Sharma Et Al 2013

Efficiency and productivityof banking sector

A critical analysis of literature anddesign of conceptual model

Dipasha Sharma, Anil K. Sharma and Mukesh K. BaruaDepartment of Management Studies,

Indian Institute of Technology Roorkee, Roorkee, India

Abstract

Purpose The purpose of this paper is to discuss a comprehensive literature survey of studiesfocusing on the efficiency and productivity of the banking sector using parametric and non-parametricfrontier techniques.

Design/methodology/approach Critically reviewing 106 studies published across the worldfrom 1994 to 2011, a conceptual framework is developed for the studies assessing the efficiency andproductivity of the banking industry using non-parametric DEA frontier approach.

Findings Both the frontier approaches, parametric and non-parametric, are gaining an edge overthe traditional financial performance measures. In the non-parametric approach, data envelopmentanalysis (DEA) is widely applied to measure a banks efficiency and productivity. Studies conducted indeveloped countries such as the USA, the UK and Europe are now emerging with the new concepts ofbanking efficiency.

Research limitations/implications These findings are based only on the critical review of106 studies. This study suggests the direction for future research and identifies the gap in existingliterature with the development of a conceptual model.

Originality/value This study is original in nature and included literature published in recentissues of 2011.

Keywords Banking, Productivity, Efficiency, Parametric and non-parametric frontier approach,Data envelopment analysis, Productivity rate

Paper type Literature review

1. IntroductionBanking institutions of any nation play a significant role in structuring economicdevelopment and economic growth via the efficient intermediation of funds fromborrowers to savers. A strong banking sector effectively channels funds and financialproducts in such a way as to strengthen the financial and economic system of any nation.Therefore, the sound performance of the banking sector has always been a key issue forresearchers and policy makers charged with ensuring a strong and economicallydeveloped nation. Traditionally, the performance and efficacy of banking institutions ismeasured by financial ratios, but this approach has a major demerit in terms of itssubjectivity and reliance on benchmarking ratios (Yeh, 1996). Sherman and Gold (1985)

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1755-4179.htm

The authors express thanks for the suggestions made by two anonymous reviewers and theEditor, Dr Bruce Burton, which have helped improve the paper. Dipasha Sharma gratefullyacknowledges the financial support provided by University Grant Commission New Delhi, India,for her research in the form of a junior research fellowship.

Received 12 October 2011Revised 3 February 2012

Accepted 17 April 2012

Qualitative Research in FinancialMarkets

Vol. 5 No. 2, 2013pp. 195-224

q Emerald Group Publishing Limited1755-4179

DOI 10.1108/QRFM-10-2011-0025

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initiated the frontier analysis approach to bank performance assessment. They arguedfor the application of frontier analysis techniques in bank performance evaluationinstead of financial ratios and other traditional financial measures. The frontier analysistechnique handles multiple inputs-outputs and captures long-term performance, whilehelping to separate poor performers from the best institutions. It is a sophisticatedmethod of benchmarking institutions on the basis of their respective efficiency, whileidentifying the best practice frontier (Berger and Humphrey, 1997). This concept offrontier technique for the estimation of efficiency of production units was outlined byFarrell (1957), on the basis of the primary model developed by Koopmans (1951) andDebreu (1951). Afterwards parametric and non parametric frontier analysis techniqueshave been widely applied in banking efficiency and productivity.

This paper provides a comprehensive review of studies that apply frontier techniquesto the analysis of banking efficiency and productivity, covering investigations from1994 to 2011. The structure of this paper is as follows: Section 2 describes the conceptualframework; Section 3 outlines the survey on the basis of conceptual model beforecritiquing methodological issues and the selection of approaches, variables andtechniques. Section 4 suggests future research in the area and discusses emergingoperational research techniques in banking and finance. Section 5 concludes the survey.

2. Framework for efficiency and productivityIn our study we have reviewed 106 studies, published in various international journals,conferences, books, thesis and working papers. A comprehensive snapshot of106 reviewed studies is given in Table AI at the end of the paper and all other analysis,tables and figures are structured on the basis of Table AI. The distribution of existingstudies from various sources is given in Table I. As shown in Table I, the most frequentsources of publications are the Journal of Banking and Finance (24); followed byEuropean Journal of Operational Research (11), Omega (4),The Journal of the OperationalResearch Society (4) and so on (figures given in parenthesis present the number ofpublications in respective journals). The reviewed studies also include internationalconference papers available on internet web sites, while the others present the thesis,working papers and books on banking efficiency and productivity.

In our survey we have included studies from the year 1994 to 2011. Year wiseclassification of reviewing studies is given in Table II.

Figure 1 shows the diagrammatic view of year wise publication of existing literaturefrom the year 1994 to 2011. Number of publications is increasing each year and it is clearfrom the given figure that the frontier practices in banking performance and efficiencyevaluation is gaining popularity year by year. We have reviewed maximum number ofstudies from recent publications in our study; 2010 (16), followed by the year 2009 (13)(figures given in parenthesis present no. of publications in a particular year).

We further classified our survey of literature on the basis of studies conducted on aparticular country. Table III provides a snapshot of country wise studies in our reviewedliterature. Studies were conducted in different countries as well as cross-country analysiswas done by the researchers. Researchers conducted good number of studies inEuropean countries; comparison of efficiency across the European Union (Pastor et al.,1997), productivity growth among the European countries (Casu and Molyneux, 2003).The UK is among the top choice for European country analysis. Figure 2 shows thecountry wise publications of literature. A good number of studies were conducted in the

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banking industry of developed nations like the USA, the UK, Europe. Developing nationsare also gaining attention of researchers as within India only, 24 studies were conductedby the eminent researchers during pre and post liberalization periods of Indian bankingsector (Bhattacharyya et al., 1997; Saha and Ravisankar, 2000; Sathye, 2003). It clearly

