNTA may lead to positive effects
on bank profitability if there are significant economies of scale. On the other hand, if increased diversifi-
cation leads to higher risks, the variable may exhibit negative effects.
External Determinants
If analysis is done in a static setting, it may fail to capture developments in the regulatory environment
and in the marketplace, which may have changed the underlying production technology and the asso-
ciated production functions. Furthermore, different financial institution forms could demonstrate differ-
ent reactions to changes in the marketplace. Hence, the change in the financial landscape and structure,
etc., may vary across banking groups (Berger et al., 1995; Button and Weyman–Jones, 1992; Saunders
et al., 1990). To measure the relationship between economic and market conditions and bank profitabil-
ity, natural log of gross domestic product (GDP) (LNGDP), the annual inflation rate (INFL), money sup-
ply growth (MSG), the three banks concentration ratio (CR3) and the ratio of stock market capitalization
divided by GDP (MKTCAP/GDP) are used.
The GDP is among the most commonly used macroeconomic indicator to measure total economic
activity within an economy. The GDP is expected to influence numerous factors related to the supply and
demand for loans and deposits. It would be reasonable to expect favourable economic conditions to
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Determinants of Bank Performance in a Developing Economy 7
positively influence the demand and supply of banking services. Another important macroeconomic
condition which may affect both the costs and revenues of banks is the inflation rate (INFL). Staikouras
and Wood (2003) point out that inflation may have direct effects, that is, increase in the price of labour,
and indirect effects, that is, changes in interest rates and asset prices, on the profitability of banks. Perry
(1992) suggests that the effects of inflation on bank performance depend on whether the inflation is
anticipated on unanticipated. In the anticipated case, the interest rates are adjusted accordingly, resulting

in revenues to increase faster than costs and subsequently, having positive impact on bank profitability.
On the other hand, in the unanticipated case, banks may be slow in adjusting their interest rates resulting
in a faster increase of bank costs than bank revenues and consequently, having negative effects on bank
profitability.
The changes in money supply may lead to changes in the nominal GDP and the price level. Although
money supply is basically determined by the central bank’s policy, it could also be affected by the behav-
iour of households and banks. Following, among others, Kosmidou (2008), the growth of money supply
(MSG) is used in this study. Mamatzakis and Remoundos (2003) used money supply as a measure of
market size and found that the variable significantly affects bank profitability.
To examine the impact of concentration and competition on bank performance, the CR3 and MKTCAP/
GDP variables are introduced in the regression models. The CR3 ratio is calculated as the total assets
held by the three largest banks in the country. The variable is used to examine the impact of asset con-
centration on the profitability of Indian banks. The structure–conduct–performance (SCP) theory posits
that banks in a highly concentrated market tend to collude, and therefore earn monopoly profits (Molyneux
et al., 1996). Berger (1995) points out that the relationship between bank concentration and performance
in the US depends critically on what other factors are held constant. The MKTCAP/GDP ratio is com-
puted as the ratio of stock market capitalization as a fraction of the national GDP. The variable is entered
in the regression model to examine the impact of competition from the stock market.
Table 1 lists the variables used to proxy profitability and its determinants. We also include the nota-
tion and the expected impact of the determinants according to the literature.
Table 2 presents the summary statistics of the dependent and the explanatory variables.
Econometric Specification
To test the relationship between bank profitability and bank-specific and macroeconomic determinants
described earlier, we estimate a linear regression model in the following form:
ln (
π
)
it
=
α

+
β
1
ln (LLP/TL)
it
+
β
2
ln (LNDEPO)
it
+
β
3
ln (EQASS)
it
+
β
4
ln (NIE/TA)
it
+
β
5
ln (NII/TA)
it
+
β
6
ln (LOANS/TA)
it

+
β
7
ln (LNTA)
it
+
ζ
1
ln (GDP)
t
+
ζ
2
ln (INFL)
t
+
ζ
3
ln (MSG)
t

+
δ
1
ln (CR3)
t
+
δ
2
ln (MKTCAP/GDP)

t
+
ε
it

ε
it
= v
it
+ u
it
(1)
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India Quarterly, 66, 2 (2010): 133–149
8 Sanjeeb Kumar Mohanty

and Jinendra Nath Mahanty8 Michael Lindfield
Table 1. Description of the Variables Used in the Regression Models
Variable Description
Hypothesized
Relationship
Dependent
ROA The return on average total assets of the bank in year t.NA
Independent
Internal Factors
LLP/TL Loan loss provisions/total loans. An indicator of credit risk, which shows how much a bank is
provisioning in year t relative to its total loans.
–
LNDEPO A proxy measure of network embeddedness, calculated as the log of total deposits of bank j in year t.
+/–

