determinants of bank profitability in developing economies: empirical evidence from the south asian...
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Determinants of bank profitabilityin developing economies: empiricalevidence from the South Asian bankingsectorsFadzlan Sufian aa IIUM Institute of Islamic Banking and Finance, InternationalIslamic University Malaysia , 205A Jalan Damansara, DamansaraHeights, 50480, Kuala Lumpur , MalaysiaPublished online: 16 Aug 2012.
To cite this article: Fadzlan Sufian (2012) Determinants of bank profitability in developingeconomies: empirical evidence from the South Asian banking sectors, Contemporary South Asia,20:3, 375-399, DOI: 10.1080/09584935.2012.696089
To link to this article: http://dx.doi.org/10.1080/09584935.2012.696089
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Determinants of bank profitability in developing economies: empirical
evidence from the South Asian banking sectors
Fadzlan Sufian*
IIUM Institute of Islamic Banking and Finance, International Islamic University Malaysia,205A Jalan Damansara, Damansara Heights, 50480 Kuala Lumpur, Malaysia
This study seeks to examine the performance of 77 Bangladeshi, Sri Lankan, andPakistani commercial banks between 1997 and 2008. The empirical findingssuggest that bank specific characteristics – in particular, liquidity, non-interestincome, credit risk, and capitalization – have positive and significant impacts onbank performance, while cost is negatively related to bank profitability. As for theimpact of macroeconomic indicators, the results suggest that economic growthhas positive and significant impact, while inflation has no significant impact onbank profitability. During the period under study, the empirical findings indicatethat private investment is positively related to bank profitability, while privateconsumption expenditure exhibits negative impact. However, the impact is notuniform across the countries studied.
Keywords: bank profitability; panel regression analysis; South Asian bankingsectors
1. Introduction
Financial markets’ deregulation throughout the world has substantially transformedthe economic behavior of many developing countries. New economic policiespromoting free movement of capital play a major role in almost every country in theworld. The Indian sub-continent is no exception and has been changingsubstantially. Although the South Asian countries began the deregulation processlater than many other countries, significant deregulatory measures have already beentaken in many aspects of the financial markets.
The banking sector is the backbone of the South Asian countries’ financialsystem and plays an important financial intermediary role (Edirisuriya 2004; Perera,Skully, and Wickramanayake 2006). The dominance of banks in the South Asiancountries can be explained by various factors, such as the underdevelopment of thecapital markets, among others. Although the banking sector is the single mostimportant financial intermediary in South Asian countries, the banking intermedia-tion capacity has been relatively weak. From Table 1, it can be observed that thebank credit to GDP ratios in most South Asian countries are significantly lower thanin other developing countries in Asia, i.e. China (126.18%), Malaysia (115.2%), andThailand (130.6%), and pales in comparison with the 200% or better levels typical in
*Email: [email protected]
Contemporary South Asia
Vol. 20, No. 3, September 2012, 375–399
ISSN 0958-4935 print/ISSN 1469-364X online
� 2012 Taylor & Francis
http://dx.doi.org/10.1080/09584935.2012.696089
http://www.tandfonline.com
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Table
1.
Domesticcreditprovided
bythebankingsector.
Country
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
Bangladesh
29.94
30.28
31.78
34.18
47.78
50.44
49.65
51.86
54.91
58.13
58.28
59.38
Bhutan
15.59
2.75
71.38
3.02
6.66
11.65
12.05
18.53
15.70
13.90
14.39
7India
46.13
46.55
49.24
53.02
54.65
58.86
57.44
59.28
60.10
63.32
64.20
71.59
Nepal
34.16
38.32
38.68
40.77
45.82
41.39
41.38
41.96
42.81
43.71
49.19
52.72
Pakistan
52.12
51.45
49.13
41.60
37.74
36.65
37.06
41.05
43.90
42.87
45.92
7SriLanka
36.78
36.62
39.16
43.75
45.43
42.24
40.72
43.30
43.63
47.13
45.02
42.84
China
100.72
113.11
119.33
119.67
123.00
143.46
151.88
140.37
135.56
136.25
132.01
126.18
Malaysia
221.81
216.56
197.25
179.23
189.17
184.78
180.86
149.51
136.22
119.18
113.43
115.22
Thailand
177.58
176.75
155.78
138.27
128.57
127.78
130.75
124.53
119.18
108.88
131.51
130.57
Japan
276.68
298.44
309.92
308.91
299.47
299.19
307.27
303.21
312.78
304.93
294.42
292.99
United
Kingdom
122.35
119.74
121.77
130.12
135.74
140.76
144.87
153.54
162.11
172.45
188.22
212.34
United
States
187.19
198.59
210.97
201.13
208.57
200.51
215.45
221.29
225.17
232.47
240.75
223.71
Note:Source:
WorldBankWorldDevelopmentIndicators
(WDI).
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the developed market economies, i.e. Japan (293.0%), United Kingdom (212.3%),and United States (223.7%).
Despite the low bank credit to GDP ratios, it can be observed from Table 1 thatmost South Asian countries have shown remarkable progress during the past decadeor so. The proliferation of the role that the banking sector plays as financialintermediary in South Asian countries raises important questions about theirprofitability. Barajas et al. (2000) among others, find that high profitability couldstrengthen bank capitalization levels and help create an additional buffer againstnegative macroeconomic shocks. Furthermore, Rajan and Zingales (1998), Levine(1998), and Levine and Zervos (1998) among others, suggest that the well-being ofthe banking sector is positively related to economic growth. Therefore, knowledge ofbanking sector performance is of interest to bank managements, central banks, andpolicymakers alike.
The present paper employs the theoretical framework of the dealership modelfirst proposed by Ho and Saunders (1981) and later developed by, among others,McShane and Sharpe (1985), Allen (1988), and Angbazo (1997) to examine theperformance of the banking sectors of Bangladesh, Pakistan, and Sri Lanka over theperiod of 1997 to 2008. The paper seeks to contribute to the present literature andexamine a wide array of bank specific, macroeconomic, and financial market factors.Although there has been an extensive literature examining the profitability offinancial sectors in developed and western countries, empirical works on factors thatinfluence the performance of banks in developing economies are relatively scarce.
The empirical findings from this study seem to suggest that liquidity, non-interestincome, credit risk, and capitalization exert positive and significant impact on bankperformance, while cost is negatively related to bank profitability. As for the impactof macroeconomic indicators, the results suggest that economic growth has positiveand significant impact on bank profitability, while inflation has no significantimpact. During the period under study, the empirical findings indicate that privateinvestment is positively related to bank profitability, while private consumptionexpenditure exhibits negative impact. However, the impact is not uniform across thecountries studied.
This paper is structured as follows: the next section gives a brief overview of thebanking sectors of the South Asian countries. Section 3 reviews related studies in theliterature, followed by a section that outlines the econometric framework. Section 5reports the empirical findings. Finally, section 6 concludes and offers avenues forfuture research.
2. Brief overview of the banking sectors in South Asian countries
The banking sectors of all the three countries covered in this study perform animportant financial intermediary role and are major lenders to both private andgovernment sectors. In Bangladesh, the banking sector is comprised of fourcategories of scheduled banks. These are the state-owned commercial banks, state-owned development financial institutions, private commercial banks, and foreigncommercial banks. As at end-2008, there were a total of 48 banks operating in theBangladeshi banking sector, consisting of four state-owned commercial banks, fivestate-owned development financial institutions, 30 private commercial banks, andnine foreign commercial banks with a total of 6886 branches throughout thecountry. As at end-2008, the private commercial banks controlled a large chunk of
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the banking sector’s total assets (approximately 5%), followed by the state-ownedcommercial banks and foreign commercial banks with approximately 31% and 8%,respectively. The state-owned development financial institutions’ share of thebanking sector’s total assets remains relatively small at about 7% as at end-2008.
