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Modelling NPLs and identifying convergence phenomenon of banks: a case of pre-during and immediate after the crisis years in India

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  • Anju Goswami

Abstract

Purpose - Comparing conventional data envelopment analysis (DEA) model with contemporary Seiford and Zhu model, this study aims to evaluate the technical efficiency (TE) of Indian banks from 1998/99 to 2016/17 in the presence of non-performing loans (NPLs). Design/methodology/approach - To examine TE, this study has considered a novel approach, i.e. linear monotone decreasing transformation as suggested by Seiford and Zhu (2002), which treats undesirable output as a desirable output in the framework of Charnes, Cooper and Rhodes (CCR)-based output-oriented DEA approach. In particular, to remove the biasness from the estimated efficiency scores, Simar Wilson (1998, Algorithm #1) has been applied, which is perhaps the first attempt in this kind of literature till now. This study further tries to investigate the notion of sigma and unconditionalβ-convergence in TE using two-step system generalized method of moments model in dynamic panel framework. Findings - Treatment of NPLs using conventional DEA model misinterprets the TE scores, while a true picture emerges when the NPLs are correctly accounted as an undesirable output in banks’ loans production process. Efficiency has declined during the crisis years, but it recovered immediately after the crisis years in India. However, a sudden and steep deterioration in efficiency scores has been seen from 2013 till the most recent study period. Public sector banks and old private banks have reported higher average efficiency scores than new private banks (NPBs) and foreign banks (FBs) in India. However, FBs are the only commercial banks that maintained their efficiency levels during crisis years in India. This study also saw the persistence and presence ofσ-convergence phenomena in TE for Indian banks, reflecting the ability to reach up to “Catch-up” phenomenon owing to the lower dispersion and persistence of convergence in TE by the Indian banks. Practical implications - The actual efficiency score can only be estimated when the NPL will be considered as an undesirable output rather than a desirable output when designing the loan production process of banks. Although the ownership clusters of all commercial banks in India need to formulate stricter policies to increase the level of assets quality and efficiency, but, NPBs need to pay some more efforts in this direction. This study’s outcome has the potential to provide useful information for regulators and policymakers, which suggests that in which direction or in which clusters improvement are needed to raise the level of asset quality and technical efficiency in the coming years. Originality/value - For a long time, there has been the existence of trade-offs between researchers, like which is the best model for accounting for NPLs? Traditional or contemporary? Thus, our study aims to add knowledge to the limited stock of NPL modelling in the efficiency literature. Dynamic convergence in TE scores in Indian banks has yet not to be tested, which is another novelty of the study.

Suggested Citation

  • Anju Goswami, 2021. "Modelling NPLs and identifying convergence phenomenon of banks: a case of pre-during and immediate after the crisis years in India," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 30(1), pages 1-125, August.
  • Handle: RePEc:eme:jfrcpp:jfrc-09-2020-0083
    DOI: 10.1108/JFRC-09-2020-0083
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    Cited by:

    1. Anju Goswami, 2023. "COVID-19: boon/disguise for Indian banks?," Journal of Banking Regulation, Palgrave Macmillan, vol. 24(4), pages 381-402, December.

    More about this item

    Keywords

    Credit risk; Banking; Banking crisis; Econometrics; Technical efficiency; Bootstrapped DEA model; Nonperforming loans; Indian banks; Dynamic convergence analysis; G21; G32; G01; C50;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G01 - Financial Economics - - General - - - Financial Crises
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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