Gender differences in agricultural productivity in Côte d'Ivoire: Distribution, drivers, and changes over time
Bibliographic record
Abstract
Abstract This paper analyzes changes in agricultural productivity gender gaps in Côte d'Ivoire between 2008 and 2016 using decomposition methods. The analysis finds that the gender gap went from 40% in food crops and 17% in exports crops in 2008, to 19% in food crops and a statistical zero in export crops in 2016. The overall gender gap decreased by 15 percentage points over this period, and is statistically insignificant in 2016 once accounting for whether households farm export crops. Moreover, our results show that while some drivers of the gender gap remain stable across the decade (including total land cultivated, and pesticide and fertilizer use), others change their contribution (number of plots, crop choice and household labor). Despite substantial improvements, female‐headed households in the bottom half of the distribution remain disadvantaged. Our results indicate that strengthening women's access to agricultural labor and adoption of export crops are policy priorities to reach gender parity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".