EQASS A measure of bank’s capital strength in year t, calculated as equity/total assets. High capital asset ratio is
assumed to be indicator of low leverage and therefore, lower risk.
+
NIE/TA Calculated as non-interest expense/total assets and provides information on the efficiency of the
management regarding expenses relative to the assets in year t. Higher ratios imply a less efficient
management.
–
NII/TA A measure of diversification and business mix, calculated as non-interest income/total assets.
+
LOANS/TA A measure of liquidity, calculated as total loans/total assets. The ratio indicates what percentage of the
assets of the bank is tied up in loans in year t.
–
LNTA The natural logarithm of the accounting value of the total assets of the bank in year t.
+/–
External Factors
LNGDP Natural logarithm of gross domestic products.
+
INFL The rate of inflation.
+/–
MSG The growth of money supply measured by currency in circulation.
+
CR3 The three largest banks asset concentration ratio. –
MKTCAP/GDP The ratio of stock market capitalization as a fraction of the national GDP. The variable serves as a proxy
of financial development.
–
Bank Origins
DUMAMER A dummy variable that takes a value of 1 for foreign banks from the North America, 0 otherwise.
+/
DUMEURO A dummy variable that takes a value of 1 for foreign banks from the European countries, 0 otherwise.
+/

DUMASIA A dummy variable that takes a value of 1 for foreign banks from other Asian countries, 0 otherwise.
+/
DUMMENA A dummy variable that takes a value of 1 for foreign banks from the Middle East and North Africa
(MENA) region, 0 otherwise.
+/
Source: Authors’ own calculations.
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India Quarterly, 66, 2 (2010): 133–149
A Global Threat 9
Table 2. Summary Statistic of Dependent and Explanatory Variables
ROA LLP/TL LNDEPO EQASS NIE/TA NII/TA LOANS/TA LNTA LNGDP INFL MSG CR3 MKTCAP/GDP
Mean 1.004 10.017 12.870 7.654 2.762 2.683 45.090 13.277 10.103 5.192 16.933 0.341 1.333
Min –18.872 –711.468 5.298 0.000 0.000 –1.011 0.000 5.976 9.833 3.774 12.100 0.329 0.550
Max 82.385 2817.000 17.800 154.516 147.949 97.970 815.776 18.094 10.416 10.746 21.485 0.357 3.443
Std. Dev. 3.577 124.866 2.370 15.569 6.185 6.199 36.157 2.099 0.195 2.113 2.938 0.009 0.908
Source: Authors’ own calculations.
Note: The table presents the summary statistics of the variables used in the regression analysis.
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10 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
where ‘i’ denotes the bank; ‘t’ the examined time period; and
ε
is the disturbance term, with v
it
capturing
the unobserved bank-specific effect and u
it
is

the idiosyncratic error and is independently identically dis-
tributed (i.i.d), e
it
∼ N(0,
σ
2
).
Following De Bandt and Davis (2000) and Staikouras et al. (2008), among others, the log linear form
is chosen as it typically improves the regression models goodness-of-fit and may reduce simultaneity
bias. We apply the least square method of fixed effects model (FEM). The opportunity to use a fixed
effects rather than a random effects model (REM) has been tested with the Hausman test. Furthermore,
equation (1) is estimated by using White’s (1980) transformation to control for cross-section hetero-
scedasticity of the variables. Nevertheless, as a robustness check, we have also performed the regression
models by using the REM.
Table 3 provides information on the degree of correlation between the explanatory variables used in
the panel regression analysis. The matrix shows that, in general, the correlation between the bank-
specific variables is not strong, suggesting that multicollinearity problems are not severe or non-existent.
Kennedy (2008) points out that multicollinearity is a problem when the correlation is above 0.80, which
is not the case here. However, it is worth noting that the correlation between LNGDP and MKTCAP/
GDP variables is relatively high. To address this concern, we have estimated all the regression models
by excluding the MKTCAP/GDP variable. All in all, the results do not qualitatively change the findings.
Therefore, we choose not to report the regression results in the article, but they are available upon
request.
Empirical Findings
The regression results focusing on the relationship between bank profitability and the explanatory vari-
ables are presented in Table 4. To conserve space, the full regression results, which include the bank-
specific fixed effects, are not reported in the article. Several general comments regarding the test results
are warranted. First, the model performs reasonably well with most variables remaining stable across the
various regressions tested. Second, the explanatory power of the models is reasonably high, while the
F-statistics for all models is significant at the 1 per cent level. Third, the adjusted R

2
is considerably
higher than those obtained by Kosmidou et al. (2007), Staikouras and Wood (2003) and Williams
(2003).
Referring to the impact of credit risk, the coefficient of LLP/TL exhibits a positive sign and is statistic-
ally significant at the 10 per cent level or better. The result is in consonance with Berger and DeYoung’s
(1997) skimping hypothesis. To recap, Berger and DeYoung (1997) suggest that under the skimping
hypothesis, a bank maximizing long-run profits may rationally choose to have lower costs in the short-
run by skimping on the resources devoted to underwriting and monitoring loans, but bear the conse-
quences of greater loan performance problems.
It is observed from Column 1 of Table 4 that the impact of network embeddedness (LNDEPO) on
bank profitability is negative and is statistically significant in the FEM regression models. The empirical
findings lend support to the earlier findings by, among others, Randhawa and Lim (2005). During the
period under study, capital strength as measured by EQASS is positively related to Indian banks’ profit-
ability. The empirical finding is consistent with Berger (1995), Demirguc–Kunt and Huizinga (1999),
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India Quarterly, 66, 2 (2010): 133–149
A Global Threat 11
Table 3. Correlation Matrix for the Explanatory Variables
Explanatory
Variables LLP/TL LNDEPO EQASS NIE/TA NII/TA LOANS/TA LNTA LNGDP INFL MSG CR3 MKTCAP/GDP
LLP/TL 1.000
–0.076
∗
0.085
∗
0.004
0.098
∗∗

