Cross-Sectional Time Series Analysis of Associations between Education and Girl Child Marriage in Bangladesh, India, Nepal and Pakistan, 1991-2011
Bibliographic record
Abstract
BACKGROUND: Girl education is believed to be the best means of reducing girl child marriage (marriage <18 years) globally. However, in South Asia, where the majority of girl child marriages occur, substantial improvements in girl education have not corresponded to equivalent reductions in child marriage. This study examines the levels of education associated with female age at marriage over the previous 20 years across four South Asian nations with high rates (>20%) of girl child marriage- Bangladesh, India, Nepal and Pakistan. METHODS: Cross-sectional time series analyses were conducted on Demographic and Health Surveys (DHS) from 1991 to 2011 in the four focal nations. Analyses were restricted to ever-married women aged 20-24 years. Multinomial logistic regression models were used to assess the effect of highest level of education received (none, primary, secondary or higher) on age at marriage (<14, 14-15, 16-17, 18 and older). RESULTS: In Bangladesh and Pakistan, primary education was not protective against girl child marriage; in Nepal, it was protective against marriage at <14 years (AOR = 0.42) but not for older adolescents. Secondary education was protective across minor age at marriage categories in Bangladesh (<14 years AOR = 0.10; 14-15 years AOR = .25; 16-17 years AOR = 0.64) and Nepal (<14 years AOR = 0.21; 14-15 years AOR = 0.25; 16-17 years AOR = 0.57), but protective against marriage of only younger adolescents in Pakistan (<14 years AOR = 0.19; 14-15 years AOR = 0.23). In India, primary and secondary education were respectively protective across all age at marriage categories (<14 years AOR = 0.34, AOR = 0.05; 14-15 years AOR = 0.52, AOR = 0.20; 16-17 years AOR = 0.71, AOR = 0.48). CONCLUSION: Primary education is likely insufficient to reduce girl child marriage in South Asia, outside of India. Secondary education may be a better protective strategy against this practice for the region, but may be less effective for prevention of marriage among older relative to younger adolescents.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".