Indian banking sector has faced several challenges in the past one decade due to a variety of factors including the macroeconomic slowdown since 2007–08, demonetisation and the pandemic caused by COVID. The buildup of excess capacity in the industrial sector led to worsening financial position of corporate firms and steep rise in the proportion of NPA s in the commercial banking sectors which remained mainly concentrated in the public sector banks. This led to the adoption of stringent asset quality norms, passage of Insolvency and Bankruptcy Code and recapitalization and consolidation of public sector banks. In the above context, the present study seeks to evaluate bank efficiency for 30 commercial banks (both public and private) for the period 2017–18 to 2023–24 using a directional DEA approach with undesirable output. Second, the study estimates both local returns to scale and scale elasticity for the period under observation. Finally, in the second stage, bank efficiency is explained by using both local likelihood and GAM regression. The second stage results indicate the importance of bank size and return on asset as determinants of bank efficiency.

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Bank Efficiency in India: A Non-parametric Approach

  • Ram Pratap Sinha

摘要

Indian banking sector has faced several challenges in the past one decade due to a variety of factors including the macroeconomic slowdown since 2007–08, demonetisation and the pandemic caused by COVID. The buildup of excess capacity in the industrial sector led to worsening financial position of corporate firms and steep rise in the proportion of NPA s in the commercial banking sectors which remained mainly concentrated in the public sector banks. This led to the adoption of stringent asset quality norms, passage of Insolvency and Bankruptcy Code and recapitalization and consolidation of public sector banks. In the above context, the present study seeks to evaluate bank efficiency for 30 commercial banks (both public and private) for the period 2017–18 to 2023–24 using a directional DEA approach with undesirable output. Second, the study estimates both local returns to scale and scale elasticity for the period under observation. Finally, in the second stage, bank efficiency is explained by using both local likelihood and GAM regression. The second stage results indicate the importance of bank size and return on asset as determinants of bank efficiency.