Investigating the Efficiency of GCC Banking Sector: An Empirical Comparison of Islamic and Conventional Banks
Bibliographic record
Abstract
We examine and compare the performance of 63 (21 Islamic and 42 conventional) GCC banks at two tiers, covering the period of 2010–2016. In the first tier, an industry-level analysis is conducted of each country, followed by an individual bank-level analysis in the second tier. Deposits, assets, and capital are taken as inputs to measure the outputs using data envelopment analysis techniques. At the industry level, we find that Islamic banking is at par with-if not better than-conventional banking in all terms of efficiency. Particularly, banking in Bahrain and KSA is among the best, whereas there is no scope for improvement in the UAE’s banking industry. This low performance could be attributed to a lack of standardization in products and schemes as well as the level of prudence in decision-making, governance, and operations. At the bank level, many Islamic banks perform even better than conventional banks. Most studies on GCC and MENA focus on the determinants and indicators of development and the banking industry growth in general. Uniquely, we further examine GCC banking performance at the individual bank level by incorporating the latest available data.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".