How Much Does Birth Weight Matter for Child Health in Developing Countries? Estimates from Siblings and Twins
Bibliographic record
Abstract
200 million children globally do not meet their potential for growth, and suffer the consequences in terms of future health, education and earnings. There is a well-established literature on the effects of in utero environment on later health in the US and Europe; however, there is less research on the most at risk populations in developing countries. This paper provides evidence on the effects of birth weight on subsequent health using information on over a million children in 72 countries from the Demographic and Health Surveys. I account for missing data and measurement error using instrumental variables, and also adopt an identification strategy based on sibling and twin models to control for potential omitted variable bias. I find a consistent effect of birth weight on risk of death, stunting, wasting, and coughing, with some evidence for fever, diarrhoea and anaemia. Results imply that focusing solely on reducing mortality, and not on improving infant health more broadly, may be missing the opportunity to build the health capital and life chances of those affected. Investments in the status and health of women are likely to have long run returns in terms of the health and productivity of their children.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".