A Comparative Study of Private and Public Banks Efficiency Using CCR Model through DEA Approach

A Comparative Study of Private and Public Banks Efficiency Using CCR Model through DEA Approach Dr.R.P.Sreedevi Professor of Mathematics, St.Peter s Engineering College, Hyderabad, India Email: rpsreedevi.abhi@gmail.com Abstract: The aim of this paper is to study the Comparative Efficiency between public and private banks through Data Envelopment analysis. Data Envelopment analysis is a Nonparametric approach to measure the effectiveness of multiple inputs and outputs with no underlying assumption of the functional form. This paper presents the effectiveness of private and public banks with 7 inputs and 6 outputs Using Constant returns to scale i.e CCR Model. The research outcomes present preferred indications about the selection of inputs and outputs for DEA application, technical efficiency and comparative study of public and private banks in Hyderabad. DEA Efficiency evaluation method identifies the functions and it improves the economy and brings improvement in the banking system. Key Words: DEA, DMUS, CCR Model, Constant Returns to Scale, Technical Efficiency Introduction: The exact date of existence of indigenous bank is not known. But, it is certain that the old banking system has been functioning for centuries. Some people trace the presence of indigenous banks to the Vedic times of 2000-1400 BC. It has admirably fulfilled the needs of the country in the past.however, with the coming of the British, its decline started. Despite the fast growth of modern commercial banks, however, the indigenous banks continue to hold a prominent position in the Indian money market even in the present times. India's public sector banks (PSBs) are compared unfavorably with their private sector counterparts, domestic and foreign. This comparison rests, for the most part, on financial measures of performance, and such a comparison provides much of the rationale for privatization of PSBs. An efficient management of the banking operations aimed at ensuring growth in profits and efficiency requires up-to dated knowledge of all those factors on which bank efficiency depends. This is only possible through research. A lot of research work has so far taken place. Some studies have been undertaken for measuring the productivity and operational efficiency of banks. Pal, Mukherji and Nath studied the efficiency of 68 major Indian commercial banks for the year 1999. They took 27 public sector banks, 20 private sector and 21 foreign sector banks for their study. This study is based on five input variable( Net worth, borrowings, operating expense, number of employees and number of branches ) and five output variables ( deposits, net profits, advances, non-interest income and spread ). Giradone, et al.(2004) investigated the main determinants of Italian banks cost efficiency over the period of 1993-1996. Bonin, et.al (2005) investigated the impact of privatization on bank efficiency. Foster and Shaffer (2005) have tried to establish relationship between efficiency and size of the market. Nageshwara rao and Shefali tiwari (2009) conducted a study on public sector banks to find the factors affecting efficiency of the public sector banks. Page No:1044

