Effectivité d'un crédit ciblé aux vauvres: le cas des microentreprises rurales du Burkina Faso
Bibliographic record
Abstract
Abstract Strategies to reduce poverty are increasingly incorporating microcredit services into their poverty fighting. However, there is little empirical evidence for activities whose funding would be more beneficial for the poor or on the effects of credit on the recipients' well-being. This article analyzes the effectiveness of credit targeted at rural microenterprises (RME) and revenue sources that are most beneficial for the poor. Results show that consumption attributable to additional RME funds alone can lift 11.25% of the populations of households involved in the processing sector out of poverty; the corresponding figure is 39.83% for those in the handicraft sector, 42.15% for those in the service sector, and 44.1% for those operating in the trade sector. An analysis of the pro-poor character of revenue sources in the rural environment indicates that most RMEs operate in sectors or areas in which revenues benefit the non-poor directly. Nevertheless, the job-creation effects (1.06 permanent direct j...
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".