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Evaluation of technical efficiency and ranking of public sector banks in India

Author

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  • Sunil Kumar
  • Rachita Gulati

Abstract

Purpose - The purpose of this paper is to evaluate the extent of technical efficiency in 27 public sector banks operating in India and to provide strict ranking to these banks. Design/methodology/approach - Two popular data envelopment analysis (DEA) models, namely, CCR model and Andersen and Petersen's super‐efficiency model, were utilized. The cross‐section data for the financial year 2004/2005 were used for obtaining technical efficiency scores. Findings - The results show that only seven of the 27 banks are found to be efficient and thus, defined the efficient frontier; and technical efficiency scores range from 0.632 to 1, with an average of 0.885. Thus, Indian public sector banks, on an average, waste the inputs to the tune of 11.5 percent. Andhra Bank has been observed to be the most efficient bank followed closely by Corporation Bank. Further, the banks affiliated with SBI group turned out to be more efficient than the nationalized banks. The regression results incisively indicate that the exposure to off‐balance sheet activities, staff productivity, market share and size are the major determinants of the technical efficiency. Practical implications - The practical implication of the research findings is that apart from the proportional reduction of all inputs equivalent to the amount of technical inefficiency, most of the inefficient public sector banks need to reduce the use of the physical capital and augment non‐interest income to project themselves on the efficient frontier. Originality/value - This paper is the first to provide a strict ranking of Indian public sector banks on the basis of super‐efficiency scores.

Suggested Citation

  • Sunil Kumar & Rachita Gulati, 2008. "Evaluation of technical efficiency and ranking of public sector banks in India," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 57(7), pages 540-568, September.
  • Handle: RePEc:eme:ijppmp:v:57:y:2008:i:7:p:540-568
    DOI: 10.1108/17410400810904029
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    Citations

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    Cited by:

    1. Ravi Kumar Jain & Ramachandran Natarajan & Amlan Ghosh, 2016. "Decision Tree Analysis for Selection of Factors in DEA: An Application to Banks in India," Global Business Review, International Management Institute, vol. 17(5), pages 1162-1178, October.
    2. Bhagwan Jagwani, 2012. "Efficiency Measurement in the Indian Banking Industry: An Application of Data Envelopment Analysis," Vision, , vol. 16(4), pages 315-331, December.
    3. Gulati, Rachita, 2022. "Global and local banking crises and risk-adjusted efficiency of Indian banks: Are the impacts really perspective-dependent?," The Quarterly Review of Economics and Finance, Elsevier, vol. 84(C), pages 23-39.
    4. Dincer, Hasan & Hacioglu, Umit & Tatoglu, Ekrem & Delen, Dursun, 2019. "Developing a hybrid analytics approach to measure the efficiency of deposit banks," Journal of Business Research, Elsevier, vol. 104(C), pages 131-145.
    5. Coto-Millán, Pablo & Inglada, Vicente & Fernández, Xose Luis & Inglada-Pérez, Lucía & Pesquera, Miguel Ángel, 2016. "The “effect procargo” on technical and scale efficiency at airports: The case of Spanish airports (2009–2011)," Utilities Policy, Elsevier, vol. 39(C), pages 29-35.
    6. Congcong Yang & Alfred Taudes & Guozhi Dong, 2017. "Efficiency analysis of European Freight Villages: three peers for benchmarking," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 25(1), pages 91-122, March.
    7. Deepak Tandon & Kamini Tandon & Nidhi Malhotra, 2014. "An Evaluation of the Technical, Pure Technical and Scale Efficiencies in the Indian Banking Industry using Data Envelope Analysis," Global Business Review, International Management Institute, vol. 15(3), pages 545-563, September.
    8. Yung‐ho Chiu & Tai‐Yu Lin & Tzu‐Han Chang & Yi‐Nuo Lin & Shih‐Yung Chiu, 2021. "Prevaluating efficiency gains from potential mergers and acquisitions in the financial industry with the Resample Past–Present–Future data envelopment analysis approach," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 42(2), pages 369-384, March.

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