Assessing the Cost Efficiency of Commercial Banks in Nepal: An Empirical Analysis
Bibliographic record
Abstract
This paper examines the cost efficiency and its determinants for Nepalese commercial banks by using semi-parametric methodology. We first estimate the efficiency and growth of productivity using Data Envelopment Analysis and then identify firm-specific attributes that potentially explain cost efficiencies. The first-stage results indicate a considerable level of cost inefficiency, which is largely caused by technical inefficiency. Additionally, there exists a low level of external (particularly regulatory) influence on the input mix, as indicated by a very low level of allocative inefficiency. The growth in productivity is low and even negative, mostly resulting from a lack of technological progress. The second-stage results indicate that state-owned banks are less cost-efficient than private banks (domestic and foreign), and size has a consistently inverse impact on cost efficiency. Banks with higher financial capital, larger loan ratios, and higher profits tend to be more cost efficient; however, banks with higher credit risk tend to be less cost efficient. Our findings have implications for policymakers, regulators, and bank managers as a better understanding of the level and sources of bank efficiency helps reduce inefficiencies, formulate regulations to enhance the overall efficiency of the banking system, and develop policies to promote competition and financial stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".