–0.072 –0.056 –0.046 –0.062 –0.047 –0.035 –0.002
LNDEPO 1.000
–0.665
∗∗
–0.101
∗∗
–0.228
∗∗
0.175
∗∗
0.097
∗∗
0.180
0.160
∗∗
0.106
∗∗
–0.063
–0.143
∗∗
EQASS 1.000
0.273
∗∗
0.390
∗∗
–0.052
–0.605
∗∗
–0.011 –0.008 0.008 –0.002 0.016
NIE/TA 1.000

0.581
∗∗
0.581
∗∗
–0.177
∗∗
–0.098
∗∗
–0.064 –0.069
0.101
∗∗
0.092
∗
NII/TA 1.000
0.332
∗∗
–0.290
∗∗
–0.164
∗∗
–0.084
∗
–0.069
0.117
∗∗
0.206
∗∗
LOANS/TA 1.000
0.103
∗∗

0.040 0.055 0.032 0.018 –0.040
LNTA 1.000 0.216
0.186
∗∗
0.133
∗∗
–0.090
∗
–0.178
∗∗
LNGDP 1.000
0.794
∗∗
0.652
∗∗
–0.489
∗∗
–0.822
∗∗
INFL 1.000
0.547
∗∗
–0.020
–0.458
∗∗
MSG 1.000
–0.554
∗∗
–0.334
∗∗

CR3 1.000
0.598
∗∗
MKTCAP/GDP 1.000
Source: Authors’ own calculations.
Notes: (i) The table presents the results from Pearson correlation coefficients.
(ii)
∗∗
and
∗
indicate significance at 1% and 5% levels respectively.
(iii) LLP/TL is a measure of bank credit risk, calculated as the ratio of total loan loss provisions divided by total loans; LNDEPO is a proxy measure for network
embeddedness, calculated as natural logarithm of total deposits; EQASS is a measure of capitalization, calculated as book value of shareholders equity as a
fraction of total assets; NIE/TA is a proxy measure for management quality, calculated as personnel expenses divided by total assets; NII/TA is a measure
of bank diversification towards non-interest income, calculated as total non-interest income divided by total assets; LOANS/TA is used as a proxy measure
of loans intensity, calculated as total loans divided by total assets; LNTA is a proxy measure of size, calculated as a natural logarithm of total bank assets;
LNGDP is natural log of GDP; INFL is the rate of inflation; MSG is the growth of money supply measured by currency in circulation; CR3 is the three bank
concentration ratio; MKTCAP/GDP is the ratio of stock market capitalization over GDP.
by guest on February 22, 2014gbr.sagepub.comDownloaded from
India Quarterly, 66, 2 (2010): 133–149
12 Sanjeeb Kumar Mohanty

and Jinendra Nath Mahanty12 Michael Lindfield
Table 4. Panel Fixed and Random Effects Regression Results
(1)
FEM
(2)
FEM
(4)
REM

(5)
REM
(6)
REM
(7)
REM
(8)
REM
(9)
REM
CONSTANT
–3.939
∗∗
(–2.047)
6.006
(0.299)
–3.149
∗∗∗
(–3.947)
5.227
(0.283)
5.222
(0.283)
4.929
(0.267)
5.163
(0.281)
5.349
(0.288)
Bank Characteristics

LLP/TL
0.001
∗
(1.911)
0.001
∗
(1.768)
0.001
∗∗
(1.947)
0.001
∗∗
2.071)
0.001
∗∗
(2.080)
0.001
∗∗
(2.319)
0.001
∗∗
(2.170)
0.001
∗∗
(2.033)
LNDEPO
–0.364
∗
(–1.787)
–0.344

∗∗
(–2.267)
–0.228
(–1.307)
–0.190
(–1.134)
–0.196
(–1.123)
–0.258
(–1.429)
–0.177
(–1.068)
–0.177
(–0.970)
EQASS 0.023
(1.099)
0.016
0.910)
0.010
(0.890)
0.004
(0.471)
0.004
(0.448)
0.003
(0.352)
0.003
0.232)
0.004
(0.430)

NIE/TA
0.589
∗∗∗
(14.032)
0.585
∗∗∗
(13.397)
0.572
∗∗∗
(10.160)
0.578
∗∗∗
(10.333)
0.578
∗∗∗
(10.344)
0.578
∗∗∗
(10.416)
0.575
∗∗∗
(9.966)
0.577
∗∗∗
(10.276
NII/TA
0.037
(0.622)
0.053
(1.230)