Particulars No. of papers

JournalsJournal of Banking and Finance 24European Journal of Operational Research 11Omega 04Managerial Finance 04The Journal of the Operational Research Society 04SSRN 04Socio Economic Planning Sciences 03Interfaces 02Economic Modeling 02Applied Financial Economics 02Management Accounting Research 02Journal of High Technology Management Research 02Unitar e-Journal 01ICRA BULLETIN 01Review of Financial Economies 01International Journal of Operations Research 01Eurasian Journal of Business and Economics 01International Journal of Business Performance Management 01International Review of Economics and Finance 01Journal of Asian Economics 01The Pakistan Development Review 01Applied Economics 01International Journal of Operational Research 01Research in International Business and Finance 01Journal of Asian Economies 01MPRA 01Journal of Business, Finance and Accounting 01Journal of Multinational Financial Management 01Investment Management and Financial Innovations 01China Economic Review 01International Journal of Productivity and Performance Management 01Public Choice 01International Journal of Production Economies 01Applied Mathematics and Computation 01International Journal of Bank Marketing 01Expert System with Application 01Journal of Business Research 01Computers and Operational Research 01International Journal of Business Management 01International conference papers 02Working papers and othera 13Total 106

Note: aOther includes thesis, books and surveys available on internet web sitesSource: Authors on calculations based on Table AI in the Appendix

Table I.Distribution of reviewed

articles from varioussources


197

S. no. Year No. of publication

1 1994 022 1995 013 1996 014 1997 045 1998 016 1999 037 2000 018 2001 029 2002 04

10 2003 1011 2004 1212 2005 0713 2006 1114 2007 0715 2008 0616 2009 1317 2010 1618 2011 05Total 106

Source: Based on authors own calculation of reviewed studies given in Table AITable II.Year wise publications

Figure 1.Year wise publicationof studies

05

101520

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011N

o. o

f Pub

licat

ions

Years

Year wise publications

Country No. of publications

India 24Other 19Europe 17USA 12Cross country 10UK 5Asia 3Australia 3China 3

Note: Other includes Hong Kong, Singapore, Greece, Spain, Thailand, Taiwan, Iran, Pakistan,Bangladesh, Japan, Italy, Poland, France and CanadaSource: Based on authors own calculation of reviewed studies given in Table AI

Table III.Country wisepublications

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depicts the popularity of frontier techniques in developing country research. Studies onbanking efficiency in developed nations like the USA, the UK, and Europe are gainingnew dimensions with the new concepts of shareholder value efficiency (Fiordelisi andMolyneux, 2010a, b), market returns (Beccalli et al., 2006), etc. but in developing nationsthere is a lack of literature on these new paradigms of banking efficiency.

3. Literature on efficiency and productivity/development of conceptualmodelThis section presents a conceptual flow chart of studies and break up of literature onthe basis of the focus areas of efficiency and productivity change of the banking sector.Table IV represents the focus areas of reviewing studies. Most of the studies arefocusing on efficiency/productivity estimation and empirical evidences on theirdeterminants (29) (the figure given in parenthesis present number of publications in thefocus area of research). New emerging areas of shareholder value and impact of stock

Figure 2.Country wise

publications of studies

No. of Publications

UK5%

India25%

Asia3%

Australia3%

China3%

US13%

Croos country10%

Europe18%

other20%

Focus No. of papers

(a) Efficiency and productivity estimation 20(b) Empirical evidences on determinants of efficiency and productivity 37(c) Efficiency, productivity: deregulation and reforms 7(d) Bank branch efficiency 8(e) Efficiency and mergers/acquisition 3(f) Efficiency and shareholder value, share market return 8(g) Efficiency and productivity: concepts and models 13(h) Literature survey 6(i) Future 4Total 106

Note: Future represents the direction of future research with the application of neural networking,fuzzy logic, analytical hierarchical process, and bootstrapping in efficiency estimation of the bankingsectorSource: Based on authors own calculation of reviewed studies given in Table AI

Table IV.Focus areas of literature


199

market on efficiency are also gaining consideration in the banking industry. The futuredirection of research is emerging with the concepts of fuzzy, neural networking,artificial intelligence and other operational research based techniques in efficiency andproductivity change of the banking sector.

Figure 3 diagrammatically shows the distribution of focus areas of reviewing studies.a-i show the breakup of literature in focus areas given in Table IV. a represents the studiesin the focus area of efficiency and productivity estimation, b depicts the studies with theempirical evidences on determinants of efficiency and productivity, c shows the effects ofderegulation and reforms on efficiency and productivity, d describes studies in bankbranch efficiency, e denotes studies on the effects of mergers and acquisition onefficiency, f presents the studies on efficiency and shareholder value/share market return,g shows studies on concepts and models of efficiency and productivity, h describesstudies on literature surveys whereas i denotes studies on future research areas.

Later we classified studies on the basis of various methodologies of research.We identified four categories of methodologies used by the researchers in existingliterature which are conceptual, descriptive, empirical and exploratory cross-sectional.Conceptual studies are based on the concepts, models and limitations of particularapproaches (parametric and non parametric) and the techniques used under differentapproaches. A descriptive study provides application frameworks and explanations ofdifferent issues of frontier approaches. An empirical study is the study which empiricallyestimates and evaluates the efficiency and productivity change using a database. Studiesdone on the basis of surveys are defined as exploratory cross-sectional studies. Table Vdepicts the distribution of methodologies in studies under review of our study.

Figure 4 shows a snapshot of distribution of methodologies and it clarifies that mostof the studies are empirical in nature.

Figure 3.Focus area of literature

19%

35%

7%

7%

3%

7%

12%

6% 4% (a) Efficiency and productivity estimation(b) evidence on determinants(c) deregulation and reforms(d) bank branch efficiency(e) meregers/acquisition (f) shareholder value/market return(g) concepts and models(h) literature survey(i) future

Methodology No. of papers

Empirical 83Conceptual 7Descriptive 11Exploratory cross sectional 5Total 106

Source: Based on authors own calculation of reviewed studies given in Table AI

Table V.Distribution ofmethodologies used inexisting literature

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On the basis of the distribution of literature and reviewed studies, we design aconceptual flow chart to represent each and every necessary step required in the studybased on frontier approach of banking sector efficiency. This flowchart is designed onthe basis of critical review of existing studies and studies on the literature reviewsconducted by various researchers (Berger and Humphrey, 1997; Ahmad Mokhtar et al.,2006; Emrouznejad et al., 2008; Fethi and Pasiouras, 2009). This conceptual modeldepicts the step by step approach of research to evaluate and assess the efficiency andproductivity change of the banking sector. It also takes into account of determiningvariables and effects on the various corporate events. Figure 5 shows the conceptualframework of this flow chart. This flow chart clearly exhibits each and every necessarystep to develop a study on efficiency and productivity estimation of the bankingindustry. Details and theoretical background of each step, with its empirical reliance isgiven in the later section of this study.