Database and Methodology: Among the public and private sector banks top five banks selected on the basis of net sales for the year 2010-13.Banking sector is one of the fastest growing sectors in India. It accounts for around 14.68% of the aggregate net sales of the top 500 companies in 2010-11. To days banking sector becoming more complex. Evaluating the factors influencing the efficiency of the banks is not an easy task. There are so many factors which need to be considered. In this paper, we have chosen twenty three ratios related to Capital Adequacy, Asset Quality. Management Efficiency, Earnings and Profitability, Liquidity to know the factors affecting efficiency of public and private sector banks employing Pearson s coefficient of correlation. The variables considered for the study are divided into independent and dependent variables. Independent Variables: The Independent variables are Customers deposits, Balance credit, Capital, Interest, Commission, Staff Expenditure and Net administrative expenditure Dependent Variables: Dependent variables are Loans, Securities, Income from interest, and Income from commission, Profit and Total margin of interest 1. Efficiency Factors Influencing Capital Adequacy: It is important for a bank to maintain depositors confidence and preventing the bank from going bankrupt. It reflects the overall financial condition of banks and also the ability of management to meet the need of additional capital. It also indicates whether the bank has enough capital to absorb unexpected losses. Capital adequacy ratios act as indicators of bank leverage. The following ratios measure capital adequacy Capital Adequacy Ratio (CAR) Debt-Equity Ratio (D/E) Total Advances to Total Assets (ADV/AST) G-Secs to Total Investments (G-Sec/Inv) 2. Efficiency Factors Influencing Assets Quality: The quality of assets in an important parameter to gauge the strength of bank. The prime motto behind measuring the assets quality is to ascertain the component of non-performing assets as a percentage of the total assets. This indicates what types of advances the bank has made to generate interest income. The ratios necessary to assess the assets quality are: Total Net NPAs to Total Assets (NNPAs/TA) Net NPAs to Net Advances(NNPAs/NA) Total Assets(TI/TA) Percentage Change in Net NPAs 3. Factors Influencing Management Efficiency: Management efficiency is another important element of the CAMEL Model. The ratio in this segment involves subjective analysis to measure the efficiency and effectiveness of management. The management of bank takes crucial decisions depending on its risk perception. It sets vision and goals for the organization and sees that it achieves them. This parameter is used to evaluate management efficiency as to assign premium to better quality banks and discount poorly managed ones. The ratios used to evaluate management efficiency are: Total Advances to Total Deposits (TA/TD) Profit per Employee (PPE) Business per Employee (BPE) Return on Net worth (RONW) 4. Efficiency Factors Influencing Earnings Quality: The quality of earnings is a very important criterion that determines the ability of a bank to earn consistently. It basically determines the profitability of bank and explains its sustainability and growth in earnings in future. Page No:1045

This parameter gains importance in the light of argument that much of a bank s income is earned through non-core activities like investments, treasury operations and corporate advisory services and so on. The following ratios explain the quality of income generation. Operating profit by Average Working Funds (OP/AWF) Percentage Growth in Net Profit(PAT Growth) Net Profit/Average assets(pat/aa) 5. Efficiency Factors Influencing Liquidity: Risk of liquidity is curse to the image of bank. Bank has to take a proper care to hedge the liquidity risk; at the same time ensuring good percentage of funds are invested in high return generating securities, so that it is in a position to generate profit with provision liquidity to the depositors. The following ratios are used to measure the liquidity under the CAMEL Model. Liquid Assets Deposits (LA/ DD) Liquid Assets to Total Deposits (LA/TD). Liquid Assets to Total Assets (LA/TA) G-Sec to Total Assets (G-Secs/TA) Approved Securities/Total Assets(AS/TA) The data relating to these variables have been collected from various sources such as annual reports of the banks, PROWESS, Ace Analyzer, Analyst journal for a period of five years (2006-2010). Internet has been an important source of secondary data. CCR MODEL To allow for applications to a wide variety of activities, we use the term Decision Making Unit (DMU) to refer to any entity that is to be evaluated in terms of its abilities to convert inputs into outputs. The evaluation can also be directed to educational institutions and hospitals as well as police forces or army units for which comparative evaluations of their performance one to be made.we assume that there are n DMUS to be evaluated; each DMU consumes varying amounts of m different inputs to produce s different outputs. Specifically, DMUJ consumes amount Xij of input i and produces amount Yrj of output r. We assume that Xij 0 and Yrj 0 and for other assume that each DMU as at least one positive input and one positive output value.we now term to the ratio form of DEA. In this form, as introduced by Charnes Cooper and Rhodes, the ratio of outputs to inputs is used to measure the relative efficiency of the DMUj = DMU0 to be evaluated relative to the ratios of all of the j = 1,2,3..n DMUj.we can interpret to the CCR construction as the reduction of the multiple output/multiple input situation to that of a single virtual output and virtual input. For a particular DMU the ratio of this single virtual output to single virtual input provides a measure of efficiency that is a function of the multipliers. In mathematical programming parlance, this ratio which is to be maximize forms the objective function per the particular DMU being evaluated, so that symbolically. Page No:1046

