IRPG

Menu
Management

Technical Efficiency and Its Determinants: A DEA and Tobit Analysis of Indian Public Sector Banks (2015–16 to 2024–25)

IJEBSM | Published: September 11, 2026 | Vol. 2 Issue 3 | ISSN: 3139-0919
Dr. Syeeda Khatoon1✉,
Md. Zulbahrain Aasi2
1 Associate Professor, Economics Section, Women’s College, Aligarh Muslim University, India
2 Research Scholar, Department of Economics, Aligarh Muslim University, India
Download PDF View DOI

Abstract

This paper examines the technical efficiency of Indian Public Sector Banks during 2015–16 to 2024–25 using a two-stage Data Envelopment Analysis (DEA)–Tobit regression framework. In the first stage, DEA estimates overall technical efficiency (CCR), pure technical efficiency (BCC), and scale efficiency (SE), while the second stage employs Tobit regression to identify the determinants of bank efficiency. The DEA results indicate a gradual improvement in bank efficiency over the study period. The BCC model identifies more efficient banks than the CCR model, suggesting that scale inefficiency, rather than managerial inefficiency, was the primary source of inefficiency. Furthermore, the scale efficiency results indicate that banks progressively moved towards their optimal scale of operation. The Tobit regression results reveal that Bank Size [Ln (Total Assets)] has a significant negative effect on efficiency, whereas Capital Adequacy Ratio (CAR), Net NPA Ratio, Return on Assets (ROA), and Cost-to-Income Ratio (CIR) do not have statistically significant effects. Overall, the findings suggest that improving resource utilization and operating at an optimal scale are essential for enhancing the efficiency and long-term performance of Indian Public Sector Banks.
Keywords: Data Envelopment Analysis (DEA), Tobit Regression, Technical Efficiency, Public Sector Banks, Total Deposits, Operating Expenses, Loans and Advances, Investments, Bank Size, Capital Adequacy Ratio (CAR), Return on Assets (ROA), Net Non-Performing Assets (Net NPA).

References

  1. Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.
  2. Berger, A. N., & Humphrey, D. B. (1997). Efficiency of financial institutions: International survey and directions for future research. European Journal of Operational Research, 98(2), 175–212.
  3. Berger, A. N., Hunter, W. C., & Timme, S. G. (1993). The efficiency of financial institutions: A review and preview of research past, present, and future. Journal of Banking & Finance, 17(2–3), 221–249.
  4. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.
  5. Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis (2nd ed.). Springer.
  6. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-Solver software (2nd ed.). Springer.
  7. Das, A., Nag, A., & Ray, S. C. (2005). Liberalisation, ownership and efficiency in Indian banking: A nonparametric analysis. Economic and Political Weekly, 40(12), 1190–1197.
  8. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society: Series A, 120(3), 253–290.
  9. Fried, H. O., Lovell, C. A. K., & Schmidt, S. S. (Eds.). (2008). The measurement of productive efficiency and productivity growth. Oxford University Press.
  10. Greene, W. H. (2018). Econometric analysis (8th ed.). Pearson.
  11. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). McGraw-Hill.
  12. Kumar, M., & Vincent, C. (2011). Benchmarking Indian banks using DEA in post-reform period: A progressive time-weighted mean approach. Service Industries Journal, 31(14), 2455–2485.
  13. Kumar, S., & Gulati, R. (2008). An examination of technical, pure technical and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33–69.
  14. Kumar, S., & Gulati, R. (2009). Measuring efficiency, effectiveness and performance of Indian public sector banks. International Journal of Productivity and Performance Management, 59(1), 51–74.
  15. Ministry of Finance. (2018, July 24). Profit, losses, GNPAs of Public Sector Banks [Press release]. Press Information Bureau, Government of India.
  16. Paradi, J. C., Yang, Z., & Zhu, H. (2011). Assessing bank and bank branch performance: Modeling considerations and approaches. In W. W. Cooper, L. M. Seiford, & J. Zhu (Eds.), Handbook on Data Envelopment Analysis (2nd ed., pp. 315–361). Springer.
  17. R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  18. Ray, S. C. (2004). Data envelopment analysis: Theory and techniques for economics and operations research. Cambridge University Press.
  19. Ray, S. C., & Das, A. (2010). Distribution of cost and profit efficiency: Evidence from Indian banking. European Journal of Operational Research, 201(1), 297–307.
  20. Reserve Bank of India. (2018). Report on Trend and Progress of Banking in India 2017–18. Reserve Bank of India.
  21. Sathye, M. (2003). Efficiency of banks in a developing economy: The case of India. European Journal of Operational Research, 148(3), 662–671.
  22. Seiford, L. M., & Thrall, R. M. (1990). Recent developments in DEA: The mathematical programming approach to frontier analysis. Journal of Econometrics, 46(1–2), 7–38.
  23. Sherman, H. D., & Zhu, J. (2006). Service productivity management: Improving service performance using data envelopment analysis (DEA). Springer.
  24. Simar, L., & Wilson, P. W. (2007). Estimation and inference in two-stage, semi-parametric models of production processes. Journal of Econometrics, 136(1), 31–64.
  25. Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36.
  26. Wooldridge, J. M. (2020). Introductory econometrics: A modern approach (7th ed.). Cengage Learning.
  27. Zhu, J. (2014). Quantitative models for performance evaluation and benchmarking: Data envelopment analysis with spreadsheets (3rd ed.). Springer.
  28. Kumar, V. P., & Kar, S. (2022). Measuring the efficiency of Indian public and private banks using the two-stage network DEA model. Benchmarking: An International Journal.
  29. Kundu, S., & Banerjee, A. (2025). Operational efficiency and Malmquist productivity of Indian banks: A comparison between public and private sector banks. Decision.
  30. Application of a Slack-Based DEA Approach to Measure Efficiency in Public Sector Banks in India with Non-Performing Assets as an Undesirable Output. (2025). Journal of Risk and Financial Management, 18(4).
🔓 Open Access | Published by IR Publishing Group. This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Log in

Sign up
Sign up
Scroll to Top