The first and foremost step is to set an objective to evaluate the efficiency andproductivity change of the banking industry. After framing the research question, the nextstep is to choose the efficiency measure to assess and productivity change (Section 3.1.1).Now the selection of appropriate frontier approach is the necessity of situation as literaturehas two approaches, parametric and non parametric (Section 3.1.1.1). To determine returnto scale and input/output orientation is the next agenda in our framework (Sections 3.1.1.2and 3.1.1.3). Selection of the relevant set of input-output, is the most important aspect ofefficiency estimation as we define efficiency as an optimal combination of input-output. Todefine inputs (resources) and output, literature has production approach, intermediationapproach; value added approach, and operating approach (Section 3.1.1.4). These steps offirst stage analysis reveal the relative efficiency and productivity scores. These scores arefurther regressed in second stage analysis to explore the nature of determining variablesand the impact on various corporate events. Determinants of efficiency are in generalclassified as bank specific variables, macroeconomic variables and regulatory variables(Section 3.1.2). Various issues on effects of efficiency are elaborated in existing literature,with the emerging concept of shareholder value efficiency, stock market return, mergersand acquisitions, bankruptcy, and bank branch efficiency (Sections 3.1.3.1-3.1.3.4). Eachand every step of this flowchart is elaborated with its theoretical concepts and empiricalevidences from reviewing studies and existing literature.

3.1 Conceptual modelThe following section of the paper elaborates the components of Figure 5 and explainsvarious aspects of the literature consulted for the present study.

Figure 4.Distribution of

methodologies used inthe reviewed studies

Distribution of methodologies used in studies

Empirical78%

Conceptual7%

Descriptive10%

Exploratory crosssectional

5%


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3.1.1 Efficiency measures. Any producing unit is said to be technically efficientwhen it can produce the maximum amount of output using the given level ofinput, or it can produce given level of output using minimum amount of input.Efficiency in general is a technical term, it is a sign of efficacy of an individualbank, and benchmarking of the industry can be evaluated by peer group (banks)efficiency.

Banks efficiency can be evaluated on the basis of two criteria:

(1) technical efficiency (Farrell, 1957); and

(2) allocative or economic efficiency (Farrell, 1957; Leibenstein, 1966); allocativeefficiency is further classified into two types of efficiency, one is cost efficiency(CE) and the other one is profit efficiency (PE) (Berger and Mester, 1997).

Figure 5.Conceptual framework ofbanking efficiency andproductivity change

To Evaluate Efficiency and Productivity Change of Banking Sector: Concept, Determinants, and Effects

Efficiency Measures Productivity

Selection of frontier

Return to scale

Input/Output orientation

Selection of Input/output

Efficiency scores and Productivity Change

Determinants Effects

Shareholder Value efficiency

Stock marketreturn

Mergers and bankruptcy

Macroeconomic: GDP, Inflation

Bank specific: Size, profitability,loan quality, capital adequacy,liquidity, Operating expenses.

Regulatory: Ownership, Time,Age, Origin, Deregulation,

Reforms, liberalization

Technical efficiency Allocative efficiency

Cost efficiency Profit efficiency

Total Factor productivity Change

Parametric Non Parametric

SFADFA TFA FDH DEA

MPI

CRS VRS

Input Oriented Output Oriented

Production approach Intermediation approach Value Added approach Operating approach

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On the other hand, productivity can be assessed by total factor productivity change(TFPCH). Table VI shows the efficiency measures estimated in reviewing studies. Theoverall technical efficiency (OTE) which includes pure technical efficiency and scaleefficiency is widely evaluated by these studies.

Figure 6 shows the percentage wise composition of various efficiency measures.3.1.1.1 Selection of frontier approach. After the selection of efficiency measures, the

next step is to choose the appropriate frontier approach for the analysis. In theliterature generally there are two main frontier practices:

(1) parametric approach based on the econometric model specification; the otherone is

(2) non parametric approach based on linear programming techniques.

In parametric approach, stochastic frontier analysis (SFA) (Aigner et al., 1977; Meusenand Van Den Broeck, 1977), distribution free approach (DFA) (Berger, 1993) and thickfrontier approach (TFA) (Berger and Humphrey, 1991, 1992) are the most practicingtechniques. data envelopment analysis (DEA) (Charnes et al., 1978) and free disposal hull(FDH) (Deprins et al., 1984) are the common techniques in non parametric approach.Similarly productivity can also be assessed by parametric and non parametricapproaches. Parametric approach results production, cost or revenue functions, whereasnon parametric approach follows the index number method. In non parametric approachthere are Fischer (1922) index, Tornqvist (1936) index and Malmquist (1953) index.Malmquist is the most popular index method to assess TFPCH, which can be furtherdecomposed into, technological change (TECHCH), efficiency change (EFFCH), puretechnical efficiency change (PEFCH), scale efficiency change (SECH). Around 25 percentstudies from the reviewed literature estimated TFPCH using the Malmquist productivityindex (MPI) (Mukharjee et al., 2001; Sufian, 2011).

Each of the frontier techniques has its own merits and demerits. The parametricapproach allows the effect of random error in the model but it needs to specify theproduction function and which leads to subjectivity in the results. Non parametric DEA

Efficiency measure No. of studies

OTE 41CE 26PE 15

Source: Studies based on only empirical assessment of efficiency measures are included in this table

Table VI.Composition of efficiency

measures inreviewed studies

Figure 6.Efficiency measures

50%32%

18%

Overall Technical Efficiency OTE

Cost Efficiency

Profit Efficiency


203

approach is simple and easy to calculate as there is no need to specify any functional formsbut it does not take into account the effect of random error and environmental noise.

Existing literature urge in support of non parametric approach, DEA for assessmentof banking efficiency, while parametric approach SFA is widely applied for CE andbank branch efficiency (Berger and Humphrey, 1997). DEA is also a main technique toevaluate the OTE while SFA is frequently used in the reviewed studies to assess cost,profit and revenue efficiency. Table VII depicts the classification of the approachesused in the reviewed literature.

Figure 7 clearly shows the popularity of non parametric DEA for the assessment ofbanking efficiency. 75 percent of reviewing studies apply DEA to assess the efficiencyand productivity of the banking sector.