0.033
(0.960)
0.044
∗
(1.610)
0.045
(1.596)
0.046
∗
(1.668)
0.043
(1.333)
0.044
(1.602)
LOANS/TA
–0.028
∗∗∗
(–3.494)
–0.031
∗∗∗
(–4.436)
–0.025
∗∗
(–2.404)
–0.027
∗∗∗
(–2.699)
–0.027
∗∗∗
(–2.704)

–0.026
∗∗∗
(–2.753)
–0.026
∗∗
(–2.521)
–0.027
∗∗∗
(–2.710)
LNTA
0.679
∗∗
(2.217)
–0.118
(–0.374)
0.487
∗∗∗
(2.763)
0.363
∗∗
(2.138)
0.368
∗∗
(2.045)
0.431
∗∗
(2.303)
0.353
∗
(1.781)

0.333
∗
(1.696)
Economic Conditions
LNGDP 0.958
(0.510)
0.165
(0.105)
0.164
(0.105)
0.199
(0.127)
0.172
(0.109)
0.183
(0.116)
INFL
0.233
∗
(1.926)
0.201
∗
(1.782)
0.201
∗
(1.785)
0.200
∗
(1.769)
0.201

∗
(1.797)
0.203
∗
(1.795)
MSG
–0.126
∗∗∗
(–3.388)
–0.124
∗∗∗
(–3.478)
–0.124
∗∗∗
(–3.498)
–0.124
∗∗∗
(–3.514)
–0.124
∗∗∗
(–3.456)
–0.124
∗∗∗
(–3.469)
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Military-Madrasa-Mullah Complex 13
India Quarterly, 66, 2 (2010): 133–149
A Global Threat 13
Industry Specific
CR3

–23.341
∗
(–1.701)
–22.510
(–1.601)
–22.447
∗
(–1.607)
–22.531
∗
(–1.603)
–22.619
∗
(–1.620)
–22.603
∗
(–1.613)
MKTCAP/GDP –0.167
(–1.028)
–0.041
(–0.258)
–0.042
(–0.267)
–0.040
(–0.254)
–0.035
(–0.218)
–0.042
(–0.262)
Bank Origins

DUMAMER –0.128
(–0.280)
DUMEURO
–0.517
∗∗
(–1.960)
DUMASIA 0.034
(0.034)
DUMMENA –0.541
(–0.825)
R
2
0.840 0.856 0.786 0.798 0.798 0.799 0.795 0.798
Adjusted R
2
0.817 0.833 0.783 0.795 0.795 0.795 0.792 0.794
F–Statistics
35.776
∗∗∗
37.935
∗∗∗
367.295
∗∗∗
229.697
∗∗∗
212.032
∗∗∗
212.621
∗∗∗
208.196

∗∗∗
211.748
∗∗∗
Hausman
χ
2
test 70.01
∗∗∗
87.87
∗∗∗
–– – – ––
No. of Observations 710 710 710 710 710 710 710 710
Source: Authors’ own calculations.
Notes: (i) ROA
jt
= β
0
+ β
1
LLP/TL
jt
+ β
2
LNDEPO
jt
+ β
3
EQASS
jt

+ β
4
NIE/TA
jt
+ β
5
NII/TA
jt
+ β
6
LOANS/TA
jt
+ β
7
LNTA
jt

+ β
8
LNGDP
t
+ β
9
INFL
t
+ β
10
MSG
t

+ β
11
CR3
t
+ β
12
MKTCAP/GDP
t

+ β
13
DUMAMER + β
14
DUMEURO + β
15
DUMSIA + β
16
DUMMENA
+ ε
jt

The dependent variable is ROA, calculated as net profit divided by total assets; LLP/TL is a measure of bank credit risk, calculated as the ratio of total loan
loss provisions divided by total loans; LNDEPO is a proxy measure for network embeddedness, calculated as natural logarithm of total deposits; EQASS is
a measure of capitalization, calculated as book value of shareholders equity as a fraction of total assets; NIE/TA is a proxy measure for management quality,
calculated as personnel expenses divided by total assets; NII/TA is a measure of bank diversification towards non-interest income, calculated as total non-
interest income divided by total assets; LOANS/TA is used as a proxy measure of loans intensity, calculated as total loans divided by total assets; LNTA is a
proxy measure of size, calculated as a natural logarithm of total bank assets; LNGDP is natural log of GDP; INFL is the rate of inflation; MSG is the growth of
money supply measured by currency in circulation; CR3 is the three bank concentration ratio; MKTCAP/GDP is the ratio of stock market capitalization over
GDP; DUMAMER, DUMEURO, DUMASIA, and DUMMENA are dummy variables that take a value of 1 for foreign banks from the North America, Europe,
Asia and MENA regions respectively, 0 otherwise. (ii) Values in parentheses are t-statistics. (iii)

