Bank Specific, Banking Sector, Macroeconomic and Democratic Determinants of Bank Efficiency in CEMAC and WAEMU Countries
Bibliographic record
Abstract
This article aims to accomplish two objectives: first, to measure the efficiency scores of banks in CEMAC and WAEMU, and to identify the factors that have influenced them over the period 2008 to 2022. To achieve these goals, we opted for a modelling framework combining the fixed-effect panel models with the stochastic frontier approach (SFA). Regarding the first objective, our results reveal that banks in the CEMAC and WAEMU countries have consistently operated beneath their optimal production capacity. As for the second objective, the findings suggest that certain bank-specific, banking sector and macroeconomic factors exert positive impact on bank efficiency, while others detract it. A close examination of democracy factors indicate their negative effect on the technical efficiency of CEMAC and WAEMU banks. However, when combining the results of the two zones (CEMAC + WAEMU), control of corruption emerges as the only significant factor contributing to diminished technical efficiency of banks. This study has the merit of presenting valuable empirical evidence to inform strategic decision-making by bankers, banking market regulators and public authorities on measures to improve technical efficiency, resilience and financial soundness within the banking sector.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".