s Z (CCR) = Max Z = µ r Y ro r =1 Subject to s m µ r Y rj - V j X ij 0. (1.1) r =1 i=1 m V i X io = 1 i =1 µ r, v i 0 Empirical Investigation for Private Banks: The private banks are homogeneous with respect to their organizational structure, goals and objectives. However, they differ quite significantly in size and production level. Therefore, the scale of banks plays a vital role in their relative efficiency. The Efficiency results of 10 private banks using the BCC Model and Linear Programming model has been formulated for the year 2010, 2011, 2012 and 2013. The main objective of the present study is to assess the performance of 10 Private Sector banks in India. The study, moreover, intends to assess the efficiency of Private Sector banks thereby identifying their influence in improving the Indian economy. By feeding the inputs and outputs, we generate the efficiency score of 10 private banks chronologically. The efficiency score (calculated annually over 2010 to 2013) has been indicated in the bar graph above. For example, from the above bar graph we can clearly see that DMU8 is utilizing its inputs to the fullest potential year-by-year and hence, we can consider it to be the benchmark or a standardization scale for other DMU s in the study. Furthermore, we observe that DMU6 after facing an initial roadblock in 2010 has consistently maintained its score at 1, which bodes well for that particular DMU. Conversely, if we look at the case of DMU2, we observe that its efficiency scores after being at a steady score of 1 has noticed a drop in 2013, which suggests that the inputs are not translating into potential outputs. Hence, there is a decrease in the efficiency. The other DMU s are utilizing only 80%-90% of the inputs that are being fed to them, which goes on to suggest that there is a considerable leakage in the output. Hence, we can safely state that if the DMU s utilize their inputs to the maximum, not only will it help in attaining a theoretical efficiency of 1 but it will ensure that with every passing year the bank sees a steady growth in its turnover rate. Page No:1047

Efficiency Score CCR model: 1.2 1 0.8 0.6 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 2010 0.869 1 1 0.88 1 0.955 0.983 1 0.872 0.843 1 0.986 1 1 2011 0.872 0.973 1 0.785 0.933 1 1 1 0.952 0.79 1 0.857 1 1 2012 0.97 1 1 0.815 1 1 0.991 1 0.965 0.812 1 0.999 0.987 1 2013 1 0.983 1 0.89 0.996 1 0.985 1 0.919 0.927 1 1 0.988 1 By feeding the inputs and outputs, we generate the efficiency score of 14 private banks and 14 public banks chronologically. The efficiency score (calculated annually over 2010 to 2013) has been indicated in the bar graph above. For example, from the above bar graph we can clearly see that DMU8 is utilizing its inputs to the fullest potential year-by-year and hence, we can consider it to be the benchmark or a standardization scale for other DMU s in the study. Furthermore, we observe that DMU6 after facing an initial roadblock in 2010 has consistently maintained its score at 1, which bodes well for that particular DMU. Conversely, if we look at the case of DMU2, we observe that its efficiency scores after being at a steady score of 1 has noticed a drop-in 2013, which suggests that the inputs are not translating into potential outputs. Hence, there is a decrease in the efficiency. The other DMU s are utilizing only 80%-90% of the inputs that are being fed to them, which goes on to suggest that there is a considerable leakage in the output. Hence, we can safely state that if the DMU s utilize their inputs to the maximum, not only will it help in attaining a theoretical efficiency of 1 but it will also have far-reaching practical impacts within the society. Page No:1049

