Women's Education: An Important Tool for Birth Reduction? A GMM - Poisson Regression Model Approach
Bibliographic record
Abstract
Burkina Faso is a country with a shallow level of woman’s education. However, it is one of the most fertile countries. This paper analyzes whether the education of women reduces the number of births and the Total Fertility Rate in Burkina Faso. It also predicts the average number of births per woman and the Total Fertility Rate if women were better educated. Using data from the Demographic and Health Surveys, I model the two-stage Generalized Method of Moments (GMM) with the Heckman model and Poisson regression. The results show that the high fertility in Burkina Faso is mainly due to the low level of the woman’s education. The post-primary education increases the age at first birth. The number of births per woman would be decreased in the quarter and the Total Fertility Rate would pass from 5.4 to 3.6 if all women had at least completed the primary school. If all women had at least an incomplete secondary school, the number of births per woman would halve and the Total Fertility Rate would become 2.0. The government would do better to improve the education system allowing a good education for all, especially for women if it intends to reduce fertility.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".