Short-term and long-term distributional consequences of prenatal malnutrition and stress: using Ramadan as a natural experiment
Bibliographic record
Abstract
INTRODUCTION: consequences of fetal environment (in the form of in-utero exposure to Ramadan, the Islamic holy month of fasting), in Indonesia, on birth weights, performance on Raven's Colored Progressive Matrices (CPM), math scores, hours worked and earnings. METHODS: We used quantile regressions to conduct a quantitative comparison of distributional consequences, by gender, of full month exposures to Ramadan in-utero on outcomes of interest. Our data included Muslim children and adults measured during rounds 1 and 4 of the Indonesian Family Life Survey. Our main outcome measures were: birth weights-559 observations (females) and 624 (males); Raven's CPM scores-1693 (females) and 1821 (males) for 8-15 year olds; math test scores-1696 (females) and 1825 (males) for 8-15 year olds; hours worked-3181 (females) and 4599 (males) for 18-65 year olds; earnings-2419 (females) and 4019 (males) for 18-65 year olds. RESULTS: Full month of exposure to Ramadan in-utero led to significant reductions at the 5% significance level that were concentrated in the bottom halves of the outcome distributions: among 8-15 years, lower scores on Raven's CPM tests for females (mean: -9.2%, 10thQ: -19%, 25th Q: -19.4%) and males (mean: -5.6%, 10thQ: -12.5%); lower math scores for females (mean: -8.6%, 25thQ: -15.9%) and males (mean: -8.5%, 10thQ: -13.6%); among females 18-65 years, significant reduction in hours worked (mean: -7.5%, 10thQ: - 26.3%). CONCLUSION: Events during the fetal period have far-reaching consequences for females and males in the lowest (10th and 25th) quantiles of outcome distributions, affecting the 'relatively poor' the most. These results call for caution in interpreting studies on child development that rely on mean comparisons alone.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".