Climate shocks, coping responses and gender gap in human development:Policies and practices from canada, new zealand and the european union
Bibliographic record
Abstract
This study examines the impact of drought on child health and schooling outcomes and investigates the contemporaneous relationship between these two main building blocks of human capital. We merge childlevel longitudinal data from the Ethiopia Rural Socioeconomic Survey (ERSS) with geo-referenced climate data. Our findings from within-child variation estimators reveal that drought has a detrimental impact on the highest grade completed of female children. We show that the negative effect of drought on a female child's completed years of formal schooling is channelled, albeit not entirely, through ill health. Our result is robust to using recursive bivariate estimation with exclusion restriction to correct for biases associated with the endogeneity of child health due to time-varying heterogeneities. Gender bias in the household explains why the direct and mediated schooling effects of drought are concentrated only on female children. We find that households respond to drought-induced income shocks by decreasing the allocation of resources for the medical treatment of an ill female child. Moreover, households also increase the use of female child labour for non-agricultural activities, which is consistent with a disproportionate increase in school absenteeism of older girls during drought. We discuss how gender-responsive policy design and implementation may help alleviate gender inequality in human development in the face of climate change.
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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".