The Pakistani financial sector consists of commercial banks, developmentfinancial institutions, microfinance banks, non-bank financial institutions (e.g.leasing companies, housing finance companies, insurance companies, venture capitalcompanies, mutual funds, etc.), modarabas (non-bank Islamic financial institutions),and the stock exchange. As in most developing countries, the banking sectordominates the financial system and accounts for approximately two-thirds of thefinancial system’s total assets. As at end-2008, there were four public sectorcommercial banks, 25 local private banks, seven foreign banks, and four specializedbanks operating in the country’s banking sector with a total of 8774 branches. Thefour largest commercial banks accounted for approximately 44% of the bankingsector’s total assets indicating a moderate level of concentration within the Pakistanibanking sector.
The Sri Lankan financial system is made up of banks, non-banking financialinstitutions (such as finance and leasing companies), specialized financial institutions(such as primary dealers in government securities and unit trusts), and contractualsavings institutions (such as the employees’ provident fund and insurancecompanies). As at end-2008, 23 licensed commercial banks (inclusive of 12 foreignbanks) and 14 licensed specialized banks with more than 5600 branches nationwide,operate in the Sri Lankan banking sector. Despite that, the Sri Lankan bankingsector is highly concentrated, with the six largest licensed commercial banks –namely Bank of Ceylon, People’s Bank, Commercial Bank of Ceylon, HattonNational Bank, Seylan Bank, and Sampath Bank – accounting for approximately65% of the banking sector’s total assets. In addition, the largest licensed specializedbank, The National Savings Bank, accounts for an additional 10.5% of the bankingsector’s total assets.
3. Related studies
The performance of the banking sector is a subject that has received a lot of attentionin recent years. In general, empirical studies have mainly followed two alternativeapproaches, namely the dealership and/or the firm theoretic approach. On the onehand, the dealership approach first proposed by Ho and Saunders (1981) and furtherextended by McShane and Sharpe (1985), Allen (1988), and Angbazo (1996) viewsbanks as dynamic dealers, setting interest rates on loans and deposits to balance theasymmetric arrival of loan demands and deposit supplies. On the other hand, thefirm theoretical approach originally developed by Klein (1971) and Monti (1972)views banking firms in a static setting where demand and supply of deposits andloans simultaneously clear both markets (see among others Zarruck 1989; Wong1997).
Although the dealership approach acknowledges the effect of markets and insti-tutions, these factors could not be directly incorporated into the model. To addressthis concern, the more recent studies have also examined the influence of otherinternal (bank specific) and external (macroeconomic and market specific) factors onbank profitability. Furthermore, the dealership approach assumes that regardless oftheir ownership, banks apply similar business strategies and are exposed to a similar
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set of profitability determinants. However, the assumption appears to beinappropriate, particularly for developing countries, which have continuouslyembraced reforms and liberalization of the financial sector. To overcome theshortcomings, some studies augment the empirical specification of the dealershipapproach to capture the impact of bank ownership by introducing dummy variablesinto the estimation models (Micco, Panizza, and Yanez 2007).
The literature which examined the role played by management of resources indetermining bank performance is vast. It is generally agreed that better qualitymanagement of resources is the main factor contributing to bank performance, asevidenced by numerous studies focusing on the US banking system (DeYoung andRice 20040; Bhuyan and Williams 2006; Stiroh and Rumble 2006; Hirtle and Stiroh2007) and the banking systems in the western and developed countries (Molyneuxand Forbes 1995; To and Tripe 2002; Williams 2003; Kosmidou, Pasiouras, andTsaklanganos 2007; Pasiouras and Kosmidou 2007; Athanasoglou, Brissimis, andDelis 2008; Kosmidou and Zopounidis 2008; Albertazzi and Gambacorta 2009).
By contrast, fewer studies have looked at bank performance in developingeconomies. Guru, Staunton, and Balashanmugam (2002) investigated the determi-nants of bank profitability in Malaysia. They used a sample of 17 commercial banksduring the period 1986 to 1995. They divided the profitability determinants in twomain categories, namely internal determinants (liquidity, capital adequacy, andexpenses management) and external determinants (ownership, firm size, andeconomic conditions). The findings indicate that efficient expenses managementhas been significant in explaining high bank profitability. Among the macroindicators, high interest ratio is associated with low bank profitability, while inflationseems to exert positive impact on bank performance.
Chantapong (2005) investigated the performance of domestic and foreign banksin Thailand from 1995 to 2000. All banks were found to have reduced their creditexposure during the crisis years and have gradually improved their profitabilityduring the post-crisis years. The results indicate that the foreign banks haveexhibited higher profitability levels compared to the average domestic banks. Thefindings also indicate that the gap between foreign and domestic banks’ profitabilityhas closed during the post-crisis period, implying that the financial restructuringprogram has yielded some positive results.
In a study on the Chinese banking sector, Fu and Heffernan (2010) examined theperformance of different types of banks operating in the Chinese banking sectorduring the period between 1999 and 2006. The results suggest that economic valuewas added and net interest margins did better than the more conventional measuresof bank profitability, namely return on assets (ROA) and return on equity (ROE).Some macroeconomic variables and financial ratios are significant with the expectedsigns. Though the type of bank is influential, bank size is not. Neither the percentageof foreign ownership nor bank listings has discernable effects.
Ben Naceur and Goaied (2008) examined the impact of bank characteristics,financial structure, and macroeconomic conditions on Tunisian banks’ net-interestmargin and profitability during the period from 1980 to 2000. They suggest thatbanks with high amount of capital and overhead expenses tend to exhibit higher net-interest margin and profitability levels, while size is negatively related to bankprofitability. During the period under study, they find that stock marketdevelopment has positive impact on bank profitability. The empirical findingssuggest that private banks are relatively more profitable than their state-owned
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counterparts. The results suggest that macroeconomic conditions have no significantimpact on Tunisian banks’ profitability.
The study by Molyneux and Thornton (1992) is the first to explore thoroughlythe determinants of bank profitability on a set of countries. They used a sample of 18European countries during the period from 1986 to 1989. They found a significantpositive relationship between return on equity (ROE) and the level of interest rates ineach country, bank concentration, and government ownership. In anothercomprehensive study, Demirguc Kunt and Huizinga (2001) examined thedeterminants of bank interest margins and profitability in 80 countries during1988 to 1995. They found that a larger ratio of bank assets to GDP and a lowermarket concentration ratio led to lower margins and profits. The findings suggestthat foreign banks have higher margins and profits compared to domestic banks indeveloping countries, while the opposite prevails in developed countries.
More recently, Pasiouras and Kosmidou (2007) examined the performance ofdomestic and foreign commercial banks in 15 EU countries during 1995 to 2001.They found that the profitability of both domestic and foreign banks is affected notonly by a bank’s specific characteristics, but also by financial market structure andmacroeconomic conditions. The results suggest that all variables have significantrelationship with bank profitability, although the impacts and relations are notalways uniform for domestic and foreign banks.
4. Data and methodology
We collected our bank specific variables from the financial statements of a sample ofcommercial banks operating in three South Asian Association for RegionalCooperation (SAARC)1 member countries, namely Bangladesh, Pakistan, and SriLanka, over the period from 1997 to 2008 available in the Bankscope database ofBureau van Dijk’s company2. The macroeconomic variables were retrieved fromIMF Financial Statistics (IFS) and the World Bank World Development Indicator(WDI) databases.
