Credit constraints and soybean farmers' welfare in subsistence agriculture in Togo
Bibliographic record
Abstract
This study assesses the impact of credit constraints on soybean farmers' welfare in subsistence agriculture in Togo. In order to control potential sample selection bias, the endogenous switching regression method was adopted and data collected from a random sample of 500 soybean farmers were used. The results showed that farmers' age, being a member of the soybean organization and selling the soybean to a recognized NGO or to a private organization and growing cotton or cashew are the main determinants of access to full amount of credit. The results show a discrimination against gender in accessing the full amount of credit. Formal education and participating in the extension programs would increase farmers' welfare. Increasing land cultivation would increase women's welfare compared to men. Adopting intercropping technique as conservation agriculture has positive and significant impact on women's welfare. Moreover, having access to the full amount of credit increases soybean production by 1.35% and farmers' revenue by 1.32%, compared to farmers without having access to the full amount of credit. These results suggest the rethinking of the role of agricultural credit in soybean farmers' welfare in the study areas with great attention to the gender dimension.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".