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Application of data envelopment analysis for measuring financial efficiency of district central cooperative banks
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The study focused to measure the efficiency of District Co-operative banks of Odisha using DEA approach. The efficiency score are calculated under BCC Mode of DEA which is based on the assumption of variable return to scale and DCCBS are ranked accordingly.

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  1. International Journal of Management (IJM)
    Volume 10, Issue 6, November-December 2019, pp. 161–169, Article ID: IJM_10_06_016
    Available online at http://www.iaeme.com/ijm/issues.asp?JType=IJM&VType=10&IType=6
    Journal Impact Factor (2019): 9.6780 (Calculated by GISI) www.jifactor.com
    ISSN Print: 0976-6502 and ISSN Online: 0976-6510
    © IAEME Publication

    APPLICATION OF DATA ENVELOPMENT
    ANALYSIS FOR MEASURING FINANCIAL
    EFFICIENCY OF DISTRICT CENTRAL
    COOPERATIVE BANKS
    Chinmaya Kumar Rout
    Research Scholar, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

    Dr. Prafulla Kumar Swain
    Professor, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India

    Dr. Manoranjan Dash*
    Associate Professor, Faculty of Management Sciences,
    Siksha O Anusandhan (Deemed to be University), Bhubaneswar, India
    *Corresponding Author Email: manoranjandash@soa.ac.in

    ABSTRACT
    Agriculture being the primary sector of Indian economy now a day’s drawn more
    attention and emphasised has given for the overall development. Efficient credit
    facilities are essential for the improvement of this sector. Cooperative bank play a vital
    role in providing the forward as well as backward linkage of agricultural credit to be
    routed. The study focused to measure the efficiency of District Co-operative banks of
    Odisha using DEA approach. The efficiency score are calculated under BCC Mode of
    DEA which is based on the assumption of variable return to scale and DCCBS are
    ranked accordingly. The findings indicated that majority of DCCB’s are efficient where
    as others found to be inefficient.
    Keywords: Financial Efficiency, Cooperative Banks, Data Envelopment Analysis
    Cite this Article: Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and
    Dr. Manoranjan Dash, Application of Data Envelopment Analysis for Measuring
    Financial Efficiency of District Central Cooperative Banks, International Journal of
    Management (IJM), 10 (6), 2019, pp. 161–169.
    http://www.iaeme.com/IJM/issues.asp?JType=IJM&VType=10&IType=6

    http://www.iaeme.com/IJM/index.asp 161 editor@iaeme.com

  2. Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central
    Cooperative Banks

    1. INTRODUCTION
    In India 70% of the gross population are dependent on agriculture. Agricultural Credit act as
    significant input which support and enhances crop production as well as other allied activities.
    Co-operative banks all over the country play a significant role in socio-economic development
    of rural people by supplying timely credit.
    The Cooperative Credit system has three elements i.e. short term credit, medium-term
    credit, and long term credit structure. The short term structure focused on rural, district and
    State as defined as Primary Agricultural Credit Societies (PACS) , District Central Cooperative
    Banks (DCCBs) and State Cooperative Banks (SCBs) . District Central Co-operative Banks
    (DCCBs) provides Short Term credit facilities for crop production through Primary
    Agricultural Credit Societies (PACS) and Medium Term credit directly to farmers for
    procurement of agricultural equipment’s. In Odisha, there are 17 District Central Cooperative
    Banks with their 322 branches catering the credit need of rural people. The District Central Co-
    operative Banks take financial assistance from State Cooperative Bank. The main functions of
    DCCBs provide finance to the PACS, acceptance of deposits, granting of loans/advances, a
    collection of bills, safe custody of valuable assets and agency services. DCCBs functions as the
    fulcrum of credit flow for improving rural economy interlinking various other supporting sub-
    sectors of agriculture. Performance of banks is the product of efficiency, utilization, and
    productivity. Productivity and efficiency reflect overall performance. As productivity
    establishes the relationship between input and output while the productivity level is recognized
    as an efficient situation.Thus efficiency reflects that a firm’s capacity to produce more outputs
    from a constant set of inputs. In the public sector .it is about to maximize the quality, scope,
    and timeliness of service delivery with minimum possible input factors. In changing era of
    competitive environment analyzing the efficiency is not only important for institutions but also
    obvious for their competitiveness and solvency. In this study, an attempt has been made to
    measure the financial efficiency of District Central Co-operative Banks (DCCBs) of Odisha
    with the help of Data Envelopment Analysis (DEA). The organization of article i.e
    Introduction, Review of Literature, Objective of the study, Methodology, Data Analysis &
    Discussions, Conclusions.

