Exploring the Impact of Country Risk on Banking Sector Stability: Evidence from the MENA Region
Bibliographic record
Abstract
This paper examines the impact of country risk on banking sector stability, employing the CAMELS framework, within 13 Middle Eastern and North African (MENA) countries for 1984–2024. The analysis exploits the impact of political, economic, and financial risk dimensions on 102 publicly listed banks using two-way random effects models and one-step dynamic panel data estimations. The findings reflected a significant inverted U-shaped nexus between country risk and the stability of the banking sector, addressing how high-country risk deteriorates banking resilience, whereas low country risk improves it. Political risk has the strongest impact with a similar nonlinear relationship. Conversely, economic and financial risks consistently have reverse linear effects. These findings signify the structural vulnerability of MENA banks to political, economic, and financial turmoil and address the urgent need for robust frames of risk management and fiscal discipline. This investigation extends sovereign risk theory, which explains the ability to maintain financial stability by integrating three core dimensions—political, economic, and financial risk—into a comprehensive empirical model that directly relates them to MENA banking stability and provides crucial insights for banking institutions, policymakers, and regulators in a highly volatile atmosphere.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".