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Impact of non-traditional activities on the efficiency of Indian banks: an empirical investigation

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  • Rachita Gulati
  • Sunil Kumar

Abstract

This article investigates the relevance of the inclusion of non-traditional activities in the specification of banks' output on the efficiency of Indian banks. The results indicate that the exclusion of non-traditional activities not only understates the cost, technical and allocative efficiencies of individual banks but also affects the ranking of ownership groups in the industry. In particular, when a proxy for non-traditional activities is accounted for in the output specification, the foreign banks appear to be more efficient than public and private sector banks. Overall, the results reinforce the prevailing view in the extant literature that the exclusion of non-traditional activities causes misspecification of banks' output, and may distort the efficiency estimates.

Suggested Citation

  • Rachita Gulati & Sunil Kumar, 2011. "Impact of non-traditional activities on the efficiency of Indian banks: an empirical investigation," Macroeconomics and Finance in Emerging Market Economies, Taylor & Francis Journals, vol. 4(1), pages 125-166.
    • Handle: RePEc:taf:macfem:v:4:y:2011:i:1:p:125-166
    DOI: 10.1080/17520843.2010.530939
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    Citations

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    Cited by:

    1. Aadil Ummar Zaman & Nisar Ahmad Khan, 2023. "Decomposition of productivity growth among the cooperative banks in Jammu and Kashmir (India)," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-8, December.
    2. Abhiman Das & Subal C. Kumbhakar, 2016. "Markup and efficiency of Indian banks: an input distance function approach," Empirical Economics, Springer, vol. 51(4), pages 1689-1719, December.
    3. Gulati, Rachita & Kumar, Sunil, 2016. "Assessing the impact of the global financial crisis on the profit efficiency of Indian banks," Economic Modelling, Elsevier, vol. 58(C), pages 167-181.
    4. Abdul Latif Alhassan & Michael Lawer Tetteh, 2017. "Non-Interest Income and Bank Efficiency in Ghana: A Two-Stage DEA Bootstrapping Approach," Journal of African Business, Taylor & Francis Journals, vol. 18(1), pages 124-142, January.
    5. Pooja Bansal & Aparna Mehra & Sunil Kumar, 2022. "Dynamic Metafrontier Malmquist–Luenberger Productivity Index in Network DEA: An Application to Banking Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 297-324, January.
    6. Bansal, Pooja & Kumar, Sunil & Mehra, Aparna & Gulati, Rachita, 2022. "Developing two dynamic Malmquist-Luenberger productivity indices: An illustrated application for assessing productivity performance of Indian banks," Omega, Elsevier, vol. 107(C).
    7. Jorge Antunes & Abdollah Hadi-Vencheh & Ali Jamshidi & Yong Tan & Peter Wanke, 2022. "Bank efficiency estimation in China: DEA-RENNA approach," Annals of Operations Research, Springer, vol. 315(2), pages 1373-1398, August.

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