∗∗∗
,

∗∗
and
∗
indicate significance at 1%, 5% and 10% levels.
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Global Business Review, 13, 1 (2012): 1–23
14 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
Goddard et al. (2004a), Kosmidou (2008), Pasiouras and Kosmidou (2007) and Staikouras and Wood
(2003), providing support to the argument that well-capitalized banks face lower costs of going bank-
rupt, thus reducing their cost of funding. The earlier studies by, among others, Sufian (2009) point out
that strong capital structure provides additional strength to withstand financial crises and increased safety
for depositors during unstable macroeconomic conditions particularly for banks operating in developing
economies banking sectors.
NIE/TA has consistently exhibited positive relationship with bank performance and is statistically
significant at the 1 per cent level, supporting the expense-preference behaviour in the Indian banking
sector. There are a few plausible explanations. First, as suggested by Sathye (2001), the more highly
qualified and professional management may require higher remuneration packages, and thus a highly
significant positive relationship with profitability measure is natural. Second, as suggested by Claessens
et al. (2001), although overstaffing may lead to the deterioration of bank profitability levels in the low-
income countries, the same could not be hold true for banks operating in the middle and high-income
countries.
Likewise, NII/TA entered all the regression models with a positive sign. It is also observed from
Column 4 of Table 4 that the coefficient of NII/TA becomes statistically significant when we control for
macroeconomic and competition variables. The results imply that banks which derived a higher propor-
tion of their income from non-interest sources such as fee-based services tend to report a higher level of
profitability. The empirical findings provide support to the earlier study by, among others, Canals (1993).
To recap, Canals (1993) suggests that revenues generated from new business units contribute signifi-

cantly to bank performance.
Concerning the liquidity results, LOANS/TA is negatively related to the profitability of Indian banks,
indicating a negative relationship between bank profitability and the level of liquid assets held by the
bank. As higher figures of the ratio denote lower liquidity, the results imply that the less liquid banks tend
to exhibit higher profitability levels. The empirical finding is consistent with the earlier studies by, for
example, Ben Naceur and Omran (2011), Molyneux and Thornton (1992), Pasiouras and Kosmidou
(2007) and Staikouras et al. (2008).
The relationship between size (LNTA) and Indian banks’ profitability is positive, a fact that supports
the results of Kosmidou (2008) and Spathis et al. (2002). Hauner (2005) offers two potential explana-
tions for which size could positively influence bank performance. First, if it relates to market power,
large banks should pay less for their inputs. Second, there may be increasing returns to scale through the
allocation of fixed costs (for example, research or risk management) over a higher volume of services or
from efficiency gains from a specialized workforce. Within the context of the Indian banking sector, the
possible cost advantages associated with product and risk diversification could be the explanation for the
positive relationship between size and profitability.
The results about the impact of GDP growth on ROA is consistent with the results of Hassan and
Bashir (2003), Kosmidou (2008) and Pasiouras and Kosmidou (2007), and provides support to the argu-
ment of positive association between economic growth and banking sector performance. Similarly, infla-
tion (INFL) is positively related to Indian banks’ profitability, implying that during the period under
study, the levels of inflation have been anticipated by banks operating in the Indian banking sector. Perry
(1992) suggests that the effect of inflation on bank performance is positive if the rate of inflation is
anticipated. This gave them the opportunity to adjust the interest rates accordingly, and consequently to
earn higher profits.
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Global Business Review, 13, 1 (2012): 1–23
Determinants of Bank Performance in a Developing Economy 15
MSG is statistically significant and negatively related to Indian banks’ profitability. According to the
quantity theory of money, changes in the supply of money may lead to changes in nominal GDP and the
price level. Money supply refers to the quantity of money available and it depends on the monetary pol-
icy that is being followed. Although money supply is determined by the central bank’s policy, it is also

affected by the behaviour of households that hold money and banks in which money is held.
Turning to the concentration variable, we observe that the coefficient of CR3 is negatively related to
bank profitability. The empirical findings seem to reject the SCP hypothesis. To recap, the SCP hypothe-
sis states that banks in highly concentrated markets tend to collude, and therefore earn monopoly profits
(Gilbert, 1984; Molyneux et al., 1996; Short, 1979). Likewise, the impact of stock market capitalization
(MKTCAP) on bank profitability is negative, implying that during the period under study, the Indian
stock market offered substitution possibilities to potential borrowers. However, the findings should be
interpreted with caution since the coefficient of the variable is never statistically significant at any con-
ventional levels.
Controlling for Bank Origins
In the following analysis, we distinguish banks according to their region of origin to test for the ‘limited
form’ of the global advantage hypothesis. Accordingly, DUMAMER, DUMEURO, DUMASIA and
DUMMENA variables are introduced in regression models 6–9 respectively. The regression results are
presented in Columns 6–9 of Table 4. It can be observed from Column 6 of Table 4 that the coefficient
of the DUMAMER variable exhibits a negative sign, but is not significant at any conventional level.
From Column 7 of Table 4 it is observed that the coefficient of DUMEURO is negative and is stati-
stically significant at the 5 per cent level. The empirical findings seem to suggest that foreign banks ori-
ginating from the European countries are the least profitable. It is interesting to note that the coefficient
of the NII/TA variable is positive and statistically significant at the 10 per cent level. The result clearly
indicates that foreign banks from the European countries in India have been active in stock exchange
listing and trading, advisory services and other fee income-generating activities which contribute posi-
tively to their profitability levels.
During the period under study, the empirical findings seem to suggest that the variable DUMASIA
entered the regression model with a positive sign, implying that profitability level is positively related to
foreign banks from the Asian region. However, the empirical findings should be interpreted with caution
since the coefficient of the variable is not statistically significant at any conventional levels. It is clear
from Column 9 of Table 4 that the coefficient of DUMMENA is negative, implying that profitability is
negatively related to foreign banks from the Middle East and North Africa (MENA) region. However, it
is worth noting that the coefficient of the variable is not statistically significant at any conventional
levels.