For example, the credibility of the bank and its stature within its customers will grow with an increase in efficiency, and it will ensure that with every passing year the bank sees a steady growth in its turnover rate. Conclusion : Mathematical models that were put forth and applied during the course of this paper clearly indicate how important a role they play in the operational analysis of the banking sector of our country at a specific period of time. This study attempts to investigate the technical efficiency of Indian private banks during the period of 2010-2013. In this research it is found that 60% of the banks are utilizing the 90% inputs produced whereas an amount of 10% is freely disposed. In this research, the number of branches has not been considered, and this is a very important indicator when it comes to the operation performance of a bank. This is so because if there are also branches in rural area then it suggest more profit making for that particular bank because of its high density across the country. The success for the banks represents a constant influx of new clients, so the management has a job to devote itself to the improvement of the operation of banks in order to gain more money and thus become a superefficient unit. References : [1] Adamauskas, S.; Krusinskas, R. 2012. Behavioural finance efficiency under the influence of country s economic cycle, Inzinerine ekonomika Engineering Economics23 (4): 327 337. [2] Association of Commercial Banks of Latvia (ACBL). 2014. The Banking Sector in Figures [online], [cited 10 March 2014]. The Association of Commercial Banks of Latvia. Available from Internet: http://www.bankasoc.lv/en/statistics/. [3] Banker, R.D. And Natarajan, R.(2008). Evaluating Contextual Variables Affecting Productivity Using Data Envelopment Analysis. [4] Banker, R.D. and Thrall, R.M (1992).Estimating Most Productive Scale Size Using Data Envelopment Analysis. European Journal of Operational Research, vol. 62, pp.74-84. [4]. [5] Canhoto. A. and Dermine, J. (2003), A note on banking efficiency in Portugal, new vs. old Banks, Journal of Banking and Finance, Vol. 27 No. 11, pp. 2087-98. Page No:1050

[6] Casu, B. (2002), A comparative study of the cost efficiency of Italian bank conglomerates, Managerial Finance, Vol. 28 No. 2, pp. 3-23. [7] Casu, B. and Molyneux, P. (2003), A comparative study of efficiency in Europeanbanking, Applied Economics, Vol. 35 No. 17, pp. 1865-76. [8] Drake, L. (2001). Efficiency in UK Banking. Applied Financial Economics, Vol. 11, pp. 557 571. [9] Edey, M. And Gray, B. (1996), The Evolving Structure of the Australian Financial System, Conference on the Future of the Financial System, 8-9 July, Reserve Bank of Australia, Sydney. [10] Fiordelisi, F. (2007). Shareholder value efficiency in European banking. Journal of Banking and Finance, Vol. 31 (7), pp. 2151 2171. [11] Howland, M., Rowse, J. (2006): Measuring Bank Branch Efficiency Using Data Envelopment Analysis: Managerial and Implementation Issues, INFOR -Information systems and operational research, vol. 44(1), Ottawa, pp. 49-64. [12] Manoj P.K., Financial Soundness of Old Private Sector Banks (OPBs) in India and Benchmarking the Kerala Based OPBs: A CAMEL Approach, American Journal of Scientific Research, 11 (2010). [13] Shahchera Mahshid and Nasim Jouzdani, The Impact of Regulation on Soundness Banking, International Conference on Business and Economics Research Publications, 1, (2011). [14] Tawarneh, M. (2006), A Comparison of Financial Performance in the Banking Sector: Some Evidence from Omani Commercial Banks, International Research Journal of Finance and Economics, 3, pp.1-12. [15] Valentine, T.J. and Ford, G. (2001), Bank Mergers in the Australian Financial System Should the Pillars be Pulled Down?, Economic Papers, 20, pp.36-53. [16] Ms.S.Sujatha, Ms.K.Santhana Lakshmi, Mr.Martin Selvakumar.M and Dr.N.Santhosh Kumar, Effectiveness of Training and Development Among Employees In Private Banks Page No:1051

(with Reference To Chennai City) International Journal of Management (IJM), Volume 4, Issue 2, 2013, pp. 118-124 [17] Dr.V.Sarangarajan, Mr.S.Karthik, Dr.S.A.Lourthu raj and Dr.R.Ramesh, Interfirm Comparison On Select Private Banking Companies In India International Journal of Management (IJM), Volume 4, Issue 2, 2013, pp. 138-146 Page No:1052