Table 2 shows the geographical spread of the sample banks across the threeSouth Asian countries, composition, and sample coverage. The sample comprised anunbalanced panel of 34 Bangladeshi, 31 Pakistani, and 12 Sri Lankan banks yielding606 bank year observations. In each of the three banking markets, the sample has areasonable coverage with respect to total assets, loans, and deposits. The depositshare of the sample banks reaches 96% in Sri Lanka, while the minimum is that of
Table 2. Country domicile of sample banks and coverage.
Bangladesh Pakistan Sri Lanka
Number of banks 34 31 12Share of assets (%)a 72 61 92Share of assets (%)b 82 70 95Share of assets (%)c 94 88 96
Note: This table shows the number of banks, their composition, and sample coverage in Bangladesh,Pakistan, and Sri Lanka. The data are sourced from Fitch Ratings and Bureau van Dijk (2006),Bangladesh Bank, State Bank of Pakistan, and Central Bank of Sri Lanka. aRatio of total assets of samplebanks to total assets of the banking sector. bRatio of total loans of sample banks to total loans of thebanking sector. cRatio of total deposits of sample banks to total deposits of the banking sector.
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Pakistan, with 88%. When asset and loan shares of the sample are considered, again,Sri Lanka has the largest coverage with 92% and 95%, respectively. Again, thePakistani sample has the lowest coverage in terms of both asset and deposit sharewith 61% and 70%, respectively.
Following Abbasoglu, Aysan, and Gunes (2007), Ben Naceur and Goaied (2008),and Kosmidou (2008) among others, the dependent variable used in this study isROA. Return on assets and ROE have been used in most bank performance studies.Return on assets shows the profit earned per dollar of assets and most importantly,reflects the ability of bank managements to utilize financial and real investmentresources to generate profits (Hassan and Bashir 2003). For any bank, ROA dependson the bank’s policy decisions as well as uncontrollable factors relating to theeconomy and government regulations. Many regulators believe ROA is the bestmeasure of bank profitability (Hassan and Bashir 2003). Furthermore, Rivard andThomas (1997) suggest that bank profitability is best measured by ROA since ROAis not distorted by high equity multipliers and represents a better measure of firms’ability to generate returns on its portfolio of assets. Return on equity on the otherhand, reflects how effective bank management utilizes its shareholders’ funds. Abank’s ROE is affected by its ROA and its financial leverage (Hassan and Bashir2003). Since ROA tends to be lower for financial intermediaries, most banks utilizefinancial leverage heavily to increase ROE to competitive levels (Hassan and Bashir2003).
4.1. Bank specific determinants
The independent variables used to explain bank profitability are grouped undertwo main characteristics. The first represents bank specific attributes, while thesecond encompasses economic conditions during the period examined. The bankspecific variables included in the regressions are, LOANS/TA (total loans dividedby total assets), LNTA (log of total assets), LLP/TL (loans loss provisionsdivided by total loans), NII/TA (non-interest income divided by total assets),NIE/TA (non-interest expenses divided by total assets), EQASS (book value ofstockholders’ equity as a fraction of total assets), and DEPO/TA (total depositsdivided by total assets).
4.1.1. Loans intensity
Total loans divided by total assets as a proxy of loans intensity is expected toaffect bank profitability positively. However, the loan–performance relationshipdepends significantly on the expected change of the economy. During a strongeconomy, only a small percentage of loans will default and bank profitability willincrease. On the other hand, banks could adversely be affected during a weakeconomy, because borrowers are likely to default on their loans. Ideally, banksshould capitalize on favorable economic conditions and insulate themselvesduring adverse situations.
H0: The relationship between loans intensity and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between loans intensity and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables.
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4.1.2. Size
The LNTA variable is included in the regression models as a proxy of size to capturepossible cost advantages associated with size (economies of scale). In the literature,mixed relationships are observed between size and profitability, while some studiessuggest a U-shaped relationship. Log of total assets is also used to control costdifferences relating to bank size and the ability of large banks to diversify. In essence,LNTA may lead to positive effect on bank profitability if economies of scale areobserved. On the other hand, if increased diversification leads to higher risks, thevariable may exhibit negative effects.
H0: The relationship between size and bank profitability is positive after controllingother bank specific traits and macroeconomic variables;
H1: The relationship between size and bank profitability is negative after controllingother bank specific traits and macroeconomic variables.
4.1.3. Credit risk3
The ratio of loan loss provisions to total loans (LLP/TL) is incorporated as anindependent variable in the regression analysis as a proxy of credit risk. Thecoefficient of LLP/TL is expected to be negative because bad loans reduce bankprofitability. In this direction, Miller and Noulas (1997) suggest that the greater thefinancial institution’s exposure towards high risk loans, the higher would be theaccumulation of unpaid loans resulting in a lower profitability.
H0: The relationship between credit risk and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between credit risk and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables.
4.1.4. Diversification
To recognize that financial institutions in recent years have been generating incomefrom ‘off-balance sheet’ business and fee income, the non-interest income over totalassets (NII/TA) ratio is entered in the regression analysis as a proxy measure of bankdiversification into non-traditional activities. Non-interest income consists ofcommissions, service charges, and fees, guarantee fees, net profit from sale ofinvestment securities, and foreign exchange profit. The variable is expected to exhibitpositive relationship with bank profitability.
H0: The relationship between diversification and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between diversification and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables.
4.1.5. Operating expenses
The ratio of non-interest expenses to total assets, NIE/TA is used to provideinformation on the variation of bank operating costs. The variable represents thetotal amount of wages and salaries, as well as costs of running branch office facilities.
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The relationship between the NIE/TA variable and profitability levels may benegative, because the more productive and efficient banks should keep theiroperating costs low. Furthermore, the usage of new electronic technology, likeATMs and other automated means of delivering services, may have caused expenseson wages to fall (as capital is substituted for labor).
H0: The relationship between operating expenses and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between operating expenses and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables.
4.1.6. Capitalization
The EQASS variable is included in the regression models to examine the relationshipbetween profitability and bank capitalization. Strong capital structure is essential forbanks in developing economies, since it provides additional strength to withstandfinancial crises and increased safety for depositors during unstable macroeconomicconditions. Furthermore, lower capital ratios in banking imply higher leverage andrisk, and therefore greater borrowing costs. Thus, the relatively better capitalizedbanks should exhibit higher profitability levels.
H0: The relationship between capitalization and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between capitalization and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables.
4.1.7. Network embeddedness
Total deposits divided by total assets is included in the regression model as a proxyvariable for network embeddedness. It would be reasonable to assume that bankswith large branch networks are able to attract more deposits, which is a cheapersource of funds (Chu and Lim 1998). The earlier studies by among others,Randhawa and Lim (2005) point out that large banks may attract more deposits andloan transactions and in the process command larger interest rate spreads, while thesmaller banking groups with smaller depositor base might have to resort topurchasing funds in the inter-bank market, which is costlier.
H0: The relationship between network embeddedness and bank profitability is positiveafter controlling other bank specific traits and macroeconomic variables;
H1: The relationship between embeddedness and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables.
4.1.8. Ownership
To examine whether foreign ownership exerts significant influence in determining theperformance of banks operating in South Asian banking sectors, following Micco,Panizza, and Yanez (2007) among others, a dummy variable DUMFORB isintroduced in the regression model. In this vein, Micco, Panizza, and Yanez (2007)point out that the state-owned commercial banks tend to be less profitable and
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relatively inefficient compared to their private and foreign-owned bank counterpartsthroughout the South Asian region. Therefore, we expect to find positiverelationship between foreign ownership and bank profitability under the nullhypothesis.