3.1.1.2 Scale of operation. Set of inputs-outputs are necessary to assess the level ofefficiency of a given firm or benchmarking of firms in a given set of industry. For thisfirst we need to define the scale of operation in an industry. If all firms are producing aconstant optimal level of output in an industry then it is said to be a constant return toscale (CRS). Charnes et al. (1978) developed a DEA model based on CRS, this modelassumes that all the decision-making units (DMUs) are performing on an optimal scaleof operations but it is very unpractical and in real scenario firms produce in variablereturn to scale (VRS) of economies. Then further Banker et al. (1984) extended the DEAmodel to the case of VRS. Therefore, in DEA method two different models are

Frontier approach No. of studies

DEA 66SFA 16DEA and SFA both 6Total 88

Source: Based on the empirical studies only, conceptual and theoretical studies are excluded fromcalculation

Table VII.Parametric and nonparametric approach

Figure 7.Classification of studieson the basis of frontierapproach

Studies classified as Parametric and Non Parametricapproaches

SFA18%

DEA75%

Both7%

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applicable for different scale of economies of an industry, one is CRS also known as aCCR model (Charnes et al., 1978) and another is VRS also known as a BCC model(Banker et al., 1984). The VRS model is widely applied in the literature as it is based ona more practical aspect of market conditions (Casu and Molyneux, 2003; Sathye, 2003;Gupta et al., 2008; Sufian, 2009). Some of the researchers comparatively analyzed boththe CRS and VRS approaches in their studies (Grigorian and Manole, 2002; Canhotoand Dermine, 2003; Luo, 2003).

3.1.1.3 Input-output orientation. Efficiency can be estimated from the two differentaspects one is output maximization while another is input minimization. These aspectsalso known as Input orientation and Output orientation. The researcher needs tospecify whether the problem is input oriented (input minimization, cost minimization) oroutput oriented (profit maximization, production maximization). Studies in the bankingindustry, in general prefer input orientation as any bank may have higher control of itsinputs (expenses) rather than outputs (income). Some studies provide findings for bothorientations; results are same under CRS but different under the VRS approach(Beccalli et al., 2006; Siriopoulos and Tziogkidis, 2010).

3.1.1.4 Approach for selection of input-output combination. Selection of input-outputcombination is a very complex issue, as any DMU may have n number of inputs andoutputs. From the literature we have identified following approaches for the selectionof variables:

. production approach;

. intermediation approach;

. value added approach;

. asset approach; and

. operating approach.

Production and intermediation approach are the main approaches used in the literature.The production approach assumes banks as a production unit, which produce loans,deposits and other financial services using inputs such as labor (no. of employees,personnel expenses) and capital (fixed assets and other assets). While the intermediationapproach perceives banks as an intermediary of financial services between borrower andsavers, it generates loans and other earning assets using deposits and labor (Sealey andLindley, 1977). The production approach treats deposits as an output variable whileintermediation approach perceives deposits as an input variable. The intermediationapproach is appropriate for banks efficiency as banks serve as an intermediary,production approach somewhat is more practical for bank branch efficiency (Berger andHumphrey, 1997). The asset approach is in line with the intermediation approach(Camanho and Dyson, 2005). Deposits and other liabilities along with labor and capital aretreated as inputs whereas output includes only bank assets generate revenue such as loansor investments (Ray and Das, 2010). Drake et al. (2006) proposed a value added approach(revenue approach) in DEA model. In this approach items which create value for the banktreated as output. Therefore, deposits and loans are treated as output in this approach.In addition the operating approach also known as income approach perceives bank as abusiness unit generating revenue (income) from total cost and expenses incurred. It takesinterest expenses and noninterest expenses as inputs, whereas interest income and


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noninterest income as output variables (Das and Ghosh, 2006). Table VIII presents thedistribution of various approaches for input-output selection under review studies.

It clearly depicts the intermediation approach as a widely applied approach inbanking sector followed by production approach mainly used in bank branches andfrontier techniques. Some of the studies applied mixed combination of production andintermediation approach resulting in profit approach, marketability approach (Luo, 2003).

Figure 8 diagrammatically shows the distribution of various approaches. Manyauthors applied more than one approach in their studies to show the variations in results.

Labor, capital, purchased funds and expenses or prices of labor and capital are mostcommon input variables found in reviewing studies, whereas loans and advances, otherearning assets and fee based income are common for output specification. Casu et al.(2004), Molyneux and Williams (2005) also included off-balance sheet items andsecurities as output variables. Sathye (2003) developed two different models on the basisof quantity and value, in quantity based model deposits and staff number taken as inputand net loans, noninterest income taken as output and in value based model interestexpense and noninterest expenses taken as input whereas interest income andnoninterest income as output variables. Some studies disaggregated deposits into retailand wholesale deposits (Drake and Hall, 2003), transaction deposits and non transactiondeposits (Mukharjee et al., 2001), and loans into commercial loans, industrial loans,consumer loans, real estate loans (Mukharjee et al., 2001). Debnath and Shankar (2008)

Approach No. of studies

Production approach 21Intermediation approach 56Value added approach 15Other approach 6

Note: Other includes asset approach, profit approach, operating approach, marketability approachand mixed approachesSource: Based on authors own calculation of reviewed studies given in Table AI

Table VIII.Approach forinput-output selection

Figure 8.Distribution of approachesfor input-outputcombination

Approaches for Input/output selection

Production22%Value added

15%

Intermediation57%

Other6%

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treated profit after tax (PAT) as an output variable. Deviating from the same length ofliterature Halkos and Salamouris (2004) introduced financial ratios as output variableswithout any input variable in the model. Table IX presents the set of input-outputvariables often used in literature and studies.

3.1.2 Determinants of efficiency and productivity. Several studies attempt two stageapproach to determine the influencing factors of efficiency and productivity change.In first stage researchers assess efficiency and productivity change and in the laterstage these efficiency scores and productivity change are regressed on set ofdetermining variables. These determining factors are categorized into three categories:

(1) bank specific variables;

(2) macro economic variables; and

(3) regulatory variables.

Most common bank specific factors are size, market share, banks profitability and capitaladequacy (Mukharjee et al., 2001; Sensarma, 2006; Das and Ghosh, 2006; Barros et al., 2007;Staub et al., 2010).

Economic conditions (GDP, GDP per capita, GDP growth rate, per capita income,inflation, inflation ratio, fiscal deficit) and stock market capitalization are commonmacroeconomic variables in the studies (Ataullah and Le, 2006; Jaffry et al., 2007;Sufian, 2009).

