Effect of Anchor Borrowers' Programme on the Income of Smallholder Maize Farmers in Kwara State, Nigeria
Bibliographic record
Abstract
The paper investigated the effect of the Anchor Borrowersˈ Credit Scheme on the income of the smallholder maize farmers using the survey data obtained from 120 maize farmers in Kwara State, Nigeria. Data was analysed using descriptive statistics, Propensity Score Matching (PSM), Average Treatment effect on Treated (ATT), and Ordinary Least Square regression. Employing descriptive statistics, Farmers were characterised based on their socioeconomic attributes. Using the Propensity Score Matching (PSM) and Average Treatment effect on Treated (ATT), it was discovered that the scheme had a positive and significant effect on the income of the maize farmers, although this effect was the same among all beneficiaries of the scheme. The Ordinary Least Square regression was used to check for the differential effect of the scheme among the benefiting farmers and was found that the positive effect varies with the socioeconomic attributes of the farmers. The results showed a general improvement in the income of the farmers as a result of the scheme. However, the effect of the improvement was higher on beneficiaries with higher educational level, more farming experience as well as beneficiaries with larger farm size. The study findings provided documentation for policymakers for improving the delivery system of the scheme.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".