The Determinants of the Efficiency of Ivorian Commercial Banks: A Study Using the Non-Parametric Approach
Bibliographic record
Abstract
The objective of this study is to analyze the determinants of banking efficiency in Côte d'Ivoire. To achieve this objective, we used annual data covering the period from 2004 to 2017, for fifteen Ivorian banks. Methodologically, we first used the non-parametric data envelopment analysis (DEA) method to determine the efficiency scores. The results show that Ivorian banks are technically inefficient. Second, we used a Tobit model to identify the determinants of bank efficiency. The Tobit regression identifies return on equity, regulatory capital, size, and credit as the main determinants of the technical efficiency of Ivorian banks. In addition, bank liquidity and ownership, GDP growth rate, and inflation are sources of inefficiency in Ivorian banks. The study recommends that banks manage their resources rationally to finance the economy efficiently. At the level of the monetary authorities, they should ensure that banks apply regulatory standards.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.014 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".