H0: The relationship between foreign ownership and bank profitability is positive aftercontrolling other bank specific traits and macroeconomic variables;
H1: The relationship between foreign ownership and bank profitability is negative aftercontrolling other bank specific traits and macroeconomic variables.
4.2. Macroeconomic and financial market determinants
To measure the relationship between economic and market conditions andbank profitability, LNGDP (natural log of GDP) and INFL (the rate of inflation)are used. We do not have priori expectations on both variables. Favorableeconomic conditions may have positive effects on both demand and supply ofbanking services, but will have either positive or negative influence on bankprofitability. Staikouras and Wood (2003) point out that inflation may have directeffects, i.e. increase in the price of labor, and indirect effects, i.e. changes ininterest rates and asset prices on bank profitability. Perry (1992) suggests that theeffect of inflation on bank performance depends on whether inflation isanticipated or unanticipated. In the anticipated case, interest rates are adjustedaccordingly, resulting in revenues increasing faster than costs, subsequentlyimpacting positively on bank profitability. On the other hand, in theunanticipated case, banks may be slow to adjust their interest rates resulting ina faster increase of bank costs compared to bank revenues, consequentlynegatively affecting on bank profitability.
We also control the impact of financial development on the performance ofbanks operating in South Asian banking sectors. We employ two proxies for thelevel of financial development. The first represents market-based indicator, whilethe second refers to bank-based indicator. As for the first proxy, following amongothers, Ben Naceur and Omran (2010) we use the ratio of stock marketcapitalization over GDP (MKTCAP/GDP) as a measure of the equity marketsize. As for the bank-based indicator, we use the size of private sector investments(LNPRIV) to measure the importance of bank financing to the economy. TheMKTCAP/GDP and LNPRIV may also indicate the complementarity orsubstitutability between bank and equity market financing (Ben Naceur andOmran 2010). In addition, we also include the size of private consumptionexpenditures (LNPCE) to capture the impact of private sector spending on theperformance of banks in South Asia. The variable may also indicate theimportance of bank deposits as a source of funding among banks operating inSouth Asian banking sectors4.
Table 3 contains the variables used to proxy profitability and itsdeterminants. We also include the notations and expected impacts according tothe literature.
Panels A, B, and C of Table 4 present the summary statistics of the dependentand explanatory variables for the Bangladeshi, Pakistani, and Sri Lankan banks,respectively.
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Table 3. Descriptive of the variables used in the regression models.
Variable Description
Hypothesizedrelationship
with profitability
DependentROA The return on assets of the bank in year t. NAIndependentInternal factorsLOANS/TA A measure of liquidity, calculated as total loans/
total assets. The ratio indicates what percentageof the assets of the bank is tied up in loans inyear t.
þ
LNTA The natural logarithm of the accounting value ofthe total assets of the bank in year t.
þ
LLP/TL Loan loss provisions/total loans. An indicator ofcredit risk, which shows how much a bank isprovisioning in year t relative to its total loans.
7
NII/TA A measure of diversification and business mix,calculated as non-interest income/total assets.
þ
NIE/TA Calculated as non-interest expense/total assetsand provides information on the efficiency ofthe management regarding expenses relative tothe assets in year t. Higher ratios imply a lessefficient management.
7
EQASS A measure of bank’s capital strength in year t,calculated as equity/total assets. High capitalasset ratio is assumed to be indicator of lowleverage and therefore lower risk.
þ
DEPO/TA Is used a proxy measure of networkembeddedness, calculated as total depositsdivided by total assets of bank j in year t.
þ
External factorsLNGDP Natural logarithm of gross domestic products. þ/7INFL The rate of inflation. þ/7LNPRIV Natural logarithm of private investment. þLNPCE Natural logarithm of private consumption
expenditure.7
MKTCAP/GDP The ratio of stock market capitalization. Thevariable serves as a proxy of financialdevelopment.
7
Country specificDUMBANG DUMBANG is a dummy variable that takes a
value of 1 for banks operating in Bangladesh, 0otherwise.
þ/
DUMSRI DUMSRI is a dummy variable that takes a valueof 1 for banks operating in Sri Lanka, 0otherwise.
þ/
DUMPAKI DUMPAKI is a dummy variable that takes avalue of 1 for banks operating in Sri Lanka, 0otherwise.
þ/
OwnershipDUMFORB DUMFORB is a dummy variable that takes a
value of 1 if a bank is a foreign owned bank, 0otherwise.
þ
Note: The data for the calculation of banks’ specific variables were obtained from Bankscope database.The macroeconomic data were obtained from International Monetary Fund (IMF 2002) InternationalFinancial Statistics (IFS) and the World Bank World Development Indicators (WDI) databases.
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Table
4.
Summary
statistic
ofdependentandexplanatory
variables.
ROA
LOANS/TA
LNTA
LLP/TL
NII/TA
NIE
/TA
EQASS
DEPO/TA
REGCAR
LNGDP
INFL
LNPRIV
LNPCE
MKTCAP/G
DP
Panel
A:Bangladesh
Mean
0.760
61.497
10.097
5.691
2.475
3.124
5.950
0.822
10.958
7.780
5.651
6.585
7.765
4.580
Min
714.840
0.610
5.888
0.000
0.030
0.350
717.080
0.007
730.400
7.469
1.931
5.925
7.273
1.900
Max
3.410
94.840
13.108
90.540
6.940
19.440
68.870
1.026
110.600
8.076
9.900
7.186
8.309
9.900
Std.Dev.
1.887
13.435
1.194
7.713
1.251
1.823
8.865
0.135
9.433
0.183
2.437
0.367
0.304
2.559
Panel
B:SriLanka
Mean
0.419
61.940
5.111
4.625
2.063
4.604
8.556
16.637
14.319
14.465
10.863
13.080
14.097
15.147
Min
738.650
39.670
0.519
0.090
0.430
1.470
716.860
0.188
74.500
13.700
4.692
12.345
13.300
6.600
Max
3.720
85.550
11.123
15.510
3.870
17.250
42.790
702.781
79.570
15.300
22.563
13.994
14.938
27.500
Std.Dev.
4.013
8.986
2.293
3.070
0.619
1.914
7.018
99.387
8.912
0.484
4.868
0.525
0.485
6.683
Panel
C:Pakistan
Mean
0.590
47.570
10.744
7.658
1.473
3.813
8.564
0.916
16.363
8.512
6.872
6.726
8.226
22.776
Min
710.450
1.570
7.275
0.190
70.110
70.480
77.100
0.443
0.651
7.795
3.100
5.984
7.506
6.800
Max
5.520
81.860
13.617
40.320
6.440
17.300
53.940
9.715
65.400
9.238
11.980
7.647
8.972
49.200
Std.Dev.
2.036
14.293
1.411
7.554
0.959
2.428
6.552
0.748
11.435
0.450
2.966
0.567
0.458
13.842
Note:Thetable
presents
thesummary
statisticsofthevariablesusedin
theregressionanalysis.
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4.3. Econometric specification
To test the relationship between bank profitability and the bank specific andmacroeconomic determinants described earlier, we estimate a linear regression modelas follows:
yit ¼ b0it þ bditXdit þ bmjtXmjt þ ejt ð1Þ
where i refers to an individual bank; t refers to year; j refers to the country in whichbank i operates; yjt refers to the return on assets (ROA) and is the observation ofbank j in year t; Xd represents the internal factors (determinants) of a bank; Xm
represents the external factors (determinants) of a bank; ejt is a normally distributeddisturbance term.