Regulatory specific variables are in general dummy variables in nature, such asownership structure, origin, age, financial reforms (Isik and Darrat, 2009; Sufian, 2009,2011). Table X shows the summary of determining variables of efficiency and productivity.

In this two stage approach, efficiency scores and productivity change scoresare regressed on the determining factors. These scores can be regressed usingTobit regression (Casu and Molyneux, 2003; Drake et al., 2006; Das and Ghosh, 2006),

Variables Description

InputLabor No. of full time employees, personnel expenses, provisions for

employeesCapital Fixed assets, liquid assets, total assets, equityPurchased funds Deposits, borrowingsExpenses Interest expenses, operational expenses, noninterest expenses,

other admin expensesOther No. of branches, no. of ATMsOutputDeposits Transaction deposits, non-transaction deposits, demand

deposits, fixed deposits, saving deposits,Loans and advances; investments,other earning assets

Commercial and industrial loans, consumer loans, real estateloans

Fee based income Interest income, non interest incomeOff balance sheet items Operating lease, securitized debtSecurities Equity, interbank loansPAT Profit after tax, operating profit


Table IX.Specification of

input/output variables


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Fixed and random effect panel data regression (Fiordelisi and Molyneux, 2010a, b;Staub et al., 2010; Bonin et al., 2005), ordinary least square (OLS) (Sufian et al., 2007;Ataullah and Le, 2006), generalized method of moments (GMM) (Casu and Girardone,2010), logit and probit regression (Barros et al., 2007; Isik and Darrat, 2009).

General linear regression is not at all appropriate regression technique in the secondstage efficiency analysis as the dependent variable (efficiency score) is a range variable(0 to 1) and it is censored variable. In logit and probit regression techniques dependentvariable (efficiency score) converted into a dummy group of most and least efficient,with 1 for the most efficient and 0 for the others. Most efficient DMU (any bank insample) scores 1 on efficiency frontier of the peer group. Table XI depicts the secondstage regression techniques applied in empirical studies.

Figure 9 diagrammatically shows the techniques used, and concludes panel dataregression and Tobit are widely applied techniques whereas few studies also appliedGMM, logit, probit and stochastic regression to assess the impact of determinants onefficiency and productivity change of the banking industry.

3.1.3 Impact of efficiency and productivity change. Several authors studied the impactof efficiency and productivity change on stock market return, shareholder value, mergersand acquisition.

3.1.3.1 Efficiency and shareholder wealth. Banks like other business units also seekto maximization of shareholder wealth and therefore Fiordelisi (2007) developed a newmeasure of bank performance, termed as shareholder value efficiency. A bank is said

Variables Description

Bank specific variablesBanks profitability Return on assets (ROA), return on equity (ROE), operating profit, business

per employees, net operating income, leverageMarket share Total depositsSize Total assetsLiquidity Total loans over total assetsAsset quality Loan loss provisions over total assetsCapital adequacy Capital adequacy ratio, book value of shareholder equity over total assetsRisk Total capital over total assets, equity over total assetsMacro economic variablesEconomic conditions GDP, GDP per capita, GDP growth rate, per capita income, inflation,

inflation ratio, fiscal deficitMarket capitalization Stock market capitalizationRegulatory variablesOwnership Dummy for public, private, and foreign ownershipSpecialization Specialization activity of banks (investment banking, corporate banking)Origin country Dummy for country specific originBranch region Dummy for region of branches (rural/urban)Age New/old bankTime Time trendFinancial reform/policyreform

Dummy for regulatory reforms in a particular year

Listed bank Listed in stock exchange


Table X.Variables fordeterminants of efficiencyand productivity

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to be shareholder value efficient when it produces maximum possible economic valueadded (EVA) using given level of input and outputs (Fiordelisi, 2007). This study was across country study of European countries and stochastic frontier technique used tocalculate shareholder value efficiency. After this few studies assessed the impact ofefficiency on shareholder wealth, regressing shareholder value as the dependentvariable with the components of efficiency and productivity (Fiordelisi and Molyneux,2007, 2010a, b). All of the above mentioned studies are conducted in Europeancountries. This is an emerging area in the research on efficiency and productivity of thebanking sector. To the best of our knowledge, no study on this aspect has been foundin developing countries so far.

3.1.3.2 Stock return and efficiency. Studies have investigated the impact ofefficiency and productivity on stock market performance and share market prices toassess the financial performance of banks in a more holistic way. Beccalli et al. (2006)investigated the relationship between percentage changes in the stock prices over thetime period with percentage changes in the CE. This study explored the association ofCE and stock prices in the banking industry of European countries. Sufian and Majid(2007) utilized the DEA window analysis method to investigate the relationshipbetween X-efficiency (technical efficiency) and share prices in Singapore bankingindustry. Majid et al. (2008) applied three stage model to examine the empirical impactof efficiency on share prices in China. However, Panel data regression and OLS isapplied in most of the studies.

3.1.3.3 Mergers and acquisition and efficiency. Efficiency and productivity changehas a significant impact on corporate events and decision-making under differentscenario of mergers, acquisition and bankruptcy. Krishnasamy et al. (2003) assessed

Figure 9.Second stage regression

techniques

Second stage Regression techniques for determinantsTobit25%

Panel27%

OLS15%

Other33%

Second stage regression techniques No. of studies

Tobit regression 10Panel data regression 11Simple regression 6Other 13

Note: Other includes GMM estimation, feasible generalized least square (FGLS), logit, probit, andstochastic regressionSource: Based on authors own calculation of reviewed studies given in Table AI

Table XI.Second stage regression

techniques


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the post mergers banks productivity of Malaysian banking industry using MPI andfound that eight out of total ten merged banks improved their productivity. WhileHalkos and Salamouris (2004) reported in their study on Greece banking that efficiencyincreases with the mergers and acquisition. Kaur and Kaur (2010) studied the Indianbanking sector and assessed the impact of mergers on CE and found that merger led toa higher level of cost efficiencies for the merging banks.