    2. REVIEW OF LITERATURE
    There are various methods and tools available to measure the efficiency of banks. Data
    Envelopment Analysis is leading one. Data Envelopment Analysis (DEA) is non-parametric
    technique used for measuring the efficiency of different Decision Making Units (DMUs). Data
    Envelopment Analysis concept was first given by Farrell, M.J. (1957) and latter on Charnes,
    Cooper, and Rhodes (CCR) (1978) improved this model with the assumption of Constant
    Returns to Scale for measuring the efficiency of various DMUs. Further Banker, Charles,
    Cooper (BCC) (1984) developed Variable Returns to Scale (VRS) of DEA for measuring the
    efficiency of DMUs which is an improvement over CCR model. First Sherman and Gold (1985)
    applied DEA to measure the efficiency of bank branches. Saha.A. and Ravisankar .S.T (2000)
    applied DEA for rating the Indian Commercial Banks and found it as suitable tool for measuring
    the efficiency of banks , Manandhar.R and Tang J.C.S. (2000) used DEA to study the efficiency
    of various branches of bank and identified that the best-performing branch are benchmark for
    other branches, Sathye .M,(2003) made an attempt to measure the productive efficiency of
    banks in a developing country that is India by using Data Envelopment Analysis and found that
    public sector banks are performing well than private sector banks and foreign banks and suggest
    that policy should be implemented to reduce Non-Performing Assets , rationalization of staffs
    and branches to make Indian Banks compete globally, Varadi, V.K., Mavaluri, P.K., and
    Boppana,N. (2006) used Data Envelopment Analysis for measuring the efficiency of all types

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  3. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    of banks i.e. Public Sector Banks, Private Sector Banks and Foreign Banks operating in India
    on the basis of productivity, profitability, financial management and asset quality. The study
    reveals that public sector banks are more efficient than others.
    Sinha,R.P.(2007) evaluate performance of Indian commercial banks Using the Super
    Efficiency Approach under DEA and found that Private sector banks are more efficient than
    Public sector banks, Debnath,R.M and Shankar.R.(2008) used DEA to measure the
    performance of 50 Indian Banks and result states that small and large nationalized banks are
    showing higher efficiency than medium-sized nationalized banks and suggest that banks should
    focus on rural area for more development, Chen, T.Y., Chen,C.B., and Peng, S.Y. (2008)
    conducted a study on a Cooperative Bank of Taiwan by using DEA and Balanced scorecard
    approach to know the operational performance and found that DEA will help to know the
    current operational efficiency whereas Balanced Scorecard approach will help to evaluate
    longer-term strategies and visions, Subramanyam.T and Reddy,C.S.(2008) measured the risk
    efficiency of commercial banks in India through Data Envelopment Analysis and indicated that
    Foreign banks showing risk efficiency than Public and Private Sector Banks, hence these banks
    should strengthen their internal risk control system. Shirvani,H., Taj.S and Mirshab.B. (2011)
    applied Data Envelopment Analysis to develop a new model for measuring the banking
    efficiency in Turkey and found that non-linear input-output combination is more appropriate
    and efficient than linear input-output combination under standard DEA measure,
    Aryanezhada.M.B, Najafib.E ,and Farkousha.S.B (2011) used DEA and Balanced Scorecard
    approach for measuring the efficiency of service industry with reference to a private bank of
    Iran and their study resulted a new model which can be implemented for other financial sectors.
    Joshi,P.V and Bhalerao,J.V.(2011) applied DEA to evaluate the efficiency of the Indian
    banking sector and found that majority of the banks are efficient, Anjum.S (2012) studied
    various technique’s for measuring banking efficiency and found that DEA is a suitable
    technique for measuring the efficiency of the decision-making unit, Sharma.A.K, Sharma.D,
    and Baru.M.K.(2012) applied DEA and Tobit regression to measure the efficiency and
    productivity of Indian banks and found that average annual efficiency scores of Public sector
    bnaks are relatively more than private and foreign banks, Shahwan,T.M and
    Hassan,Y.M.(2013) analyzed the efficiency of UAE banks using DEA on the basis of three
    parameters i.e. profitability, marketability, and social disclosure and found that banks in UAE
    are performing in terms of profitability and social disclosure than marketability , Titko, J.,
    Stankeviciene. J., and Lace.N. (2014) used DEA for measuring the efficiency of Latvian banks
    and found that current financial database required for further investigation of efficiency,
    Fujii,H., Managi, S., and Matousek, R.(2014) analysed the efficiency of Indian banks and
    productive changes with undesirable outputs by using Weighted Russell Directional Distance
    Model (WDM) and found that foreign banks are showing more efficiency than public and
    private sector banks. So policymakers should take the necessary steps to improve their
    efficiency, Agarwal, N., Guha,B., Dutta, A., and Bandyopadhyay, G.(2014) evaluate the
    performance of banks in India using DEA and found that private sector banks are showing more
    efficiency than public sector bnaks, so public sector banks should improve their performance.
    Rao.N .E.S.V. and Gudala.C (2015) made performance appraisal and ranking of District
    Central Cooperative Banks through the Malmquist Index and Super-Efficiency model of DEA
    of Andhra Pradesh. They found that 32 % of DCCBs are showing an increasing trend and rest
    of the DCCBs are showing a mixed trend and they need more funds to achieve optimality. Kaur,
    S., & Gupta, P.K.(2015) measure the efficiency of the Indian banks with the help of DEA and
    found that State bank and it’s associates showing higher efficiency in comparison to private
    banks. Akinsoyinu,A.C.(2015) evaluate the efficiency European Financial Cooperative sector
    by using Data Envelopment Analysis and found that Financial Cooperative sector in Europe are