In essence, the findings which suggest that the European banks are the least profitable banking group
compared to the domestic and other foreign bank peers lend support to the limited form of the global
advantage hypothesis. To recap, Berger et al. (2000, p. 26) suggest that ‘under the limited form of global
advantage hypothesis, only the efficient institutions in one or a limited number of nations with specific
favourable market or regulatory conditions in their home countries can operate more efficiently than
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Global Business Review, 13, 1 (2012): 1–23
16 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
domestic banks in other nations’. On the other hand, the ‘liability of unfamiliarness’ hypothesis is rejected
as the findings seem to suggest that there is no significant advantage accrued to foreign banks originating
from the Asian region.
Robustness Checks
Foreign banks from different countries and regions may react differently to the same profitability deter-
minants. Foreign banks may have quite different goals from their domestic bank peers, since they may
trade-off performance with market share to penetrate a local market (Isik and Hassan, 2003). Furthermore,
lack of exposure in a lesser known market may manifest itself in the form of extra information-gathering
costs for clients. Therefore, foreign banks may be at disadvantage in terms of input efficiency, which is
primarily driven by excess expenditures on personnel, or over-reliance on purchased funds in the inter-
bank market which is costlier. Alternatively, foreign banks might possess some distinct advantages,
stemming from their asset portfolios. Relative to their domestic bank counterparts, foreign banks asset
portfolios tend to be skewed towards investment securities, which have low administrative and trans-
actional costs compared to loans.
Moreover, Berger et al. (2000) point out that there are diseconomies in managing subsidiaries that are
located at longer distance relative to their parent bank location. The same argument applies to other
dimensions of distance, like the difference in language and legal systems across countries. To address
this concern, in the preceding analysis, we repeat equation (1) by interacting the explanatory variables
against the foreign banks’ nation of origin. The regression results are presented in Table 5. As observed
from Table 5, most of the baseline variables continued to remain robust in terms of directions and signi-
ficance levels.
The empirical findings seem to suggest that the coefficient of LLP/TL x DUMAMER is negative and

statistically significant at the 1 per cent level. This is reasonable since loan loss reserves is the cumulative
stock of loan loss reserves that changes according to the amount of new loan loss provisions added each
year. Provisions are subtracted from operating profit before taxes and extraordinary items, the numerator
of ROA. Rogers and Sinkey (1999) point out that bank management may use loan loss provision charges
to manage their credit risk, including the possibility to smooth out profits. Banks can reduce the variabil-
ity of reported income by making higher provisions than necessary when credit quality and net income
are high, during favourable economic conditions. In this vein, provisions would not have to increase as
much if credit quality was to deteriorate, or economic conditions are hard. The earlier studies by, among
others, Kosmidou (2008), Kosmidou et al. (2007) and Staikouras et al. (2008) have also found negative
relationship between problem loans and bank profitability.
From Columns 3 and 4 of Table 5, it is clear that the coefficient of NIE/TA exhibits a negative sign
when interacted with DUMEURO and becomes statistically significant when we control for macroeco-
nomic conditions. The results imply that foreign banks from the European countries with high overhead
expenses tend to report lower profitability levels. It is observed from Columns 5 and 6 of Table 5 that
network embeddedness (LNDEPO) exerts positive and significant impact on the profitability of foreign
banks from the Asian countries. If anything could be delved, the empirical findings suggest that foreign
banks from the Asian countries with higher deposit market share tend to be relatively more profitable.
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Military-Madrasa-Mullah Complex 17
India Quarterly, 66, 2 (2010): 133–149
A Global Threat 17
Table 5. Panel Fixed Effects Regression Results
(1) (2) (3) (4) (5) (6) (7) (8)
CONSTANT
2.857
∗∗∗
(26.516)
3.012
∗∗∗
(44.833)

CONSTANT
2.884
∗∗∗
(114.993)
2.792
∗∗∗
(25.574)
CONSTANT
2.901
∗∗
(1.991)
2.485
∗∗
(2.197)
CONSTANT
2.677
∗∗∗
(47.831)
2.774
∗∗∗
(28.679)
Bank
Characteristics
Bank
Characteristics
Bank
Characteristics
Bank
Characteristics
LLP/TL x

DUMAMER
–0.029
∗∗∗
(–3.391)
0.010
(0.050)
LLP/TL x
DUMEURO
0.129
∗∗∗
(3.809)
0.126
∗∗∗
(5.093)
LLP/TL x
DUMASIA
0.141
(0.067)
0.608
(0.369)
LLP/TL x
DUMMENA
0.091
(0.762)
0.124
(0.981)
LNDEPO x
DUMAMER
0.033
(0.741)

