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

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Global Business Review

http://gbr.sagepub.com/content/13/1/1The online version of this article can be found at:

DOI: 10.1177/097215091101300101

2012 13: 1

Global Business Review

Fadzlan Sufian and Mohamad Akbar Noor Mohamad NoorMatters?

Determinants of Bank Performance in a Developing Economy: Does Bank Origins

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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

behav-The financial sector is the backbone of the Indian economy and plays an important financial mediary role Therefore, its health is critical to the health of the economy at large Given the relation

inter-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: fadzlans@iium.edu.my; fadzlan.sufi an@gmail.com

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: akbar.mohamad@sapura.com.my

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

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.1The 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 dence on the impact of origin on the performance of the foreign-owned banks in India

evi-Global Business Review, 13, 1 (2012): 1–23

This article is structured as follows The next section reviews the related studies in the literature, 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

fol-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 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

profit-to capture the impact of bank ownership by introducing dummy variables inprofit-to 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 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

coun-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

Global Business Review, 13, 1 (2012): 1–23

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

Global Business Review, 13, 1 (2012): 1–23

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 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

embed-The EQASS variable is included in the regression models to examine the relationship between 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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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 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

man-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 cation leads to higher risks, the variable may exhibit negative effects

diversifi-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 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

profitabil-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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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 puted as the ratio of stock market capitalization as a fraction of the national GDP The variable is entered

com-in the regression model to examcom-ine the impact of competition from the stock market

Table 1 lists the variables used to proxy profitability and its determinants We also include the tion and the expected impact of the determinants according to the literature

nota-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 + β2 ln (LNDEPO) it + β 3 ln (EQASS) it + β 4 ln (NIE/TA) it

+ β5 ln (NII/TA) it + β6 ln (LOANS/TA) it + β7 ln (LNTA) it

+ ζ1 ln (GDP) t + ζ 2 ln (INFL) t + ζ3 ln (MSG) t

+ δ 1 ln (CR3) t + δ2 ln (MKTCAP/GDP) t

Loan loss provisions/total loans An indicator of credit risk, which shows how much a bank is provisioning in year

Calculated as non-interest expense/total assets and provides information on the efficiency of the management regarding expenses relative to the assets in year

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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 isthe idiosyncratic error and is independently identically

dis-tributed (i.i.d), e it ∼ N(0, σ2)

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 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

vari-F-statistics for all models is significant at the 1 per cent level Third, the adjusted R2 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 ally significant at the 10 per cent level or better The result is in consonance with Berger and DeYoung’s

statistic-(1997) skimping hypothesis To recap, Berger and DeYoung statistic-(1997) suggest that under the skimping

hypothesis, a bank maximizing long-run profits may rationally choose to have lower costs in the run by skimping on the resources devoted to underwriting and monitoring loans, but bear the conse-quences of greater loan performance problems

short-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),