The Impact of Multi-Layer Corporate Governance on Banks’ Performance under the GFC and the COVID-19: A Cross-Country Panel Analysis Approach
Bibliographic record
Abstract
This paper examines the impact of multi-layer corporate governance (MCG) on banks’ performance under the global financial crisis (GFC) and COVID-19. Using a random and fixed effects method, we regressed the impact of MCG variables on return on assets (ROA), return on equity (ROE), and non-performing loans (NPL) of a panel data of 44 conventional banks (CBs) and 40 Islamic banks (IBs), across 17 countries, and over the period from 2006 to 2020. The results show that board of directors (BoD)’ structure has no association with CBs performance whereas the chief executive officer (CEO) duality is strongly negatively impacting CBs performance, especially during the GFC. In addition, supervision framework proxies have a strong positive influence on CBs performance, especially in the period after the GFC. Furthermore, cross-membership and the size of the Shariah board (SB) have a significant negative influence on IBs’ performance, but SB qualification has a positive non-significant impact overall—with the exception of NPLs, which had a positive significant impact during the GFC. The supervision position has a favorable impact on IBs performance except during crises.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".