Determinants of Banks’ Technical Efficiency in Loans Allocation: Evidence from Côte d’Ivoire
Bibliographic record
Abstract
The role of the financial sector is widely discussed in the economic literature. As part of this sector, banks in general and commercial banks specifically are at the front line. They are the first channels to finance the economy of a contry. Their funds come mainly from clients deposits. In an academic paper published some years ago, we assessed the efficiency of commercial banks operating in Côte d’Ivoire in converting deposits into loans. Even though the results were edifying, an important question remained: what are the factors that affect technical efficiency scores obtained? The present paper aims at answering this question. Based on a literature review, we identified several variables likely to impact the scores. Those variables are classified into two main groups. On the one hand, there are variables under the direct control of banks; on the other hand, there are variables that cannot be impacted by a given bank in a context of perfect competition. To conduct our study, we run a multiple regression model using Ordinary Least Squares. The dependent variable is the efficiency score. As per the potential explanatory variables, we take methodically some of those found within the literature in light of the context of the Ivorian bank market. The results reveal that bank specific factors are the most recurrent factors explaining variation in technical efficiency scores.
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 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 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".