Efficiency Measurement of Indian Banking Industry: An Empirical Comparative Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The following study is conducted to measure and compare the performance of 32 Indian banks, 21 public banks, and 11 private banks, at two tiers during the period of 2008–2018. Industrial analysis of both the public and private banking sectors is conducted in the first tier, followed by an individual bank-level analysis at the second tier. Data analysis consists of deposits, assets, and equity as inputs to measure the outputs practicing data envelopment analysis techniques. The empirical results portray a mixed trend in various elements of efficiency. They reveal that with the common pledge to expand market share and performance, public and private banks have been improving and covering the highest efficiency level. However, at the industry level, the private banking industry has slightly better technical and pure technical efficiency results compared to the public banking industry. On the other hand, the public banking sector performed well compared to the private banking industry in the stipulated study period based on mean scale efficiency results.Generally, many studies on Indian Banking Industry focus on determinants of industrial banking growth indicators. Further, we examine Indian banking performance at the individual bank level by incorporating the latest available data. In terms of technical and pure technical efficiency, Kotak Mahindra Bank Ltd., a private bank, scored the highest at the individual bank level. The State Bank of Bikaner & Jai has the highest score in terms of scale efficiency and thus is the best example of a public sector bank. Despite the improvement in income and deposits in both types of banking, there is still room for public banks to redirect their short-term and long-term marketing and communication strategies to focus on targeting customers and enhancing management skills at the branch level.
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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 | 0.024 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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