The impact of eliminating primary school tuition fees on child marriage in sub-Saharan Africa: A quasi-experimental evaluation of policy changes in 8 countries
Bibliographic record
Abstract
BACKGROUND: Child marriage harms girls' health and hinders progress toward development goals. Randomized studies have shown that providing financial incentives for girls' education can effectively delay marriage, but larger-scale interventions are needed in light of slow progress toward curbing the practice. Many sub-Saharan African countries eliminated primary school tuition fees over the past two decades, resulting in massive increases in enrolment. We measured the effect of these policies on the probability of primary school completion and of marriage before 15 and 18 years of age. METHODS: We used Demographic and Health Surveys to assemble a dataset of women born between 1970 and 2000 in 16 countries. These data were merged with longitudinal information on the timing of tuition fee elimination in each country. We estimated the impact of fee removal using fixed effects regression to compare changes in the prevalence of child marriage over time between women who were exposed to tuition-free primary schooling and those who were not. RESULTS: The removal of tuition fees led to modest average declines in the prevalence of child marriage across all of the treated countries. However, there was substantial heterogeneity between countries. The prevalence of child marriage declined by 10-15 percentage points in Ethiopia and Rwanda following tuition elimination but we found no evidence that the removal of tuition fees had an impact on child marriage rates in Cameroon or Malawi. Reductions in child marriage were not consistently accompanied by increases in the probability of primary school completion. CONCLUSIONS: Eliminating tuition fees led to reductions in child marriage on a national scale in most countries despite challenges with implementation. Improving the quality of the education available may strengthen these effects and bolster progress toward numerous other public health goals.
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".