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Record W4406295835 · doi:10.5539/ijef.v17n2p55

Bank Specific, Banking Sector, Macroeconomic and Democratic Determinants of Bank Efficiency in CEMAC and WAEMU Countries

2025· article· en· W4406295835 on OpenAlexvenueno aff
Mvono Essono Bertrand, Zomo Yebe Gabriel

Bibliographic record

VenueInternational Journal of Economics and Finance · 2025
Typearticle
Languageen
FieldComputer Science
TopicEconomic Growth and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDemocracyFinancial systemInternational economicsMonetary economicsPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.407

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.218
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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