Application of a Slack-Based DEA Approach to Measure Efficiency in Public Sector Banks in India with Non-Performing Assets as an Undesirable Output
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Bibliographic record
Abstract
Ignoring the presence of non-performing assets makes efficiency measurement inappropriate and incomplete. Thus, the present study considers non-performing assets as an undesirable output and applies the slack-based efficiency model to measure the efficiency of public sector banks in India during 2004–2005 to 2018–2019. A two-metric performance assessment of sample banks is carried out using mean efficiency and the non-performing assets management ratio. This study is extended to investigate determinants of bank efficiency using a fixed effects model and dynamic panel data regression on the contextual variables. Results show that profitability as measured by return on equity (ROE) and priority sector exposure have had no impact on efficiency. However, cost of deposits and capital adequacy ratio have a significant negative impact on the efficiency of public sector banks in India. Most importantly, the study finds a decline in efficiency in recent years, indicating a necessity of serious efforts for revamping these state-owned banks.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it