Trends in child marriage and new evidence on the selective impact of changes in age-at-marriage laws on early marriage
Bibliographic record
Abstract
This study adopts a cohort perspective to explore trends in child marriage - defined as the proportion of girls who entered first union before the age of 18 - and the effectiveness of policy changes aimed at curbing child marriage by increasing the minimum legal age of marriage. We adopt a cross-national perspective comparing six low- and middle-income countries (LMICs) that introduced changes in the minimum age at marriage over the past two decades. These countries belong to three broad regions: Sub-Saharan Africa (Benin, Mauritania), Central Asia (Tajikistan, Kazakhstan), and South Asia (Nepal, Bhutan). We combine individual-level data from Demographic and Health Surveys and Multiple Indicator Cluster Surveys with longitudinal information on policy changes from the PROSPERED (Policy-Relevant Observational Studies for Population Health Equity and Responsible Development) project. We adopt data visualization techniques and a regression discontinuity design to obtain estimates of the effect of changes in age-at-marriage laws on early marriage. Our results suggest that changes in minimum-age-at-marriage laws were not effective in curbing early marriage in Benin, Mauritania, Kazakhstan, and Bhutan, where child marriage showed little evidence of decline across cohorts. Significant reductions in early marriage following law implementations were observed in Tajikistan and Nepal, yet their effectiveness depended on the model specification and window adopted, thus making them hardly effective as policies to shape girls' later life trajectories. Our findings align with existing evidence from other countries suggesting that changes in age-at-marriage laws rarely achieve the desired outcome. In order for changes in laws to be effective, better laws must be accompanied by better enforcement and monitoring to delay marriage and protect the rights of women and girls. Alternative policies need to be devised to ensure that girls' later-life outcomes, including their participation in higher education and society, are ensured, encouraged, and protected.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".