Evaluating the Role of Education as a Birth Control Policy in Burkina Faso: A Propensity Score Weighting Approach
Bibliographic record
Abstract
We use the 2014 round of Burkina Faso’s Demographic and Health Surveys (DHS) to identify and quantify the causal effect of women’s education on their fertility outcomes focusing on two fertility indicators: the total number of children ever born and the age at first birth. However, women's educational attainments may reflect the difference in term of access to schooling or individual characteristics such the family wealth, causing a threat to the empirical identification. In order to achieve consistent estimation, our empirical strategy follows Imbens (2000) and uses the propensity score weighting (PSW) approach to generate an appropriate counterfactual group accounting for education levels. Results from the PSW estimation suggest that education reduces the number of children per woman and delays women’s first birth in Burkina Faso. Hence, promoting girls education is an efficient policy to achieve birth control in Burkina Faso.
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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 | 0.043 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".