–0.062
∗∗
(–2.280)
LNDEPO x
DUMEURO
0.000
(0.108)
–0.004
(–1.016)
LNDEPO x
DUMASIA
0.116
∗∗∗
(2.662)
0.085
∗∗
(2.257)
LNDEPO x
DUMMENA
–0.113
(–0.955)
–0.119
(–0.939)
EQASS x
DUMAMER
0.075
∗∗∗
(3.300)
–0.003
(–0.902)

EQASS x
DUMEURO
–0.000
(–0.032)
–0.003
(–0.706)
EQASS x
DUMASIA
0.216
∗∗
(2.300)
0.184
∗∗
(2.239)
EQASS x
DUMMENA
0.455
∗∗∗
(8.730)
0.430
∗∗∗
(6.611)
NIE/TA x
DUMAMER
0.130
∗∗∗
(5.435)
–0.005
(–0.345)
NIE/TA x

DUMEURO
–0.018
(–1.464)
–0.023
∗
(–1.778)
NIE/TA x
DUMASIA
0.068
∗∗
(2.228)
0.103
∗∗∗
(3.270)
NIE/TA x
DUMMENA
–0.045
(–0.802)
–0.041
(–0.839)
NII/TA x
DUMAMER
–0.035
(–0.635)
0.084
∗∗∗
(4.256)
NII/TA x
DUMEURO
0.012

(1.168)
0.015
∗
(1.870)
NII/TA x
DUMASIA
–0.014
(–0.421)
–0.013
(–0.454)
NII/TA x
DUMMENA
0.009
(0.340)
0.002
(0.046)
LOANS/TA ×
DUMAMER
0.287
(1.012)
0.013
(0.355)
LOANS/TA ×
DUMEURO
0.001
(0.152)
0.005
(0.568)
LOANS/TA ×
DUMASIA

–0.039
(–0.786)
–0.029
(–0.656)
LOANS/TA ×
DUMMENA
–0.012
(–0.544)
–0.026
(–1.096)
LNTA ×
DUMAMER
–0.032
(–0.936)
0.056
∗∗
(2.025)
LNTA ×
DUMEURO
0.007
(1.032)
–0.008
(–0.763)
LNTA ×
DUMASIA
–0.176
∗∗∗
(–3.030)
–0.184
∗∗∗

(–3.356)
LNTA ×
DUMMENA
0.473
∗∗∗
(4.035)
0.445
∗∗∗
(2.880)
(Table 5 continued)
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India Quarterly, 66, 2 (2010): 133–149
18 Sanjeeb Kumar Mohanty

and Jinendra Nath Mahanty18 Michael Lindfield
(1) (2) (3) (4) (5) (6) (7) (8)
Economic
Conditions
Economic
Conditions
Economic
Conditions
Economic
Conditions
LNGDP ×
DUMAMER
–0.057
(–0.863)
LNGDP ×
DUMEURO

0.136
(1.202)
LNGDP ×
DUMASIA
0.165
∗∗
(2.328)
LNGDP ×
DUMMENA
–0.094
(–0.679)
INFL ×
DUMAMER
0.120
∗∗∗
(2.971)
INFL ×
DUMEURO
0.017
(0.392)
INFL ×
DUMASIA
0.166
∗∗∗
(2.641)
INFL ×
DUMMENA
0.156
∗∗
(1.978)

MSG ×
DUMAMER
–0.078
(–1.410)
MSG ×
DUMEURO
–0.113
∗∗∗
(–3.111)
MSG ×
DUMASIA
–0.224
∗∗∗
(–2.968)
MSG ×
DUMMENA
–0.211
∗∗
(–2.152)
R
2
0.228 0.228 R
2
0.231 0.234 R
2
0.441 0.462 R
2
0.344 0.350
Adjusted R
2

0.114 0.110 Adjusted R
2
0.117 0.116 Adjusted R
2
0.358 0.379 Adjusted R
2
0.247 0.250
Hausman
χ
2
test 177.79
∗∗∗
217.16
∗∗∗
Hausman
χ
2
test
177.79
∗∗∗
217.16
∗∗∗
Hausman
χ
2
test
177.79
∗∗∗
217.16
∗∗∗

Hausman
χ
2
test
177.79
∗∗∗
217.16
∗∗∗
F-Stat.
2.002
∗∗∗
1.928
∗∗∗
F-Stat.
2.027
∗∗∗
1.985
∗∗∗
F-Stat.
5.328
∗∗∗
5.587
∗∗∗
F-Stat.
3.539
∗∗∗
3.506
∗∗∗
No. of Obs. 710 710 No. of Obs. 710 710 No. of Obs. 710 710 No. of Obs. 710 710
Source: Authors’ own calculations.