Extending Equation (1) to reflect the variables as described in Table 3, thebaseline regression model is formulated as follows:
yit ¼ b0 þ b1LOANS=TAjt þ b2LNTAjt þ b3LLP=TLjt þ b4NII=TAjt
þ b5NIE=TAjt þ b6EQASSjt þ b7DEPO=TAjt þ b8LNGDPt þ b9INFLt
þ b10LNRPIVt þ b11LNPCEt þ b12MKTCAP=GDPt þ ejt
ð2Þ
Equation (2) is estimated through a fixed effect regression taking each bank’sROA as the dependent variable. The opportunity to use fixed effects rather thanrandom effects regression model has been tested with the Hausman test. Equation (2)is estimated by using White’s (1980) transformation to control cross-sectionheteroscedasticity of the variables. Nevertheless, as a robustness check, we havealso performed the regression models by using the random effects model (REM).
Table 5 provides information on the degree of correlation between theexplanatory variables used in the panel regression analysis. The matrix shows thatin general the correlation between the explanatory variables is not strong, suggestingthat multicollinearity problem is not severe. Kennedy (2008) points out thatmulticollinearity is a problem when the correlation is above 0.80, which is not thecase here. However, it is worth noting that the correlations between LNGDP,LNPRIV and LNPCE variables are relatively high. To address this concern, we haveestimated a stepwise regression model by regressing the LNGDP, LNPRIV, andLNPCE variables separately. All in all, the results do not qualitatively change thefindings. We therefore choose not to report the regression results in the paper, butthey are available upon request.
5. Empirical findings
The regression results focusing on the relationship between bank profitability andthe explanatory variables are presented in Table 6. To conserve space, the fullregression results, which include both bank and time specific effects are not reportedin the paper. The model performs reasonably well with most variables remainingstable across various regressions tested. The explanatory power of the regressionmodels is reasonably high, while the F-statistic for all models is significant at the 1%level. The adjusted R2 is also considerably higher than reported by Staikouras andWood (2003), Williams (2003), and Kosmidou, Pasiouras, and Tsaklanganos (2007).
Total loans divided by total assets entered the regression model with a positivesign and is statistically significant at the 5% level in both FEM and REM regression
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Table
5.
Correlationmatrix
fortheexplanatory
variables.
LLP/TL
EQASS
NII/TA
NIE
/TA
LOANS/TA
LNTA
DEPO/TA
LNGDP
INFL
LNPRIV
LNPCE
MKTCAP/
GDP
LLP/TL
1.000
EQASS
0.169**
1.000
NII/TA
70.267**
70.001
1.000
NIE
/TA
0.347**
70.008
70.009
1.000
LOANS/TA
70.162**
70.217**
0.253**
0.044
1.000
LNTA
0.122**
70.209**
70.056
70.059
0.011
1.000
DEPO/TA
70.031
0.004
0.056
0.004
70.013
70.181**
1.000
LNGDP
70.109**
0.115**
70.048
0.244**
0.119**
70.723**
0.141**
1.000
INFL
70.070
0.162**
70.004
0.198**
0.116**
70.248**
0.021
0.550**
1.000
LNPRIV
70.128**
0.110**
0.005
0.237**
0.181**
70.725**
0.138**
0.992**
0.559**
1.000
LNPCE
70.118**
0.112**
70.022
0.242**
0.149**
70.723**
0.142**
0.998**
0.556**
0.997**
1.000
MKTCAP/G
DP
0.034
0.238**
70.269**
0.125**
70.221**
0.111**
0.009
0.251**
0.288**
0.199**
0.227**
1.000
Note:Thetable
presents
theresultsfrom
Spearm
anrcorrelationcoeffi
cients.**and
*indicatessignificance
at1%
and5%
levels.Thenotationusedin
thetable
isdefined
asfollows:LLP/TLisa
measure
ofbankcreditrisk
calculatedastheratiooftotalloanloss
provisionsdivided
bytotalloans;EQASSisameasure
ofcapitalization,calculatedasbookvalueofshareholdersequityasa
fractionoftotalassets;
NII/TA
isameasure
ofbankdiversificationtowardsnoninterest
income,
calculatedastotalnon-interest
incomedivided
bytotalassets;
NIE
/TA
isaproxymeasure
for
managem
entquality,calculatedaspersonnelexpensesdivided
bytotalassets;LOANS/TA
isusedasaproxymeasure
ofloansintensity,calculatedastotalloansdivided
bytotalassets;LNTA
isa
proxymeasure
ofsize,calculatedasanaturallogarithm
oftotalbankassets;
DEPO/TA
istotaldeposits
divided
bytotalassets;
LNGDPis
naturallogofgross
domesticproducts;
INFL
isthe
inflationrate;LNPRIV
isnaturallogarithm
ofprivate
investm
ents;LNPCEisnaturallogarithm
ofprivate
consumptionexpenditure;MKTCAP/G
DPistheratioofstock
market
capitalizationover
GDP.
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models. The findings imply that banks with higher loans-to-asset ratios tend to bemore profitable. Thus, in the case of South Asian banking sectors, bank loans seemto be more highly valued than alternative bank outputs such as investments andsecurities. The result is consistent with among others, Molyneux and Thornton(1992), Guru, Staunton, and Balashanmugam (2002), and Pasiouras and Kosmidou(2007).
Likewise, LNTA exhibits positive coefficient implying that larger banks tend tobe more profitable. Hauner (2005) offers two potential explanations for which sizecould have a positive impact on bank performance. First, if it relates to marketpower, large banks should pay less for their inputs. Second, there may be increasingreturns to scale through the allocation of fixed costs (e.g. research or riskmanagement) over a higher volume of services or from efficiency gains from aspecialized workforce. It is interesting to note that the coefficient of LLP/TL revealsa positive relationship with bank profitability, which is in consonance with Bergerand DeYoung’s (1997) skimping hypothesis. To recap, Berger and DeYoung (1997)suggest that under the skimping hypothesis, banks maximizing long-run profits mayrationally choose to have lower costs in the short-run by skimping on resourcesdevoted to underwriting and monitoring loans, but bear the consequences of greaterloan performance problems.
The coefficient of NII/TA variable has consistently exhibited positive andsignificant relationship with bank profitability under both FEM and REMregression models. The results imply that banks which derive a higher proportionof its income from non-interest sources such as fee-based services tend to reporthigher profitability levels. On the other hand, NIE/TA has consistently exhibitednegative and significant impact on bank profitability. The results imply that anincrease (decrease) in these expenses reduces (increases) the profits of banksoperating in South Asian banking sectors. Guru, Staunton, and Balashanmugam(2002), Pasiouras and Kosmidou (2007), and Kosmidou (2008) among others, alsofound poor expenses management contributed to lower profitability levels. Clearly,efficient cost management is a prerequisite to improve the profitability of banksoperating in South Asian banking sectors. Furthermore, it could be argued that theSouth Asian banking sectors have not reached the maturity level required to linkquality effects from increased spending to higher bank profitability.
As expected, the empirical findings suggest that EQASS has a positiverelationship with bank profitability and is statistically significant at the 1% levelin the REM regression model. The result is consistent with previous studies (Isik andHassan 2003; Staikouras and Wood 2003; Goddard, Molyneux, and Wilson 2004;Pasiouras and Kosmidou 2007; Kosmidou 2008) providing support to the argumentthat well capitalized banks face lower costs of going bankrupt, thus lowers their costof funding, or that they require less external funding resulting in a higherprofitability. Nevertheless, strong capital structure is essential for banks indeveloping economies since it provides additional strength to withstand financialcrises and increased safety for depositors during unstable macroeconomic conditions(Sufian 2009).
