Challenges in Changing Social Norms: Evidence from Interventions Targeting Child Marriage in Ethiopia
Bibliographic record
Abstract
Abstract We study a set of interventions in Ethiopia geared towards eliminating child marriage. The interventions facilitate community discussion about child marriage and provide economic incentives for girls to stay in school and remain unmarried. Changing social norms is often thought of as very difficult, and if there is a marriage penalty to being among the first to deviate to an older age of marriage, raising the typical age at first marriage could be especially challenging. Regardless, using weighting and a difference-in-differences approach, we find that both interventions reduce the probability a girl from 8 to 17 years old has been married by about 4 to 7 percentage points. We observe some positive spillover effects: the program appears to have increased the intra-household decision-making power of women. However, we also find suggestive evidence of increased polarisation in beliefs about child marriage, including some possible backlash especially among men. No robust effects were seen on education outcomes, suggesting that, in contrast to other studies, this was not the mechanism through which the intervention had an effect.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".