Maternal Age and Offspring Human Capital in India
Bibliographic record
Abstract
Early motherhood remains a widespread phenomenon in low- and middle-income countries (LMICs). While the consequences of early motherhood for the mother have been extensively investigated, the impact on their children is severely understudied, especially in LMICs, which host 95% of teen births globally (WHO, 2014). Using panel and sibling data from India, this paper investigates the effect of early maternal age on offspring human capital development in terms of health and cognition, and relies on mother fixed effects to allow for household and mother unobserved heterogeneity. Furthermore, this paper explores the evolution of these effects over time during childhood and early adolescence for the first time. Results indicate that early maternal age has an overall detrimental effect on offspring health and cognition. We show that children born to early mothers are shorter for their age and perform poorer in the math test. Interestingly, the effect on child's heath is observed at early ages and weakens over time, while the cognition effect surges only in early adolescence. The analysis on heterogeneous effects suggests that children and in particular girls born to very young mothers are worst off. The transmission channel analysis tentatively hints at some behavioral channels driving the relationships of interest and documents a positive (and modest) association between height-for-age and subsequent math performance. Overall, our results support both restorative policies assisting children born to early mothers and preventive policies tackling early pregnancy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".