The impact of network embeddedness (DEPO/TA) on bank profitability ispositive supporting the earlier findings by among others Randhawa and Lim (2005)and Sufian (2007) which have found that large banks tend to be relativelymanagerially efficient. It could be argued that large banks with extensive branchnetworks may have added advantage compared to their smaller bank counterparts as
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they may attract more deposits and loan transactions and in the process commandlarger interest rate spreads and subsequently higher profitability levels.
The results of the impact of GDP growth on ROA are consistent with Hassanand Bashir (2003), Pasiouras and Kosmidou (2007), and Kosmidou (2008) providingsupport to the argument of positive association between economic growth andbanking sector’s performance. On the other hand, INFL is negatively related toSouth Asian banks’ profitability, implying that during the period under study, levelsof inflation have been unanticipated by banks. This impedes banks from adjustinginterest rates charged on loans and assigning deposits accordingly and consequentlyresults in lower profits.
During the period under study, the results seem to suggest that LNPRIV exerts apositive impact on bank profitability. The empirical findings clearly indicate thatbank financing remains an important source of financing for the private sector inSouth Asian countries, which could be attributed to the underdevelopment offinancial markets. On the other hand, it is observed from Table 6 that the coefficientof LNPCE is negative, implying that a higher (lower) level of private consumptionenhances the profitability of banks operating in South Asian banking sectors. Thereare a few plausible explanations. First, a higher level of private consumption reflectsa lower level of interest rates, which subsequently has a negative impact on bankinterest margins. Second, higher private consumption reflects lower deposits, sincedepositors have less incentive to save, which could be argued to be the main source offunds for banks in South Asian banking sectors. It is also observed from Table 6 thatthe impact of stock market capitalization (MKTCAP/GDP) on bank profitability isnegative, implying that during the period under study, stock markets in South Asiancountries offered substitution possibilities to potential borrowers. However, thefindings should be interpreted with caution since the coefficients of the variables arenever significant at any conventional levels.
5.1. Robustness checks
In order to check for the robustness of the results, we performed a number ofsensitivity analyses. First, we performed a similar regression model by having onlyBangladeshi banks in the sample. The results are presented in columns 3 and 4 ofTable 6. The results seem to suggest that coefficients of both LNTA and EQASS arenegative, but not statistically significant at any conventional levels. On the otherhand, the empirical findings suggest that credit risk (LLP/TL) and networkembeddedness (DEPO/TA) exhibit positive and significant impact on Bangladeshbanks’ profitability. It is also apparent that the coefficient of LNGDP is negative, butis not significant at any conventional levels. Likewise, the impact of inflation (INFL)is negative and is statistically significant at the 1% level under both FEM and REMregression models. It is also observed from columns 3 and 4 of Table 6 that thecoefficient of LNPCE is positive, but is not significant at any conventional levels. Thestock market development has negative and significant impact on the profitability ofbanks in Bangladesh, implying that the country’s stock market offers substitutionpossibility to potential borrowers.
Secondly, we performed a similar regression model on Sri Lankan banks. Theresults are presented in columns 5 and 6 of Table 6. It is observed that the coefficientsof LNGDP and LNPCE lose their explanatory power under both FEM and REMregression models, implying that economic growth and private consumption
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Table
6.
Panel
fixed
andrandom
effects
regressionsresults.
(I)
(II)
(III)
(IV)
(V)
(VI)
(VII)
(VIII)
Allbanks
Allbanks
Bangladesh
banks
Bangladesh
banks
SriLankabanks
SriLanka
banks
Pakistanbanks
Pakistan
banks
(FEM)
(REM)
(FEM)
(REM)
(FEM)
(REM)
(FEM)
(REM)
CONSTANT
73.664
72.943
18.690
1.827
737.084*
717.387**
711.096**
77.891
(71.162)
(71.598)
(0.698)
(0.103)
(71.776)
(72.319)
(71.988)
(71.494)
Bankcharacteristics
LOANS/TA
0.025**
0.024**
0.009*
0.017***
0.075***
0.093***
0.003
0.013
(2.503)
(2.291)
(1.638)
(4.340)
(3.078)
(3.037)
(0.386)
(1.372)
LNTA
0.453
0.338
70.237
70.239
0.683*
0.223**
0.022
0.355***
(1.352)
(3.442)
(70.747)
(71.598)
(1.703)
(2.472)
(0.084)
(2.691)
LLP/TL
0.020
0.017
0.026**
0.026*
0.154
0.223**
0.007
0.004
(1.302)
(0.893)
(2.343)
(1.950)
(1.244)
(2.480)
(0.267)
(0.174)
NII/TA
0.428***
0.492***
0.445***
0.543***
1.341**
1.638***
0.348***
0.256*
(5.052)
(6.113)
(6.764)
(7.446)
(2.019)
(4.064)
(2.787)
(1.885)
NIE
/TA
70.968***
70.950***
70.873***
70.837***
71.756***
71.921***
70.813***
70.736***
(710.495)
(78.316)
(713.065)
(715.640)
(77.500)
(75.334)
(721.015)
(718.238)
EQASS
0.086
0.068**
70.005
0.012
0.221**
0.107*
0.035
0.033
(1.436)
(2.237)
(70.195)
(0.479)
(2.317)
(1.769)
(0.709)
(1.195)
DEPO/TA
0.001
0.000
3.870**
1.573
0.000
70.003
0.272**
0.128
(0.263)
(70.019)
(2.401)
(1.453)
(70.056)
(71.540)
(2.069)
(1.596)
Economic
conditions
LNGDP
3.082***
3.105***
78.024
73.277
12.008
1.011*
13.558***
10.917
(2.774)
(3.971)
(71.289)
(70.727)
(0.653)
(1.924)
(2.695)
(2.174)
INFL
70.012
70.001
70.104***
70.077***
70.119**
70.024
0.042
0.015
(70.650)
(70.106)
(73.765)
(73.489)
(71.985)
(70.501)
(1.595)
(0.709)
Financialdevelopment
LNPRIV
1.311
0.978*
2.818
0.467
72.098
70.365
70.173
(1.296)
(1.703)
(0.937)
(0.230)
(70.770)
(70.268)
(70.119)
LNPCE
74.179***
73.900***
3.497
3.007
77.926
712.147***
710.431**
(72.899)
(73.446)
(1.428)
(1.487)
(70.521)
(72.738)
(72.256)
(continued)
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Table
6.
(Continued).
(I)
(II)
(III)
(IV)
(V)
(VI)
(VII)
(VIII)
Allbanks
Allbanks
Bangladesh
banks
Bangladesh
banks
SriLankabanks
SriLanka
banks
Pakistanbanks
Pakistan
banks
(FEM)
(REM)
(FEM)
(REM)
(FEM)
(REM)
(FEM)
(REM)
MKTCAP/G
DP
70.013
70.004
70.065**
70.067**
70.075
70.001
70.002
(71.245)
(70.561)
(72.517)
(72.441)
(71.354)
(70.143)
(70.288)
R2
0.779
0.664
0.894
0.818
0.900
0.830
0.835
0.694
Adjusted
R2
0.742
0.657
0.868
0.808
0.876
0.816
0.802
0.679
Durbin–Watsonstat
1.856
1.630
1.543
1.508
1.924
1.731
2.007
1.591
F-statistic
21.009***
97.699***
35.067***
82.533***
38.713***
58.478***
25.359***
45.574***
No.ofobservations
606
606
234
234
118
118
254
254
Note:Thenumber
ofobservationsistoolimited
toperform
REM
regressionsbyhavingLNPRIV
,LNPCE,andREGCAR
simultaneously.Therefore,weperform
astepwise
REM
regressionmodelsbyaddingLNPRIV
,LNPCE,andREGCAR
variablesoneatatime.
