The Impact of Market Power, Credit Risk, and Economic Environment on the Stability of the Arab Banking Sector
Bibliographic record
Abstract
The paper presents an investigation of the dialectical relationship between banking concentration and the stability of the banking sector, using data from twelve Arab countries for the period 2014-2019 in the framework of a dynamic panel data model. The findings show that the banking sector in the Arab countries follows the "Concentration-Stability" hypothesis. That is, banking concentration has a significant positive impact on bank stability. The paper explains that this result is due to two main reasons. The first reason is that large banks tend to manage their assets and capital more efficiently compared to smaller banks, while the second reason is that systemically important banks (DSIBs) are subject to additional quantitative and qualitative regulatory requirements, especially after the global financial crisis in 2008. The paper also reveals that economic growth has a significant positive effect on bank stability, while credit risk has a significant negative impact on bank stability. The paper suggests encouraging the merger of small banks, as this leads to enhancing their operational efficiency, strengthening their financial positions, and supporting their ability to absorb potential shocks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".