3.1.3.4 Bank branch efficiency. Apart from the efficiency of banking institutions as awhole, the literature also contributes towards the efficiency of bank branches. Theproduction approach in general is more applicable in bank branch efficiency as outputis measured in terms of the number of transactions (Zenios et al., 1999; Camanho andDyson, 1999). Camanho and Dyson (2005) applied production and value added modelto assess CE of 144 bank branches using DEA whereas Paradi et al. (2011) appliedproduction, intermediary and profitability model of DEA. Number of branches, thenumber of administrative and clerical staff and operating expenses are main inputvariables whereas the number of transactions, number of ATMs and number ofaccounts, deposits, loan are main output variables. Manandhar and Tang (2002)developed a framework for bank branch efficiency and benchmarking includinginternal service quality aspects of banking. Meepadung et al. (2009) focused oninformation technology (IT) based banking services and therefore consider employeeperceptions of service quality as an input variable. This study further appliedexploratory factor analysis on qualitative variables and then DEA to assess operatingand PE of bank branches.

4. Way to futureApplication of operational research and artificial intelligence techniques in the DEA andSFA are paving a new way to future direction of research. With the application of fuzzylogic, neural networking, Analytical hierarchical process (AHP) and bootstrapping,results of studies are providing holistic insights of banks performance and efficiency.Fuzzy and artificial intelligence techniques deal with the imprecise and ambiguous datain the DEA (Hatami-Marbini et al., 2011). Azadeh et al. (2011) integrated AHP with DEAto assess and optimize personnel productivity in large industrial bank of Iran. Thisintegrated approach of DEA and AHP is capable of dealing with both qualitative andquantitative data in banking. AHP is more purposeful in the case of qualitative datamodeling in banking efficiency and productivity. The bootstrapping procedure is widelyapplied in DEA research to overcome the dependency problem in DEA efficiency scoresand sensitivity analysis of second stage regression results (Casu and Molyneux, 2003;Tortosa-Ausina et al., 2008; Matthews and Zhang, 2010).

5. ConclusionEfficiency is an indicator of performance and frontier analysis techniques are recognizedas an important technique for performance measurement in banking and financialinstitutions. Country wise publication of studies depicts good number of studies indeveloped and developing nations but in emerging areas of shareholder value efficiencyand market return evidences, developing nations are lagging behind. Most of thecross-country studies are based on European countries. Around 80 percent reviewedstudies are empirical in nature conducted in different countries and across countries.

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In case of parametric and non parametric frontier analysis techniques, there is nosuch evidence in the literature on the superiority of any technique over another but stilla non parametric approach DEA is easy, simple and widely applied to measure theOTE of any financial institution. Parametric SFA is an econometric specificationtechnique, taking into account random error and noise of the environment and moresuitable for ambiguous data and measurement of cost, profit and revenue efficiency. Theproduction approach is not so far applied for bank efficiency whereas intermediationapproach, mixed approach, asset approach (similar to intermediation) and operatingapproach are more appropriate for selection of input-output. Tobit and panel dataregression techniques are most suitable for second stage regression of determinants ofefficiency and productivity change.

Bank branch efficiency, impact of efficiency of corporate events (mergers,acquisition and bankruptcy) is seeking the attention of researchers and policy makers.

The linkage between banking efficiency, productivity change and market returnis an emerging area. This linkage may provide a holistic picture of performance ofbanking industry considering whole banking system. Further research is needed forimplementation of artificial intelligence techniques to remove out ambiguity in data. Sothat managerial and regulatory decision-making on banking policy formulation andperformance metrics would strengthen the banking system.

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stfr

omth

eac

tual

cost

ish

igh

erfo

rth

ep

ub

lic

ban

ks

than

pri

vat

eb

ank

s5

Sat

hy

e(2

003)

(In

dia

)a

Em

pir

ical

Ap

pli

cati

onof

two

DE

A(i

np

ut-

outp

ut)

mod

els

onth

eb

asis

ofq

uan

tity

and

val

ue

6D

ebn

ath

and

Sh

ank

ar(2

008)

(In

dia

)a

Em

pir

ical

Am

ong

med

ium

size

db

ank

s,n

on

atio

nal

ized

ban

ks

are

effi

cien

t7

Sah

aan

dR

avis

ank

ar(2

000)

(In

dia

)a

Em

pir

ical

Pu

bli

cse

ctor

ban

ks

hav

eim

pro

ved

thei

ref

fici

ency

du

rin

gth

ep

erio

dof

stu

dy

8S

ahoo

etal.

(200

7)(I

nd

ia)

aE

mp

iric

alP

riv

ate

ban

ksh

owin

gh

igh

erC

Eth

ann

atio

nal

ized

ban

ks

9P

ark

and

Web

er(2

006)

(Kor

ea)

aE

mp

iric

alB

ank

ing

ind

ust

ryex

per

ien

ced

pro

du

ctiv

ity

gro

wth

du

eto

tech

nic

alp

rog

ress

10K

aoan

dL

iu(2

004)

(Tai

wan

)a

Em

pir

ical

Cal

cula

ted

effi

cien

cysc

ores

fall

sw

ith

inth

ep

red

icte

dra

ng

eof

per

form

ance

11R

ezv

ania

nan

dM

ehd

ian

(200

2)(S

ing

apor

e)a

Em

pir

ical

Eco

nom

ies

ofsc

ale

for

all

ban

ks

reg

ard

less

ofth

eir

size

12K

wan

(200

3)(A

sia)

aE

mp

iric

alA

fter

con

trol

lin

gfo

rlo

anq

ual

ity

,li

qu

idit

y,

cap

ital

izat

ion

,an

dou

tpu

tm

ixp

eru

nit

ban

k;o

per

atin

gco

sts

are

fou

nd

tov

ary

sig

nifi

can

tly

acro

ssA

sian

cou

ntr

ies

and

over

tim

e13

Lu

o(2

003)

(US

A)

aE

mp

iric

alL

arg

eb

ank

sac

qu

ire

rela

tiv

ely

low

erle

vel

ofm

ark

etab

ilit

yef

fici

ency

14B

ruce

Ho

and

Das

hW

u(2

009)

(Tai

wan

)a

Em

pir

ical

Ap

pli

cati

onof

DE

Aan

dp

rin

cip

alco

mp

onen

tan

aly

sis

toas

sess

onli

ne

ban

kin

gp

erfo

rman

ce15

Cas

uan

dG

irar

don

e(2

010)

(EU

)a

Em

pir

ical

Su

pp

orti

ng

evid

ence

ofco

nv

erg

ence

ofef

fici

ency

lev

els

tow

ard

san

EU

aver

age

16D

rak

ean

dH

all

(200

3)(J

apan

)a

Em

pir

ical

Con

trol

lin

gfo

rth

eex

ogen

ous

imp

act

ofp

rob

lem

loan

sis

imp

orta

nt

for

the

smal

ler

reg

ion

alb

ank

s17

Mee

pad

un

getal.

