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Technical Efficiency, Ownership, And Reforms: An Econometric Study of Indian Banking Industry

Author

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  • Prithwis Kumar De

    (Ernst and Young Private Limited, 2nd Floor, The Capital Court, LSC Phase III, Olof Palme Marg, Munirka, New Delhi-110 067, India)

Abstract

The paper attempts to empirically investigate the ownership-liberalisation-efficiency issue of the Indian banking industry using a panel data set for the years 1985 to 1995-96. It tries to estimate time-invariant and time-variant technical efficiency of the banks in the Indian banking industry. It distinguishes between three ownership groups of the banking industry: public sector, domestic private sector, and foreign sector. A stochastic frontier production function incorporating the Cobb-Douglas technology with four inputs and two alternative measures of output is estimated. Our results show that the efficiency of the banking industry has not improved after liberalisation and the foreign-owned banks as a group has the highest efficiency regardless of the choice of the output measure. Between public sector banks and domestic private banks the results are sensitive to the output measure. Our results also show that for more than 70 per cent of the banks the hypothesis of time-invariant technical efficiency holds. In the post-liberalisation period, technical efficiency has increased for only 14 banks out of 18 banks for which time-variant model is more appropriate. The Bank of Tokyo-Mitsubishi has gained most in technical efficiency whereas the Vijaya Bank is worst affected in the post-liberalisation period. Among the public sector banks, the State Bank of Indore has gained most in technical efficiency in the post-liberalisation era.

Suggested Citation

  • Prithwis Kumar De, 2004. "Technical Efficiency, Ownership, And Reforms: An Econometric Study of Indian Banking Industry," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 39(1), pages 261-294, January.
    • Handle: RePEc:dse:indecr:v:39:y:2004:i:1:p:261-294
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    Citations

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    Cited by:

    1. Silva, Thiago Christiano & Tabak, Benjamin Miranda & Cajueiro, Daniel Oliveira & Dias, Marina Villas Boas, 2018. "Adequacy of deterministic and parametric frontiers to analyze the efficiency of Indian commercial banks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1016-1025.
    2. Kv, Bhanu Murthy & Deb, Ashis Taru, 2008. "Theoretical Framework Of Competition As Applied To Banking Industry," MPRA Paper 7465, University Library of Munich, Germany.
    3. Md Zobaer Hasan & Anton Abdulbasah Kamil & Adli Mustafa & Md Azizul Baten, 2012. "A Cobb Douglas Stochastic Frontier Model on Measuring Domestic Bank Efficiency in Malaysia," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-5, August.
    4. Tulika Bhattacharya & Meenakshi Rajeev & Indrajit Bairagya, 2018. "Are high-linked sectors more productive in India? An analysis under an input–output framework," Indian Economic Review, Springer, vol. 53(1), pages 333-367, December.
    5. H P Mahesh, 2006. "Liberalisation and Efficiency of Indian Commercial Banks: A Stochastic Frontier Analysis," Working Papers 177, Institute for Social and Economic Change, Bangalore.
    6. Sunil Kumar, 2013. "Banking reforms and the evolution of cost efficiency in Indian public sector banks," Economic Change and Restructuring, Springer, vol. 46(2), pages 143-182, May.
    7. H.P. Mahesh & Shashanka Bhide, 2008. "Do Financial Sector Reforms Make Commercial Banks More Efficient? A Parametric Exploration of the Indian Case," Margin: The Journal of Applied Economic Research, National Council of Applied Economic Research, vol. 2(4), pages 415-441, November.

    More about this item

    Keywords

    Indian Banking Industry; Technical Efficiency; Stochastic Frontier Analysis; Financial Reform;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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