Bibliographic record
Abstract
ABSTRACT We estimate the impact of temperature shocks on the composition of farm labor in rural Nigeria using a nationally representative household panel survey. Leveraging plausibly exogenous year‐to‐year variation in growing season temperatures, we find that warmer temperatures significantly alter farm labor composition, prompting a substantial shift away from hired labor toward family labor. Interestingly, the displaced hired labor is not easily absorbed into non‐farm sectors in the short term; instead, high temperatures also reduce household participation in local non‐farm wage employment. We further provide suggestive evidence that households reallocate labor in response to temperature shocks because extreme heat renders reliance on external labor economically less viable. In particular, heat stress decreases farm productivity, lowering marginal returns to labor and incentivizing farmers to substitute costly hired labor with household labor. These findings underscore the multifaceted threat that climate change poses to rural livelihoods, reducing not only crop yields but also distorting labor allocation in ways that may further constrain farm productivity.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".