(200

9)(T

hai

lan

d)

aE

mp

iric

alIT

-bas

edtr

ansa

ctio

ns

atth

eb

ran

chle

vel

hav

ea

sig

nifi

can

tim

pac

ton

PE

18R

esti

(199

7)(I

taly

)a

Em

pir

ical

Hig

hv

aria

nce

inef

fici

ency

scor

esof

two

app

roac

hes

19P

asto

retal.

(199

7)(U

SA

and

Eu

rop

e)a

Em

pir

ical

Ev

iden

ces

ofsc

ale

inef

fici

enci

esfo

un

din

the

Au

stra

lian

,G

erm

anan

dU

Sb

ank

ing

syst

em

(continued

)

Table AI.Comprehensive snapshotof 106 reviewed studies

QRFM5,2

218

S.

no.

Au

thor

(yea

r)F

ocu

sar

eaM

eth

odol

ogy

Fin

din

gs

20U

sman

etal.

(201

0)(P

akis

tan

)a

Em

pir

ical

Th

esc

ale

inef

fici

ency

isth

em

ain

sou

rce

ofO

TE

21S

tau

betal.

(201

0)(B

razi

l)b

Em

pir

ical

Sta

te-o

wn

edb

ank

sar

esi

gn

ifica

ntl

ym

ore

cost

effi

cien

tth

anfo

reig

nan

dp

riv

ate

ban

k22

Mu

kh

arje

eetal.

(200

1)(U

SA

)b

Em

pir

ical

Lar

ger

asse

tsi

zean

dsp

ecia

liza

tion

ofp

rod

uct

mix

asso

ciat

ew

ith

hig

her

pro

du

ctiv

ity

gro

wth

wh

ile

hig

her

equ

ity

toas

sets

asso

ciat

ew

ith

low

erp

rod

uct

ivit

yg

row

th23

Bon

inetal.

(200

5)(E

uro

pe)

bE

mp

iric

alF

orei

gn

ban

ks

are

mor

eco

stef

fici

ent

than

its

cou

nte

rp

arts

24B

arro

setal.

(200

7)(E

uro

pe)

bE

mp

iric

alS

mal

ler

size

db

ank

sw

ith

hig

her

loan

-in

ten

sity

and

fore

ign

ban

ks

from

cou

ntr

ies

up

hol

din

gco

mm

onla

wtr

adit

ion

sh

ave

ah

igh

erp

rob

abil

ity

ofb

est

per

form

ance

25M

ille

ran

dN

oula

s(1

996)

(US

A)

bE

mp

iric

alL

arg

eran

dm

ore

pro

fita

ble

ban

ks

hav

eh

igh

erle

vel

sof

tech

nic

alef

fici

ency

26S

ath

ye

(200

2)(A

ust

rali

a)b

Em

pir

ical

No

asso

ciat

ion

was

fou

nd

bet

wee

nsi

zean

dp

rod

uct

ivit

y27

Cas

uan

dM

oly

neu

x(2

003)

(Eu

rop

e)b

Em

pir

ical

Effi

cien

cyd

iffe

ren

ces

det

erm

ined

by

cou

ntr

y-s

pec

ific

fact

ors

28N

akan

ean

dW

ein

trau

b(2

005)

(Bra

zil)

bE

mp

iric

alS

tate

-ow

ned

ban

ks

are

less

pro

du

ctiv

eth

anth

eir

pri

vat

ep

eers

,an

dth

atp

riv

atiz

atio

nh

asin

crea

sed

pro

du

ctiv

ity

29K

outs

oman

oli-

Fil

ipp

akietal.

(200

9)(E

uro

pe)

bE

mp

iric

alF

orei

gn

ban

ks

outp

erfo

rmin

term

sof

effi

cien

cyan

dp

rod

uct

ivit

yg

ain

s

30T

ann

a(2

009)

(75c

oun

try

)b

Em

pir

ical

FD

Ih

asa

neg

ativ

esh

ort

term

effe

ctb

ut

pos

itiv

elo

ng

term

effe

cton

TF

Pch

ang

e31

Loz

ano-

Viv

asan

dP

asio

ura

s(2

010)

(85c

oun

trie

s)b

Em

pir

ical

Incl

usi

onof

non

-tra

dit

ion

alou

tpu

tsd

oes

not

alte

rth

ed

irec

tion

alim

pac

tof

env

iron

men

tal

var

iab

les

onb

ank

inef

fici

ency

32S

ensa

rma

(200

6)(I

nd

ia)

bE

mp

iric

alF

orei

gn

ban

ks

hav

eb

een

the

wor

stp

erfo

rmer

asco

mp

ared

wit

hst

ate

own

edan

dp

riv

ate

ban

ks

33R

ayan

dD

as(2

010)

(In

dia

)b

Em

pir

ical

Ow

ner

ship

exp

lain

ing

the

effi

cien

cyd

iffe

ren

tial

ofb

ank

s34

Das

and

Gh

osh

(200

6)(I

nd

ia)

bE

mp

iric

alC

apit

alad

equ

acy

rati

osh

ows

pos

itiv

eas

soci

atio

nw

ith

effi

cien

cy35

Ku

mar

and

Gu

lati

(201

0)(I

nd

ia)

bE

mp

iric

alS

tron

gp

rese

nce

ofsi

gm

aan

db

eta

con

ver

gen

cein

CE

lev

els

ofp

ub

lic

sect

or36

Gu

pta

etal.

(200

8)(I

nd

ia)

bE

mp

iric

alC

apit

alad

equ

acy

rati

oan

dp

rofi

tab

ilit

ysh

owp

osit

ive

rela

tion

wit

hef

fici

ency

(continued

)

Table AI.


219

S.

no.

Au

thor

(yea

r)F

ocu

sar

eaM

eth

odol

ogy

Fin

din

gs

37S

han

mu

gam

and

Das

(200

4)(I

nd

ia)

bE

mp

iric

alS

tate

ban

kg

rou

pan

dfo

reig

nb

ank

sar

em

ore

effi

cien

tth

anth

eir

cou

nte

rpar

ts38

Das

etal.