Theresultssuggestthatboth
theLNPRIV
andLNPCE
variablesare
not
significantatanyconventionallevels.Ontheother
hand,REGCAR
isnegativelyrelatedto
SriLankabanks’profitability.However,thecoeffi
cientofthevariable
isonly
statisticallysignificantatthe10%
level
(p-value¼
0.059).Thefullregressionresultsare
available
uponrequestfrom
theauthors.
ROA
jt¼
b 0þb 1LOANS=TA
jtþb 2LNTA
jtþb 3LLP=TLjt
þb 4NII=;T
Ajtþb 5NIE=TA
jtþb 6EQASSjtþb 7DEPO=TA
þb 8LNGDPþb 9IN
FLþb 1
0LNRPIVþb 1
1LNPCEþb 1
2MKTCAP=GDP
þe jt
ThedependentvariableisROAcalculatedasprofitafter
taxdivided
bytotalassets;LOANS/TA
isusedasaproxymeasure
ofloansintensity,calculatedastotalloansdivided
bytotalassets;LNTA
isaproxymeasure
ofsize,calculatedasanaturallogarithm
oftotalbankassets;LLP/TLisameasure
ofbankcreditrisk
calculatedastheratiooftotal
loanloss
provisionsdivided
bytotalloans;NII/TA
isameasure
ofbankdiversificationtowardsnon-interestincome,calculatedastotalnon-interestincomedivided
bytotal
assets;NIE
/TA
isaproxymeasure
formanagem
entquality,calculatedaspersonnelexpensesdivided
bytotalassets;EQASSisameasure
ofcapitalization,calculatedasbook
valueofshareholdersequityasafractionoftotalassets;DEPO/TA
isusedasaproxymeasure
ofnetwork
embeddednesscalculatedastotaldepositsdivided
bytotalassets;
LNGDPisnaturallogofgross
domesticproducts;IN
FListherate
ofinflation;LNPRIV
isnaturallogarithm
ofprivate
investm
ents;LNPCEisnaturallogarithm
ofprivate
consumptionexpenditure;MKTCAP/G
DPistheratioofstock
market
capitalizationover
GDP.Values
inparentheses
are
t-statistics.***,**,and*indicate
significance
at1,
5,and10%
levels,respectively.
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expenditures have no significant impact on banks’ profitability in Sri Lanka. Theresults also suggest that size (LNTA) and credit risk (LLP/TL) exert positive andstatistically significant impact on Sri Lankan banks’ profitability levels, while theimpact of inflation (INFL) is statistically significantly negative. The empiricalfindings suggest that larger banks and banks with higher credit risks in Sri Lankatend to exhibit higher profitability levels.
Thirdly, we repeated Equation (2) by removing Bangladeshi and Sri Lankanbanks. The results are presented in columns 7 and 8 of Table 6. The empiricalfindings suggest that all explanatory variables continued to remain robust in terms ofdirections and significance levels with the exception of LOANS/TA, which loses itsexplanatory power in both FEM and REM regression models, while LNGDP andLNPRIV lose their explanatory power in the REM regression model. It is alsoapparent that coefficients of LNTA and DEPO/TA variables remain positive and arestatistically significant at the 1% level in both REM and FEM regression models.The findings indicate that in the case of the Pakistani banking sector, larger banksand banks with higher deposit-to-asset ratios tend to be more profitable.
Finally, we repeated Equation (2) and included REGCAR (regulatory capitalratio) as an explanatory variable in the regression models. To the extent thatregulatory capital is not remunerated, or yield is lower than market rates, theseregulations impose a burden on banks. Thus, it is of interest to examine the impactof regulation on the performance of banks in Bangladesh, Pakistan, and Sri Lanka.We find that the coefficient of the REGCAR variable has consistently exhibitednegative signs in all regression models estimated. A plausible explanation is that highregulatory capital requirement could be considered as an implicit tax, which impedesthe ability of banks to intermediate and therefore exerts negative impact on bankprofitability. However, the coefficient of the variable is never significant in any of theregression models. We therefore choose not to report the findings in the paper(although they are available upon request).
5.2. Further robustness checks
To further check for the robustness of the results, we introduced binary dummyvariables DUMBANG, DUMSRI, and DUMPAKI, which take a value of 1 forBangladeshi, Sri Lankan, and Pakistani banks, and 0 otherwise in regression modelsI, II, and III, respectively. The results are presented in Table 7. It is worth noting thatthe coefficients of the baseline variables stay mostly the same: they keep the samesign, same order of magnitude, and remain significant as in the baseline regressions(albeit sometimes at different levels). Therefore, we will only discuss the results of thenew variables added to the baseline specification.
It is observed from column 1 of Table 7 that DUMBANG (a dummy variablethat takes a value of 1 for banks operating in the Bangladesh banking sector, 0otherwise) entered the regression model with a negative sign. Likewise, the resultsseem to suggest that the coefficient of DUMSRI (a dummy variable that takes avalue of 1 for banks operating in the Sri Lanka banking sector, 0 otherwise) isnegative. The empirical findings seem to suggest that banks operating in Bangladeshand Sri Lanka have been relatively less profitable. However, the results need to beinterpreted with caution since the coefficients of the variables are never significant atany conventional levels. On the other hand, the results suggest that the coefficient ofDUMPAKI (a dummy variable that takes a value of 1 for banks operating in the
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Pakistan banking sector, 0 otherwise) is positive, implying that banks operating inthe Pakistan banking sector have been relatively more profitable. Again, thecoefficient of the variable is not statistically significant at any conventional levels.
Banks of different ownership forms may react differently to similar profitabilitydeterminants. To capture the effects of organizational forms and governance onSouth Asian banks’ profitability, a binary dummy variable DUMFORB isintroduced in regression model IV to examine factors that influence foreign banks’profitability. The variable is expected to exhibit a positive sign since foreign banks indeveloping and transition countries have been found to successfully capitalize ontheir advantages and exhibit higher efficiency levels compared to their domestic bankpeers (e.g. Isik and Hassan 2002; Ataullah and Le 2006; Havrylchyk, 2006). Theresults are presented in column 4 of Table 7.
As expected, DUMFORB entered the regression models positively, implying thatthe foreign owned banks have been more profitable compared to their domesticallyowned bank counterparts. This should come as no surprise because of the ability ofthe foreign-owned banks to capitalize on advanced risk management andoperational techniques, which are usually made available through their parentbanks abroad. Furthermore, foreign-owned banks tend to cherry-pick the bestborrowers available in the market (particularly those from their own countries of
Table 7. Panel random effects regressions results.
