Differences in Agricultural Productivity Among Women and Men on Small-Scale Farms in Senegal: Contributions of Agricultural Innovations
Bibliographic record
Abstract
This study aims to measure the contribution of technological innovations in gender gaps in agricultural productivity in Senegal. This study uses data from the 2018 Annual Agricultural Survey (AAS) conducted under the FAO Integrated Agricultural Survey Program (AGRISurvey). Using the Kitagawa-Oaxaca-Blinder decomposition method, results indicated a 69.6% productivity gap between plots managed by men and those managed by women, with plots managed by women on average more productive than those managed by men. There are two main reasons for this unexpected result. First, women on average cultivate much smaller plots of land, with higher production per hectare cultivated. Second, rainfed rice, which is considered a women’s crop, is a highly productive crop that is often grown on very small plots, especially in southern Senegal and has much higher productivity among women than men. 85.5% of the overall productivity gap observed is explained by endowment effects: characteristics of the plot managers and the plots themselves, and unequal access to resources across women and men. The adoption of certified seeds and the use of chemical fertilizers (NPK, urea, and phosphate) were agricultural innovations associated with the gender productivity gap. The use of certified seeds, fertilizers, and motorized equipment during soil preparation and harvesting are all positively associated with increased agricultural productivity among women and men. Findings suggest increasing women’s access to land and technological innovations could further unleash the productivity potential of Senegalese agriculture.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".