    http://www.iaeme.com/IJM/index.asp 163 editor@iaeme.com

  4. Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central
    Cooperative Banks

    showing higher efficiency during the study period, Gayval,B.K, and Bajaj,V.H. (2015) measure
    the efficiency of Indian Banks by using Data Envelopment Analysis and Stochastic Frontier
    Analysis and found that the efficiency level of Indian banks are same under both the approach.
    Syamni, G., and Abd Majid,M.S.(2016) studied the efficiency of Saving and Credit Cooperative
    Units in North Aceh of Indonesia by using Data Envelopment Analysis and found that
    cooperative units are not operating efficiently . So various steps should be taken to improve the
    efficiency level and increase capital. Othman, F.M., Mohd-Zamil, N.A., Rasid, S. Z. A.,
    Vakilbashi, A.,and Mokhber, M. (2016) studied extensive literatures available on the use of
    DEA as a technique for measuring efficiency of the banking sector. They found that generally
    two methods of Data Envelopment Analysis are used DEA-CCR (Charnes-Cooper-Rhodes-
    1979) method with the assumption of constant return to scale and DEA –BCC (Banker-
    Charnes-Cooper-1984) method with the assumption of variable return to scale are used by
    different researchers. Madhvi and Srivastava.A. (2017) measure the efficiency of Indian
    commercial banks using Data Envelopment Analysis and found that merely generating more
    profits is not a significant parameter of banking efficiency but the path of growth is also
    important. Kaur.R and Aggarwal.M. (2017) measure the performance of public sector banks in
    India with the help of DEA and found that majority of Public Sector Banks are inefficient
    because they use excess input variables to produce more outputs. Rezaeiani, M. J., and
    Foroughi, A. A. (2018) conducted a study to find the criteria of differentiating between efficient
    DMUs under DEA approach and develop reference frontier concept. They have given a new
    model to measure the reference frontier which has the capability for ranking extreme and non-
    extreme efficient DMUs again it has no problem in dealing with negative data. From the study
    of the above literatures, we have observed that very less work has been undertaken on
    measuring the efficiency of cooperative banks. Agriculture development is possible through
    timely credit which provided by cooperative banks. So knowing the efficiency of cooperative
    banks is prime importance. In this paper we have attempted to measure the efficiency of District
    Central Cooperative Banks (DCCBs) operating in Odisha by applying Data Envelopment
    Analysis (DEA). The present study analyzed the efficiency of DCCBs operating in Odisha and
    ranks the DCCBs according to their performance.