(200

4)(I

nd

ia)

bE

mp

iric

alB

ank

size

,ow

ner

ship

and

bei

ng

list

edon

stoc

kex

chan

ge

hav

ep

osit

ive

imp

act

onP

E39

San

yal

and

Sh

ank

ar(2

011)

(In

dia

)b

Em

pir

ical

Com

pet

itio

nh

asa

pos

itiv

eim

pac

ton

pro

du

ctiv

ity

for

the

old

Ind

ian

pri

vat

eb

ank

s40

Gal

agad

era

and

Ed

iris

uir

ya

(200

4)(I

nd

ia)

bE

mp

iric

alS

mal

ler

ban

ks

are

less

effi

cien

tan

dh

igh

lyD

EA

-effi

cien

tb

ank

sh

ave

ah

igh

equ

ity

toas

sets

and

hig

hre

turn

toav

erag

eeq

uit

yra

tios

41Ja

ffry

etal.

(200

7)(I

nd

ia,

Pak

ista

nan

dB

ang

lad

esh

)b

Em

pir

ical

Ind

iaan

dB

ang

lad

esh

exp

erie

nce

dim

med

iate

and

sust

ain

edg

row

thin

tech

nic

alef

fici

ency

,wh

erea

sP

akis

tan

end

ure

da

red

uct

ion

inef

fici

ency

du

rin

gst

ud

yp

erio

d42

Moh

an(2

005)

(In

dia

)b

Des

crip

tiv

eE

ffici

ency

and

tech

nol

ogy

chan

ge

isco

nsi

sten

tin

Ind

ian

ban

kin

gse

ctor

43A

tau

llah

etal.

(200

4)(I

nd

iaan

dP

akis

tan

)b

Em

pir

ical

Ban

ks

are

effi

cien

tin

gen

erat

ing

earn

ing

asse

td

ue

toh

igh

non

per

form

ing

loan

s44

Su

fian

(200

9)(M

alay

sia)

bE

mp

iric

alE

ffici

ency

isp

osit

ivel

yas

soci

ated

wit

hex

pen

sep

refe

ren

ceb

ehav

ior

and

econ

omic

con

dit

ion

wh

erea

sn

egat

ivel

yas

soci

ated

wit

hlo

anin

ten

sity

ofb

ank

s45

Su

fian

(201

0)(M

alay

sia)

bE

mp

iric

alP

rod

uct

ivit

yis

pos

itiv

ely

asso

ciat

edw

ith

bei

ng

list

edw

her

eas

neg

ativ

ew

ith

fore

ign

own

ersh

ip,

and

also

fore

ign

ban

ks

dep

icts

ap

rod

uct

ivit

yre

gre

ssd

uri

ng

stu

dy

per

iod

46M

atth

ews

and

Zh

ang

(201

0)(C

hin

a)b

Em

pir

ical

Boo

tstr

app

ing

and

MP

Iis

app

lied

tost

ud

yT

FP

chan

ge

inth

eb

ank

ing

sect

orof

Ch

ina

47D

rak

eetal.

(200

6)(H

ong

Kon

g)

bE

mp

iric

alD

iffe

ren

tial

imp

acts

ofen

vir

onm

enta

lfa

ctor

son

dif

fere

nt

size

gro

up

san

dfi

nan

cial

sect

ors

48C

anh

oto

and

Der

min

e(2

003)

(Eu

rop

e)b

Em

pir

ical

Th

en

ewb

ank

sd

omin

ate

the

old

ban

ks

inte

rms

ofav

erag

eef

fici

ency

scor

es49

Ber

ger

etal.

(200

9)(C

hin

a)b

Em

pir

ical

Big

fou

rb

ank

sar

eth

ele

ast

effi

cien

tw

her

eas

fore

ign

ban

ks

are

the

mos

tef

fici

ent

50A

l-M

uh

arra

mi

(200

7A

rab

)(G

CC

)b

Em

pir

ical

Dow

nw

ard

tren

din

pro

du

ctiv

ity

du

rin

gth

est

ud

yp

erio

d51

Ros

sietal.

(200

5)(E

uro

pe)

bE

mp

iric

alN

egat

ive

corr

elat

ion

bet

wee

np

rob

lem

loan

and

effi

cien

cy52

Ak

hig

be

and

McN

ult

y(2

003)

(US

A)

bE

mp

iric

alS

mal

lb

ank

sar

em

ore

pro

fit

effi

cien

tth

anla

rge

ban

ks

(continued

)

Table AI.

QRFM5,2

220

S.

no.

Au

thor

(yea

r)F

ocu

sar

eaM

eth

odol

ogy

Fin

din

gs

53A

hm

ad(2

006)

(Pak

ista

n)

bE

mp

iric

alN

on-p

erfo

rmin

glo

ans

and

oth

erri

skfa

ctor

sp

ut

ad

own

war

dp

ress

ure

onb

ank

ing

effi

cien

cy54

Isik

and

Dar

rat

(200

9)(T

urk

ey)

bE

mp

iric

alA

pp

lica

tion

ofp

rob

itre

gre

ssio

nan

aly

sis

55S

irio

pou

los

and

Tzi

ogk

idis

(201

0)(G

reec

e)b

Em

pir

ical

Per

form

ance

beh

avio

rof

ban

kin

gin

stit

uti

ons

wit

hin

the

scop

eof

chan

ge

man

agem

ent

theo

ry56

Hav

rylc

hy

k(2

006)

(Pol

and

)b

Em

pir

ical

For

eig

nb

ank

sn

otsu

ccee

ded

inen

han

cin

gth

eir

effi

cien

cy57

Gri

gor

ian

and

Man

ole

(200

2)(E

uro

pe)

bE

mp

iric

alF

orei

gn

own

ersh

ipw

ith

con

trol

lin

gp

ower

enh

ance

sef

fici

ency

58Z

hao

etal.

(200

6)(I

nd

ia)

cE

mp

iric

alB

ank

sow

ner

ship

stru

ctu

reim

pac

tb

ank

sef

fici

ency

bu

td

oes

not

affe

ctT

FP

chan

ge

59A

tau

llah

and

Le

(200

6)(I

nd

ia)

cE

mp

iric

alC

omp

etit

ion

and

ban

kef

fici

ency

isp

osit

ivel

yas

soci

ated

wh

ile

pre

sen

ceof

fore

ign

ban

kn

egat

ivel

yaf

fect

sb

Sharma Et Al 2013

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Transcript of Sharma Et Al 2013