Measurement and Drivers of Financial Inclusion in Cote d’Ivoire: A Case Study of Oil Palm and Rubber Tree Producers in Sud-Comoé Region
Bibliographic record
Abstract
This study uses data collected by ENSEA within the framework of the Agricultural Sector Support Project in Côte d'Ivoire (PSAC) carried out in 2015 to measure, firstly, the financial availability, use and quality of financial inclusion of farmers in the rural region of Sud-Comoé of Côte d'Ivoire and, secondly, to examine the factors that influence their choices in terms of financial inclusion. To this end, we construct indices for each of the three dimensions (access, use and quality) and a synthetic financial inclusion index. The results of the univariate analysis reveal that farmers in the zone are poorly financially included in terms of the "use" and "quality" dimensions, and that the overall financial inclusion situation of farmers in the Sud-Comoé is low (33% of farmers are financially included). To achieve the second objective, we used a logit model. The empirical results indicate that farmers with no schooling and fewer qualifications are more likely to be financially excluded; that the high cost of banking services and the low income of farmers limit their financial inclusion and, finally, that the experience acquired by farmers on the farm and the savings products offered by financial institutions are the factors that stimulate the financial inclusion of farmers. In addition, the education variable partly explains why financial inclusion is more frequent among rubber tree farmers than among oil palm farmers. In view of these results, economic choices and decisions must be targeted according to these empirical findings to increase the level of financial inclusion of producers, the true pillars of the economy of Côte d’Ivoire.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".