Examining the Gender Productivity Gap among Farm Households in Mali
Bibliographic record
Abstract
Abstract This paper decomposes the gender agricultural productivity gap and measures the factors that influence the gap between male and female agricultural plot managers in Mali. The Oaxaca–Blinder approach and the recentred influence function (RIF) decomposition methodology are applied to a nationally representative survey of Mali. The results show that the agricultural productivity of female plot managers is 20.18% lower than that of male plot managers. Additionally, while more than half (56%) of the agricultural productivity gap is influenced by female-specific structural disadvantages, 44% of the gap is due to an endowment effect. Socio-economic characteristics such as the educational level and age of the plot manager, environmental factors and agricultural production practices, i.e., the differential use of inputs (organic or inorganic fertiliser and improved seeds) and the use of hired female workers seem to affect the female-specific structural disadvantages. To reduce or close the gender productivity gap, the underlying causes of female-specific structural disadvantages must be addressed to enable female farmers to obtain the same returns as men. Traditional means of addressing the gender gap, such as providing education for women in rural areas and facilitating rural women’ access to extension services and improved seeds, can mitigate the endowment deficit. This paper highlights the need to develop a better understanding of the factors influencing the structural disadvantages faced by female farmers in Mali that could feed into the development of more effective policies to address the gender gap in agricultural productivity, improving productivity and gender equity and reducing poverty.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".