Responses in child health to concerted reconstruction efforts in the aftermath of the 2016 earthquake in Ecuador
Bibliographic record
Abstract
The paper explores the short-term impact of recovery in the aftermath of a natural disaster on a set of child health outcomes. We analyze the impacts of a major earthquake with a magnitude of 7.8 that occurred on the coast of Ecuador on April 16, 2016. As damage was geographically concentrated, affected infrastructure and individuals could be readily identified. We implement a quasi-experimental difference-in-difference (DiD) strategy with geo-referenced data to compare affected and non-affected children, which is complemented by the event study approach, inverse probability weighting, placebo tests, and a synthetic DiD, which together provide a robust empirical framework identifying that the quick and large response of the government compensated for the earthquake-induced destruction. Affected children aged 0-5 years show similar levels of nutrition (weight-, height-, and BMI-for-age) and anemia as non-affected children. There is even some indication from the heterogeneity analyses (birth cohorts) that weight-for-age might have improved after the disaster and in response to the concerted reconstruction. We present exploratory evidence suggesting that the reconstruction activities led to infrastructure improvements, that nutritional programs continued their operations, and mothers in affected areas engaged more in breastfeeding. All three channels are likely to contribute to the stable child health environment in earthquake-affected areas relative to the non-affected areas, suggesting that mitigation of negative health consequences for the weakest members of society, i.e., the children, is possible in the aftermath of a natural disaster if appropriate activities and policies are put in place.
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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".