The Impact of Risk Taking and Institutional Quality on MENA Region Banking Performance
Bibliographic record
Abstract
This study analyzes banking profitability by examining the impact of bank risk taking and institutional quality on the performance of banks operating in the MENA region between 1999 and 2021. Using the generalized method of moments (GMM) panel data estimator, we identify that banking performance is influenced by specific-bank variables, country-level macroeconomic variables, and the quality of institutions. Our findings demonstrate that an increase in the capital requirement ratio and banks' size has a positive impact on the Return on Assets ratio (ROA), while the Non-Performing Loans to Gross Loans ratio (NPL) and Liquidity (LIQ) have a negative effect on banking performance. It is evident that banks under study expand their interest rates in line with economic growth and high inflation rates, negatively influencing banking performance. Additionally, we find that control of corruption and political stability leads to an increase in banking profitability, whereas the rule of law negatively affects banking profitability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".