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www.ajbms.org Asian Journal of Business and Management Sciences

ISSN: 2047-2528 Vol. 1 No. 2 [150-156]


Muhammad Farhan Akhtar

Hailey College of Commerce,

University of The Punjab, Lahore, Pakistan

E-mail: vjfarhan@yahoo.com

Khizer Ali Hailey College of Commerce, University of The Punjab, Lahore, Pakistan E-mail: vjkhizer@yahoo.com Shama Sadaqat Hailey College of Commerce, University of The Punjab, Lahore, Pakistan E-mail: shama_sadaqat@yahoo.com ABSTRACT The purpose of this research is to measure the performance for the commercial banks of Pakistan during the period 2006-2009 using data envelopment analysis (DEA). DEA, primarily, takes into account the input and output gears of a decision-making unit (DMU) to calculate their performance between 0 and

1. The estimated result shows that 6 banks are relatively efficient when their efficiency is measured in terms of ‘constant return to scale’ and 8 banks are relatively efficient when their efficiency is measured in terms of ‘variable return to scale’. By improved handling of operating expenses, advances, capital and by boosting banking investment operations, the less efficient banks can successfully endorse resource utilization efficiency. These results are valuable contribution for both managers and researchers.

Key Words: Data Envelopment Analysis, Performance, Efficiency, Banks, Pakistan

1.0 INTRODUCTION Performance is generally conceptualized as bank’s ability to generate transaction by effectively utilizing its resources. Economically the efficiency refers to the ratio of outputs to inputs. Inputs which refers to the scarce resource and outputs in terms of goods and services offered to the consumers. The notion of efficiency in terms of banking operation is more consequential as banking sector is deemed to have significant role in the financial division of a country.

Banking sector of a country is one of the most vital sectors of the country’s economy. The importance of measuring its performance has been in debates for a long time. Primarily, the performance is measured through an analysis using different accounting ratios like; return on assets (ROA), return on investments (ROI), return on equity (ROE), equity to assets (ETA) and internal growth of equity, etc. Although these ratios are still used as performance measures and thought to be partial productivity measure, data envelopment analysis (DEA) can be considered a tool for measuring total productivity (Ramanathan, 2007).

©Society for Business Research Promotion

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The large number of commercial banks in the Pakistan, the high branch density, the quick technological change and amplified competition has added marvellous pressure to improve its performance. The lack of statistical evidence on the performance of the banking industry in Pakistan in terms of data envelopment analysis (DEA) has been a main cause to demeanour this study in this part of the world. To empirically approximate the efficiency of commercial banks, this study will use a nonparametric linear programming (LP) method, data envelopment analysis (DEA). This system has the benefit of developing a data-driven technological frontier that necessitates no specification of any scrupulous functional shape or error structure. This study fills a vacuum in the literature by leaving from the traditional examination of efficiency.

1.1 Conceptual framework

The objective of this study is to validate the implication of data envelopment analysis (DEA) in investigating the efficiency of commercial banks of Pakistan. DEA was first developed by Charnes et al. (1978) as a mathematical programming procedure for evaluating the relative efficiencies of multiple decision-making units (DMUs). It was constructed on the theoretical framework presented by Farrell (1957). Data envelopment analysis is a nonparametric technique which offers a comparative ratio for each unit in terms of output and input. The ratio is stated as efficiency score for each unit.

The efficiency score is usually represented as a number between zero (0) to one (1) or 100%.

A unit whose score is less than 1 is usually believed to be inefficient as compared to other units. One of the significant roles of data envelopment analysis (DEA) is that the efficiency scores highlight the gap for potential improvements and developments for inefficient DMUs (Decision making units). Moreover another advantage of DEA is that this technique does not have any rigid or predetermined structure in defining the efficient units (Banker, 1984; AlFaraj et al., 1993; Burley, 1995; Mester, 1996).

1.2 Objectives

The objectives for this study are:

 To measure and compare the efficiency of commercial banks of Pakistan, using BCC model and CCR model of Data Envelopment Analysis (DEA) for the reason of classifying the banks as efficient and less efficient banks.

 To suggest the suitable measures to advance the efficiency of the commercial banks.


The ratio analysis is longstanding technique to evaluate the performance of the banks. The financial statements are examined to find different ratios and then compare them with the benchmark. The parametric techniques are usually used by researchers to investigate the performance of the banks. The mathematical programing techniques used to measure the efficiency of the organizations. The empirical evidence shows the two non-parametric tests Malmquist Productivity Indices (MPI) and Data Envelopment Analysis (DEA) in previous studies.

Seiford and Zhu (1999) examined that the profitability and marketability of top 55 U.S.

commercial banks by applying data envelopment analysis (DEA) model.The process of data analysis was based on two stages. On the basis of this study it was concluded that large banks performed better with respect to profitability than small size banks, while small size banks have better characteristic of marketability as compare to large size banks.

Maudos et al.( 2002) described the cost and profit efficiency of 832 European banks of ten European Union Countries. Their study covered a period of 1993 to 1996. The return on assets (ROE) and return on equity (ROA) were acquired as a performance measures to check profit efficiency of banks under data envelopment analysis (DEA).the four dimensions market characteristics, differences in size, other bank characteristics and specialization.

Variations in profit terms were found to be greater than the variations in cost terms

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Park and Weber (2006) studies the profitability of all Korean banks by testing with (traditional hypothesis approach) market structure hypothesis against efficient structure hypothesis applied after examination of the panel data (for the period of 1992-2002); with the help of data envelopment analysis (DEA) model. The study found performance measures to have significant affect on banks profitability.

Pastor, Lovell, and Tulkens (2006) calculated financial performance of branch offices. They studied 573 branch offices for six month accounting period of large European savings bank.

