An Empirical Analysis of Bank Efficiency in Gambia
Bibliographic record
Abstract
This study evaluates the Technical Efficiency (TE), Pure Technical Efficiency (PTE) and Scale Efficiency (SE) of commercial banks in the Gambian banking sector. In the first stage, a non-parametric approach (DEA) is used to evaluate the relative efficiency of 12 banks from 2009 to 2017 based on “the production approach” of modeling bank efficiency. In the second stage the relationship between certain bank-specific and environmental variables and efficiency scores are examined by employing the Tobit regression model. The empirical analyses from the first stage reveals that about 42% of commercial banks were CRS technically efficient and 83% of them were VRS technically efficient in 2017. Only 42% of the banks were at the optimal size for their particular input–output mix, the remaining eight banks were scale inefficient. The level of overall technical efficiency of commercial banks in the Gambia accounted 86.5% in terms of TE, 93.1% in terms of PTE and 92.5% in terms of SE. The second stage analyses reveal that banks with the ability to charge lower interest on deposits and maintain higher interest rates on loans attain higher efficiency scores. Further, banks with large market share and market power in pricing their products can improve their efficiency levels. Lower liquidity risk is associated with higher efficiency scores. There is a weak evidence of negative association with bank size and efficiency, suggesting that smaller banks may obtain operational advantages that bring about higher efficiencies
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How this classification was reachedexpand
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".