Measuring Efficiency of Indian Banks Using Window DEA Analysis

Part of the India Studies in Business and Economics book series (ISBE)


This chapter aims at measuring the efficiency of Indian banks in the liberal era. It also attempts to unearth the reasons behind the divergence in efficiency scores among different categories of banks and across time. The study uses nonparametric window DEA or estimating efficiency scores of 59 Indian banks over the period 1992–2012. The window DEA model helps understand the panel data features present in efficiency score. The entire time period is broken into 19 windows (1992–94, 1993–95,…, 2010–12) to carry out the window DEA analysis. The study finds that the public sector banks performed relatively better in terms of efficiency. The performance of the foreign banks and the old private sector banks has been relatively worse. From the pattern of changes in the efficiency scores across windows, a decline in the efficiency score in the banking sector during the period 2000–2004 is seen. In early 2000s, Indian banking sector witnessed several changes related to technology adoption and banking market operations. In the dynamic situation of expanding new possibilities, Indian banks took the necessary time to adjust themselves to the changed scenario.


  1. Asmild, M., Paradi, J. C., Aggarwal, V., & Schaffnit, C. (2004). Combining dea window analysis with the malmquist index approach in a study of the Canadian banking industry. Journal of Productivity Analysis, 21(1), 67–89.CrossRefGoogle Scholar
  2. Avkiran, N. K. (2004). Decomposing technical efficiency and window analysis. Studies in Economics and Finance, 22(1), 61–91.CrossRefGoogle Scholar
  3. Charnes, A., Clark, C. T., Cooper, W. W., & Golany, B. (1985). A developmental study of data envelopment analysis in measuring the efficiency of maintenance units in the U.S. air forces. Annals of Operations Research, 2, 95–112.CrossRefGoogle Scholar
  4. Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1995). Data Envelopment Analysis: Theory. New York: Springer, Methodology and Applications.Google Scholar
  5. Coelli, T., Rao, D. S. P, O’Donnell, C. J., & Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis, Second edn. Springer Science & Business Media: New York.Google Scholar
  6. Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on Data Envelopment Analysis. New York: Springer Science+Business Media.CrossRefGoogle Scholar
  7. David, P. (1990). The dynamo and the computer: An historical perspective on the modern productivity paradox. The American Economic Review, May, pp. 355–61.Google Scholar
  8. Greenwood, J., & Yorukoglu, M. (1997). 1974. Carnegie-rochester conference series on public policy, 46, 49–95.CrossRefGoogle Scholar
  9. Gu, H., & Yue, J. (2011). The relationship between bank efficiency and stock returns: Evidence from Chinese listed banks. World Journal of Social Sciences, 1(4), 95–106.Google Scholar
  10. Jones, C. I. (1997). Introduction to Economic Growth. New York: W.W. Norton & Company.Google Scholar
  11. Oliner, S. D., & Sichen, D. E. (2000). The resurgence of growth in the late 1990s: Is information technology the story? Journal of Economic Perspectives, 14(Fall), 3–22.CrossRefGoogle Scholar
  12. Repkova, I. (2014). Efficiency of czech banking sector employing the dea window analysis approach. Procedia Economics and Finance, 12, 587–596.CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Economics DepartmentBurdwan UniversityBurdwanIndia
  2. 2.Economics DepartmentShyampur Siddheswari Mahavidyalaya, University of CalcuttaHowrahIndia

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