Data envelopment analysis (DEA) and Free disposal hull (FDH) programming mathematical methods were applied to estimate financial performance with respect to their safeguard against expenses in giving customer services and building customer bases. They found financial performance evaluation factors can be reduced without statistical loss of significant information to bank management.

Sufian (2009) studied the efficiency of Malaysian banking sector during Asian crisis of 1997 for the period of 1995-1999. Efficiency of individual banks was evaluated by Data Envelopment Analysis (DEA) technique. Profitability was the major ingredient which was used to measure the efficiency with other explanatory variables, like bank size and ownership. Efficiency of banks was found to be positively related to loans intensity and was found to be negatively related with economic conditions and expense preference behavior.

Nigmonov (2010) aims to study the banks performance and efficiency in Uzbekistan for the period of 2004-2006. The basic two DEA models were applied to analyse the data under the assumption of constant and variable return to scale. The results have revealed that the inefficiency occurs due to technical efficiency and overall banks average efficiency level decreased.

The development in the efficiency of the Thailand banking sector covered the duration 1999-2008 (Sufian & Habibullah, 2010). The data envelopment analysis (DEA) approach used to measure the technical efficiency of individual banks. The results have shown that inefficiency offset during formulation of technical efficiency with respect to pure technical efficiency in banking sector.

The efficiency level of banks in data envelopment analysis is measured using ratio of weight sum of outputs to weighted sum of inputs. The empirical evidence considered the different inputs and outputs to measure the efficiency of banking sector. The inputs were used by previous studies like fixed assets, total funds, interest & non-interest expenses, total deposits, labour, capital, and total loans, whereas the outputs were used Total loans, other earning assets, off-balance sheet items, revenue, profit, interest income, non-interest income and investments (Hassan, Mohamad, & Bader, 2009; AlKhathlan & Malik, 2010;

Sufian, 2009; Singh, Singh, & Munisamy, 2008; Pastor, Lovell, & Tulkens, 2006; Chen & Yeh, 1998; Sarkis & Talluri, 2002) Koutsomanoli-Filippaki, Margaritis, & Staikouras (2009); Sufian (2007); Choi, Stefanou, and Stokes (2007) observed significant variation and diverse prototypes in inefficiency levels across banking systems. Additionally the study found small and home private banks emerge to be the utmost efficient.


3.1 Data All the data required for this study have been taken from the annual reports of the banks for the financial year 2006-2009. Banxia Frontier Analyst has been used in the analysis of the data.

3.2 Selection of inputs and outputs Reviewing the literature on the application of data envelopment analysis (DEA), different studies have used different combination of inputs and outputs. This study has used operating expense, advances and capital as input and operating income and net-interest

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income as outputs for this study. The selection of the inputs and outputs has been supported by literatures (Chen & Yeh, 1998; Sarkis & Talluri, 2002; Mukherjee, et al., 2002; Ong, et al., 2003; Pastor, et al., 2006; Singh, et al., 2008; Hassan, et al., 2009;

Sufian, 2009; AlKhathlan & Malik, 2010).

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Figure 3.1 Inputs and Outputs for Data Envelopment Analysis (DEA)

3.3 CCR and BCC Model The original CCR model was pertinent only to that expertise which is categorized by constant returns to scale. The major advancement was extended by Charnes, and Cooper (BCC) model to facilitate expertise that reveals variable returns to scale. This study has used input-oriented DEA model, which emphasized on the minimization of inputs and the

outputs are held at their current levels:


4.1 Input-Oriented Technical Efficiency (Constant Return to Scale) The table 4.1 reported the DEA efficiency score based on Constant return to scale under the CCR Model. The Allied Bank Limited, MCB Bank Limited and Habib Metropolitan Bank Limited are the three banks which show the consistency at their efficiency scores i.e. 1 for each year from 2006-2009. It is observed that there is a decreasing trend in their mean of technical efficiency of commercial banks of Pakistan from 2006 to 2009, the average score decreases form 92% (FY-2006) to 90% (FY-2009).

Generally, it means that these banks were more inefficient in FY-2006 as compare to FYThe performance of the banks which score 75% (0.75) or less considered to be less efficient.

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4.2 Input-Oriented Pure Technical Efficiency (Variable Return to Scale) The data envelopment analysis BCC Model used to account for constant return to scale (CRS) to variable return to scale (VRS) to analyse the pure technical efficiency of the Commercial banks of the Pakistan for the period of 2006-2009 reported in table 4.2. The average score of pure technical efficiency of commercial banks of Pakistan varies from 0.97 (FY-2006) to 0.94 (FY-2009) during 2006-2009 exhibits the decreasing trend in their efficiency level at variable return to scale.

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In BCC Model there is an increase in number of banks which shows the consistency in their performance for each year with score 1, these banks included Allied bank limited, Bank AlHabib Limited, Habib Bank Limited, MCB bank limited, Habib Metropolitan bank limited and Standard Chartered Bank (Pakistan) limited. Instead of this the United Bank Limited have a large volume of operations and resources in Pakistan did not consistent in its performance which can be observed from less efficient score (0.99) for year 2009. This

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transition can be observed with the fact that, shaking economic conditions has been observed during this period.

4.3 Input-Oriented Scale Efficiency Table 4.3 shows the mean efficiency each year by decomposing technical efficiency into pure technical efficiency and scale efficiency. Decomposing technical efficiency into pure technical efficiency and scale efficiency allows us to gain insight into the main sources of inefficiencies. The average index of technical efficiency during the study period varies in between 89.89% to 92.07%, of pure technical efficiency varying at 94.77% to 97.14%, and of scale efficiency varying at 94.83% to 94.77%.

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