Fiscal externalities and underinvestment in early-life human capital: Optimal policy instruments for a developing country
Bibliographic record
Abstract
We study policy instruments to correct inefficiently low investment in maternal nutrition in India, where one-fifth of all births occur. We focus on fiscal externalities: healthier babies become more productive adults, who pay more tax. However, parents do not internalize this externality, which, combined with other distortions, results in mothers weighing too little during pregnancy. We calibrate the first sufficient-statistics policy model for the quantitatively important case of fiscal externalities and maternal nutrition in developing countries. The optimal subsidy is large. Yet, welfare gains are even greater from public investment in state capacity to monitor nutrition, enabling targetted incentives. • We study policy instruments to correct low investment in maternal nutrition in India. • Focus on fiscal externalities: healthier babies become more productive adults. • Parents do not internalize externality; mothers weigh too little during pregnancy. • The optimal maternal nutrition subsidy is large. • Even larger welfare gains from including award for meeting threshold weight.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".