(I) (II) (III) (IV)
CONSTANT 70.353 74.661** 71.460 73.211*(70.123) (72.040) (70.643) (71.732)
Bank characteristicsLOANS/TA 0.024** 0.024** 0.024** 0.025**
(2.234) (2.294) (2.219) (2.345)LNTA 0.315*** 0.266** 0.290*** 0.341***
(3.584) (2.391) (3.374) (3.447)LLP/TL 0.019 0.018 0.019 0.017
(0.975) (0.940) (1.009) (0.906)NII/TA 0.501*** 0.514*** 0.508*** 0.483***
(6.474) (6.602) (6.747) (5.703)NIE/TA 70.956*** 70.957*** 70.960*** 70.951***
(78.124) (78.160) (78.092) (78.321)EQASS 0.065** 0.063** 0.063** 0.069**
(2.249) (2.032) (2.172) (2.261)DEPO/TA 0.000 0.000 0.000 0.000
(70.079) (70.331) (70.188) (70.001)Economic conditionsLNGDP 2.456*** 3.484*** 2.485*** 3.028***
(4.015) (3.692) (3.855) (3.835)INFL 70.008 70.008 70.011 70.001
(70.497) (70.460) (70.688) (70.095)Financial developmentLNPRIV 1.761* 1.080* 1.937* 0.939*
(1.714) (1.798) (1.830) (1.679)LNPCE 74.126*** 74.052*** 74.227*** 73.771***
(73.150) (73.231) (73.088) (73.385)MKTCAP/GDP 70.007 70.008 70.010 70.005
(70.839) (71.118) (71.061) (70.703)
(continued)
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origin), thereby improving the quality of their asset portfolio. The empiricalobservation that foreign banks perform better than domestic banks in developingcountries also implies that the technical savvy of banks from developed countrieswould generally overcome the home field advantage of domestic banks, especiallywhen the domestic economy has relatively unsophisticated financial markets andinstitutions (Jeon and Miller 2005).
Finally, the South Asian banking sectors could adversely be affected by the Asianfinancial crisis in 1997 to 1998. To address this concern, we repeated Equation (2)and added a control dummy variable DUMCRIS (a dummy variable that takes avalue of 1 for years 1997 and 1998, 0 otherwise) in the regression model. All in all,the results remained robust in terms of directions and significance levels. To conservespace, the results are not reported in the paper, but are available upon request.
Table 7. (Continued).
(I) (II) (III) (IV)
Country dummiesDUMBANG 70.762
(71.315)DUMSRI 72.544
(71.179)DUMPAKI 0.893
(1.506)Bank ownershipDUMFORB 0.815
(1.439)
R2 0.662 0.666 0.664 0.665Adjusted R2 0.655 0.658 0.656 0.658Durbin–Watson stat 1.618 1.648 1.629 1.632F-statistic 89.328*** 90.611*** 89.855*** 89.079***No. of observations 606 606 606 606
ROAjt ¼ b0 þ b1LOANS/TAjt þ b2LNTAjt þ b3LLP/TLjt
þ b4NII/TAjt þ b5NIE/TAjt þ b6EQASSjt þ b7DEPO/TAjt
þ b8LNGDPt þ b9INFLt þ b10LNRPIVt þ b11LNPCEt þ b12MKTCAP/GDPt
þ b13DUMBANGj þ b14DUMSRIj þ b15DUMPAKIjþ b16DUMFORBjt
þ ejt
The dependent variable is ROA calculated as profit after tax divided by total assets; LOANS/TA is used asa proxy measure of loans intensity, calculated as total loans divided by total assets; LNTA is a proxymeasure of size, calculated as a natural logarithm of total bank assets; LLP/TL is a measure of bank creditrisk calculated as the ratio of total loan loss provisions divided by total loans; NII/TA is a measure of bankdiversification towards non-interest income, calculated as total non-interest income divided by total assets;NIE/TA is a proxy measure for management quality, calculated as personnel expenses divided by totalassets; EQASS is a measure of capitalization, calculated as book value of shareholders equity as a fractionof total assets; DEPO/TA is a proxy measure of network embeddedness calculated as total depositsdivided by total assets; LNGDP is natural log of gross domestic products; INFL is the rate of inflation;LNPRIV is natural logarithm of private investments; LNPCE is natural logarithm of private consumptionexpenditure; MKTCAP/GDP is the ratio of stock market capitalization over GDP; DUMBANG,DUMSRI, and DUMPAKI are dummy variables that takes a value of 1 for banks operating in theBangladesh, Sri Lanka, and Pakistan respectively, 0 otherwise; DUMFORB is a dummy variable thattakes a value of 1 if a bank is a foreign owned bank, 0 otherwise. Values in parentheses are t-statistics. ***,**, and *indicate significance at 1, 5, and 10% levels, respectively.
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6. Concluding remarks
The South Asian countries have undergone noteworthy financial reforms. To date,empirical studies on the impact of reforms have mainly concentrated on largecountries, i.e. China. On the other hand, empirical evidence on South Asiancountries is relatively scarce. By using an unbalanced bank level panel data, thisstudy seeks to examine the performance of 77 Bangladeshi, Sri Lankan, andPakistani commercial banks. We cover the period between 1997 and 2008 andcontrol a wide array of macroeconomic and bank specific characteristics.
The empirical findings of this study indicate that bank specific characteristics, inparticular liquidity, non-interest income, credit risk, and capitalization have positiveand significant impact on bank performance, while cost is negatively related to bankprofitability. However, the impact is not uniform across the countries studied.During the period under study, the results indicate that liquidity and credit risk arepositively related to Bangladeshi and Sri Lankan banks, but not Pakistani banks,while size exerts positive and significant impact on bank profitability in Sri Lankaand Pakistan, but not in Bangladesh. The findings also suggest that the level ofcapitalization has a positive and significant relationship with Sri Lankan banks, butnot Bangladeshi and Pakistani banks, while the impact of network embeddeddnesspositively and significantly explains profitability of banks in Bangladesh andPakistan, but not in Sri Lanka.
The findings of this study have considerable policy relevance. It could be arguedthat relatively profitable banks will be able to offer more new products and services.To this end, the role of technology advancement is particularly important given thatbanks with relatively more advanced technologies may have an added advantageover their peers. The continued success of the South Asian financial sector dependson its efficiency, profitability, and competitiveness. Furthermore, in view of theincreasing competition attributed to the more liberalized banking sector, bankmanagements as well as policymakers will be more inclined to find ways to obtain theoptimal utilization of capacities as well as making the best use of their resources, sothat these resources are not wasted during the production of banking products andservices.
Moreover, the ability to maximize risk-adjusted returns on investment to sustainstable and competitive returns is crucial for South Asian banking sectors to remaincompetitive. Thus, from the regulatory perspective, the performance of banks will bebased on their efficiency and profitability. Going forward, the policy direction will bedirected towards enhancing the resilience and efficiency of the financial institutionswith the aim to intensify the robustness and stability of the banking sectors.
Acknowledgements
We would like to thank John Zavos (the editor) and two anonymous referees for theconstructive comments and suggestions, which have significantly improved the contents of thepaper. The usual caveats apply.
Notes
1. The South Asian Association for Regional Cooperation (SAARC) is comprised ofAfghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka.
2. In South Asia, regional trends and averages can be misleading because India accounts forthree-fourths of the region’s population and about 80% of its GDP. Furthermore, Indiahas relatively developed capital and financial markets. Due to these reasons, we have
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excluded Indian banks to avoid estimation bias. On the other hand, Bhutan, Maldives,and Nepal are excluded from the sample due to unavailability of data.
3. Laeven and Majnoni (2003) point out that economic capital should be tailored to copewith unexpected losses and loan loss reserves should instead buffer the expectedcomponent of the loss distribution. Consistent with this interpretation, loan lossprovisions should be considered and treated as cost, which will be faced with certaintyover time, but is uncertain as to when it will materialize.
4. It could be argued that higher private consumption expenditures (PCE) leads to lowerdeposit levels.
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