    3. RESEARCH METHODOLOGY
    3.1. Data
    We have taken 17 District Central Cooperative Banks (DCCBs) operating in Odisha. The data
    collected from the Annual report of State Cooperative Bank and reports published by the
    National Federation of State Cooperative Banks (NAFSCOB). Data for 5 years (2012-13 to
    2016-17) are taken for study. For measuring the efficiency of DCCBs, we have used Data
    Envelopment Analysis (DEA).

    3.2. Data Envelopment Analysis (DEA)
    The model can also indicate directions for inefficient DMUs to become efficient. This model is
    popularly known as CCR model.This is based on the assumption of Constant Returns to Scale
    (CRS) of Various DMUs. Constant returns to scale occur when increasing the number of inputs
    leads to an equivalent increase in the output.

    max ℎk = ∑𝑚
    𝑟=1 ur yrk 1
    Subject to:
    𝑛

    ∑ vi xik = 1
    𝑖=1

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  5. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    𝑚 𝑛

    ∑ ur yrj − ∑ vi xij ≤ 0, ∀ j
    𝑟=1 𝑖=1
    ur vi ≥ 0, ∀ r, i
    With:
    Y= outputs ,x = inputs
    u ,v = weights;
    r=1,……..,m; i=1,……,n;
    j=1,…….N
    BCC model developed by Banker, Charles, Cooper (BCC) (1984) developed which are
    based on the assumption of Variable Return to Scale (VRS) is an improvement of the CCR
    model of DEA. Variable returns to scale occurs when an increase in inputs does not result in a
    proportional change in the outputs.
    max ℎk = ∑𝑚
    𝑟=1 ur yrk − uk 2
    Subject to:
    𝑛

    ∑ vi xik = 1
    𝑖=1
    𝑚 𝑛

    ∑ ur yrj − ∑ vi xij − ur ≤ 0,
    𝑟=1 𝑖=1
    ur vi ≥ 0,
    With:
    Y= outputs, x = inputs
    u ,v = weights;
    r=1,……..,m; i=1,……,n;
    j=1,…….N

    4. DATA ANALYSIS AND DISCUSSION
    Here Input oriented model of BCC-DEA is used. The 5 years average (2012-13 to 2016-17) is
    taken for each of the variables and presented in table .1

    Table 1 Input Data for DEA
    Deposits Borrowings Loan & Advances Investments
    Name of the DCCBs
    (In lakhs) (In lakhs) (In lakhs) (In lakhs)
    Angul DCCB 60,578.90 36382.58 50059.17 53127.82
    Aska DCCB 18,748.40 15767.64 22995.74 14339.94
    Balasore DCCB 1,20,349.86 82159.15 120947.88 47737.01
    Banki DCCB 14,118.53 9269.72 15944.62 12529.62
    Bhawanipatna DCCB 15,912.91 17265.36 23739.62 11456.74
    Berhampur DCCB 37,151.11 18797.95 28505.3 24532.41
    Bolangir DCCB 47,564.59 21492.53 60229.01 22710.48
    Boudh DCCB 14,277.74 15108.99 23833.92 23521.37
    Cuttack DCCB 94,848.79 83371.77 111018.2 82867.19
    Keonjhar DCCB 31,764.87 17596.34 23639.45 25056.27

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  6. Application of Data Envelopment Analysis for Measuring Financial Efficiency of District Central
    Cooperative Banks

    Khurda DCCB 27,463.17 28462.02 37712.58 16584.32
    Koraput DCCB 47,813.28 35111.38 51104.01 43043.48
    Mayurbhanj DCCB 26,856.16 17377.71 29080.95 20313.48
    Nayagarh DCCB 15,962.10 18476.16 28126.46 12178.09
    Sambalpur DCCB 91,827.61 90797.72 136958.64 66093.63
    Sundargarh DCCB 52,639.43 32930.57 51698.23 44178.09
    Puri Nimapara DCCB 7,582.54 11940.55 15751.4 5592.19
    (Authors design)
    The of table .1data run through DEA model
    DEA Solver LV8.0/ BCC(BCC-I)
    Problem = Name of the DCCBs
    No. of DMUs = 17
    Returns to Scale = Variable (Sum of Lambda = 1)

    Table 2 Input Output variables
    S.no Input Variables Output Variables
    1 Deposits Loan & Advances
    2 Borrowings Investments

    Banks secure score 1 will be efficient and bank secure score less than 1 will be inefficient.

