Comparative Bootstrap DEA Technical Efficiencies and Determinant Factors: Evidence From the Islamic Banks of Bahrain and United Arab Emirates
Bibliographic record
Abstract
Applying the Bootstrap DEA method the paper obtained the technical efficiencies of the Islamic banks of Bahrain and the United Arab Emirates (UAE) using the panel data of 2011-2016. The paper found the 95 percent confidence interval mean bias-corrected overall technical efficiencies (OTEBC) of the Islamic banks of Bahrain was less than that of UAE. The OTEBC of Bahrain and UAE was 85.4 percent and 99.1 percent respectively suggesting the average inefficiency (14.6 percent) of the Islamic banks of Bahrain was higher than that (0.5 percent) of the UAE bank and the difference was significant. The paper applied the Simar-Wilson regression (both sided truncated) for determining the efficiency factors. The regression results of pooled data found that non-performance loan to total assets (NPLTA), loan to total assets (LOATA), profitability index (ROA), and bank-size (LOGTA) were significant factors. The regression results found that the efficiency of the Islamic banks was positively related to ROA and negatively related to NPLTA, LOANTA, DEPTA, and LOGTA. Results of regression, running the regression separately for Bahrain and UAE, confirmed the findings of pooled results. The country wise regression results of the Bahrain and UAE Islamic banks found that the NPLTA, LOATA, and LOGTA were significant factors and they are negatively related to the efficiency of the Islamic banks. The finding of this paper that LOANTA was negatively related to bank TE supported the finding of Zelenyuk (2015).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".