Turning up the heat: Extreme heat and labor implications in West Africa
Bibliographic record
Abstract
We examine the impact of extreme heat on household labor allocation in Ghana, Mali, and Nigeria using earth observation and microdata from Ghana. We find that extreme heat affects household labor in distinct ways with significant cross-country heterogeneities. In Nigeria, extreme heat reduces labor use at the extensive margin but increases labor use at the intensive margin. Notably, child labor rises while adult labor declines at the extensive margin. In Mali, extreme heat leads to an overall increase in household labor, particularly among women and children, whereas Ghana shows minimal impact except for reduced child labor. Both Mali and Nigeria experience decreases in hired labor, animal traction, and associated labor costs under extreme heat exposure. These patterns could be explained by farmers’ adaptive strategies: extreme heat triggers the build-up of pests, weeds, and diseases, which could induce farmers to use more pesticides and engage in manual weeding, which are labor-demanding. Moreover, households rely on climate-resistant crop varieties and cropland expansion, which may require additional labor. These findings underscore the importance of context-specific adaptation strategies and the nuanced effects of extreme heat on rural labor markets in West Africa.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".