    Table 3 Results
    No. Decision Making Units Score Return To Scale of DMUs
    1 Angul DCCB 1 Decreasing
    2 Aska DCCB 0.8348 Increasing
    3 Balasore DCCB 0.9291 Decreasing
    4 Banki DCCB 1 Increasing
    5 Bhawanipatna DCCB 0.8707 Decreasing
    6 Berhampur DCCB 0.8801 Decreasing
    7 Bolangir DCCB 1 Constant
    8 Boudh DCCB 1 Constant
    9 Cuttack DCCB 1 Decreasing
    10 Keonjhar DCCB 0.9213 Decreasing
    11 Khurda DCCB 0.8411 Decreasing
    12 Koraput DCCB 0.9744 Decreasing
    13 Mayurbhanj DCCB 0.8514 Increasing
    14 Nayagarh DCCB 1 Decreasing
    15 Sambalpur DCCB 1 Decreasing
    16 Sundargarh DCCB 1 Decreasing
    17 Puri Nimapara DCCB 1 Constant
    The result of analysis shown in Table.3. Variable return to scale model of Data Envelopment
    Analysis are categorized into three i.e Increasing Return to Scale, Decreasing Return to Scale
    and Constant Return to Scale. Those banks have more increase of output variables than input
    variables, they are coming under an Increasing Return to Scale, DMUs having more increase
    of input variables than output variables are coming under Decreasing Return to Scale and
    DMUs having an increase of input and output variables at equal proportion are coming under a
    Constant Return to Scale. Out of 17 District Central Cooperative Banks (DCCBs) in Odisha 3
    DCCBs are coming under an Increasing Return to Scale, 11 DCCBs are coming under
    Decreasing Return to Scale and 3 DCCBs are coming under a Constant Return to Scale.

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  7. Chinmaya Kumar Rout, Dr. Prafulla Kumar Swain and Dr. Manoranjan Dash

    Table 4 Ranking
    DMU Score Rank
    Angul DCCB 1 1
    Banki DCCB 1 1
    Bolangir DCCB 1 1
    Boudh DCCB 1 1
    Cuttack DCCB 1 1
    Nayagarh DCCB 1 1
    Sambalpur DCCB 1 1
    Sundargarh DCCB 1 1
    Puri Nimapara DCCB 1 1
    Koraput DCCB 0.9744 10
    Balasore DCCB 0.9291 11
    Keonjhar DCCB 0.9213 12
    Berhampur DCCB 0.8801 13
    Bhawanipatna DCCB 0.8707 14
    Mayurbhanj DCCB 0.8514 15
    Khurda DCCB 0.8411 16
    Aska DCCB 0.8348 17
    Table-4 shows the score of DCCBs operating in Odisha basing upon the BCC model of
    DEA. It is observed that 9 District Central Cooperative Banks (DCCBs) of Odisha are efficient,
    because they have scored 1 and other 8 District Central Cooperative Banks (DCCBs) of Odisha
    are inefficient because they have scored less than 1. We have taken Deposits and Borrowings
    of DCCBs as input variables, these are a source of funds to banks. Loan & Advances,
    Investments of DCCBs as output variable these are two major utilization of funds by banks.
    The banks recorded inefficiencies are not maintaining equilibrium between in and out the flow
    of funds. The banks recorded inefficiencies are in the tribal and backward area of the state, so
    awareness should be created by banks to attract the rural farmer to avail timely credit and take
    the advantages of various schemes. The DCCBs are balancing center between Primary
    Agriculture Cooperative Societies and State Cooperative Banks (StCB). Cooperative banks are
    facing top competition from Commercial Banks. Political interference also affecting smooth
    functioning Cooperative Banks. Out 17 DCCBs more than 50 % of them are operating at
    efficiency level whereas others operating at lower than efficiency.

    5. CONCLUSION
    The result of the study indicated that District Central Cooperative Banks (DCCBs) in Odisha
    are showing a moderate level of efficiency. Out of 17 DCCBs 9 of them are efficient and 8 of
    them are showing inefficiency in performance on basis of BCC model of DEA. The study has
    the limitation of considering less number of input and output variables. Other researchers may
    take more variables for future study. The policymakers should focus on fund utilization and
    timely credit disbursement to make inefficient DCCBs to make them an efficient. The banks
    should implement modern banking facilities like ATM facilities, Internet banking, issue more
    number of Kisan credit card , new banking schemes for customer to make themselves
    competitive in market.

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    Cooperative Banks

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