Notes: (i) ROA
jt
= β
0
+ β
1
LLP/TL
jt
+ β
2
LNDEPO
jt
+ β
3
EQASS
jt
+ β
4
NIE/TA
jt
+ β
5
NII/TA
jt
+ β
6
LOANS/TA
jt
+ β
7

LNTA
jt
+ β
8
LNGDP
t
+ β
9
INFL
t
+ β
10
MSG
t
+ ε
jt
The dependent variable is ROA, calculated as net profit divided by total assets; LLP/TL is a measure of bank credit risk, calculated as the ratio of total loan loss
provisions divided by total loans; LNDEPO is a proxy measure for network embeddedness, calculated as natural logarithm of total deposits; EQASS is a measure of
capitalization, calculated as book value of shareholders equity as a fraction of total assets; NIE/TA is a proxy measure for management quality, calculated as personnel
expenses divided by total assets; NII/TA is a measure of bank diversification towards non-interest income, calculated as total non-interest income divided by total
assets; LOANS/TA is used as a proxy measure of loans intensity, calculated as total loans divided by total assets; LNTA is a proxy measure of size, calculated as
a natural logarithm of total bank assets; LNGDP is natural log of GDP; INFL is the rate of inflation; MSG is the growth of money supply measured by currency in
circulation. (ii) Values in parentheses are t-statistics. (iii)
∗∗∗
,
∗∗
and
∗
indicate significance at 1%, 5% and 10% levels.
(Table 5 continued)

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Global Business Review, 13, 1 (2012): 1–23
Determinants of Bank Performance in a Developing Economy 19
It is also interesting to note from Columns 5 and 6 of Table 5 that the coefficient of LNTA × DUMASIA
is negative. The empirical findings clearly indicate that the impact of size is negative on the profitability
of foreign banks from the Asian countries.
Additional Robustness Checks
In order to check for the robustness of the results, we perform a number of sensitivity analyses. First,
we restrict our sample to foreign banks with a wide presence in the Indian banking sector. All in all, the
results remain qualitatively similar in terms of directions and significance levels. Second, we address the
effects of outliers in the sample by removing the top and bottom 1 per cent of the sample. The results
remain robust in terms of directions and significance levels. Third, we replace ROA with ROE and repeat
equation (1). In general, the results confirm the baseline regression results. Finally, we drop the NII/TA
variable from the regression models. When we exclude the NII/TA variable, we find that the EQASS
variable has statistically significant impact on the profitability of banks operating in the Indian banking
sector. On the other hand, the coefficient of the other variables remained robust in terms of directions and
significance levels. To conserve space, we do not report the full regression results in the article, but these
are available upon request.
Concluding Remarks and Directions for Future Research
The present article examines the internal and external factors that influence the profitability of banks in
a developing economy. Specifically working within the Indian banking sector, the analysis is confined to
the universe of domestic and foreign commercial banks, which have been operating in the Indian bank-
ing sector during the period 2000–08.
The empirical findings from this study suggest that all the explanatory variables have statistically sig-
nificant impact on the profitability of Indian banks. However, the impact is not uniform across banks of
different nations of origin. During the period under study, we find that liquidity and network embedded-
ness exert negative impacts on bank profitability, but the impact is positive on foreign banks from the
Asian countries. On the other hand, the results seem to suggest that size has positive impact on bank
profitability, but exerts negative impact on the profitability of foreign banks from other Asian countries.
The results indicate a positive relationship between credit risk and bank profitability. However, the

impact is negative on foreign banks from the North America. In general, the impact of overhead expenses
is positive on bank profitability, but is negative on the foreign banks from the European countries.
As for the impact of macroeconomic indicators, the empirical findings seem to suggest that inflation
exerts positive impact on bank profitability levels, while growth in money supply has negative impact.
The empirical findings also suggest that the impact of GDP growth is positive, but is only statistically
significant on foreign banks from the Asian countries. The empirical findings indicate that the level of
concentration in the banking sector has negative relationship with bank profitability.
The empirical findings do not lend support for the ‘limited form’ of global advantage hypothesis.
During the period under study, the empirical findings seem to suggest that foreign banks from the
European countries have been relatively less profitable compared to domestic and foreign banks from
by guest on February 22, 2014gbr.sagepub.comDownloaded from
Global Business Review, 13, 1 (2012): 1–23
20 Fadzlan Sufi an and Mohamad Akbar Noor Mohamad Noor
other nations. Likewise, the liability of unfamiliarness hypothesis is also rejected, since we do not find
any advantage accruing to foreign banks from other Asian countries.
Future research could include more variables such as taxation and regulation indicators, exchange
rates as well as indicators of the quality of the offered services. Another possible extension could be the
examination of differences in the determinants of profitability between small and large or high and low
profitability banks. In terms of methodology, a statistical cost accounting and/or frontier optimization
techniques such as the non-parametric Data Envelopment Analysis (DEA), the Stochastic Frontier
Analysis (SFA) and/or the Malmquist Productivity Index (MPI) approach is recommended to examine
the impact of origins on performance of the foreign banks operating in the Indian banking sector.
Acknowledgements
We would like to thank the editor and the anonymous referee for their constructive comments and suggestions,
which have signifi cantly improved the contents of the article. The remaining errors are our own responsibility. The
analyses, opinions and fi ndings in this article represent the views of the authors; they are not necessarily those of
the institutions.
Note
1. The SAARC comprises Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka.
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