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23-04-2024 | Original Article

Dynamics of Operational Efficiency in Credit Lending and Recovery of Stressed Assets: An Alternative Approach with Undesirable By-Products

Authors: Gargi Sanati, Anup Kumar Bhandari

Published in: Journal of Quantitative Economics

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Abstract

Our study estimates the operational efficiency of Indian banks during 2009–2010 to 2017–2018, considering advances, and recovery of stressed assets as desirable outputs, while NPAs and slippages are undesirable by-products. Our first stage DEA analyses conclude that public sector banks have significant scope to improve their lending and recovery efficiency, while also reducing their stressed assets. Our analyses also reveal that banks are more efficient in managing credit risks for shorter-term loan portfolios and secured loans, while positive economic externalities are more prominent in determining the efficiency of banks in priority sector lending. We also observe that a competitive scenario within the banking sector, overall macroeconomic growth, and low cost of funds play vital roles in improving banks' operational efficiency and reducing credit risk. Our findings suggest that liquid assets might be more beneficial than illiquid collaterals in improving the recovery of stressed assets.

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Appendix
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Footnotes
1
To add further, Das et al. (2005) also study profit and revenue efficiencies for Indian banking sector.
 
2
As per bank’s annual report, reduction of NPA can have three segments: reduction from upgradation, reduction from write off, and reduction from cash recovery.
 
3
Due to large scale merger drives of the public sector banks, we keep aside the later periods from our analyses.
 
4
Another strand of literature is to use bootstrap-based analyses to (a) obtain biased-corrected TES at the first stage; and (b) truncated regression for drawing consistence inference at the second stage, a la Simar and Wilson (2007). However, the question of bias at the conventional DEA estimation arises in the absence of true data generating process while using sample data. Nonetheless, we restrain ourselves from doing so, since we use almost the entire set of the banks under the two chosen groups (for which consistent data is available) and only a handful of them is left out. Hence, extent of bias, if any, is expected to be negligible.
 
5
The basic assumptions about the production technology that are made in this method are as follows: (a) All observed input–output bundles are feasible; (b) the production possibility set is convex implying that given a set of N feasible input–output bundles, any weighted average of these N input bundles can produce the same weighted average of the corresponding N output bundles and (c) any input or output is freely disposable. However, free disposability assumption needs to be adjusted if bad output(s) or some input(s) use of which beyond a threshold level may have detrimental effects on production process. Nonetheless, these assumptions enable one to construct a production possibility frontier on the basis of the observed inputs-output bundles of a given set of banks, following the DEA method.
 
6
One may also define an input-oriented technical efficiency. This is not done in the present study. See Coelli et al. (1998) and Ray (2004) for further details.
 
7
For extensive mergers and acquisitions of public sector banks in India in the year 2018, 2019 and 2020, we exclude these years from our analyses. This helps us to keep the standard of the data without losing much of information.
 
8
To clarify further, TES = 92.2% implies that there exists a further [{(1/0.922) – 1} × 100]% of output expansion opportunities.
 
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Metadata
Title
Dynamics of Operational Efficiency in Credit Lending and Recovery of Stressed Assets: An Alternative Approach with Undesirable By-Products
Authors
Gargi Sanati
Anup Kumar Bhandari
Publication date
23-04-2024
Publisher
Springer India
Published in
Journal of Quantitative Economics
Print ISSN: 0971-1554
Electronic ISSN: 2364-1045
DOI
https://doi.org/10.1007/s40953-024-00389-8

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