Fertility, Health and Female Labour Force Participation in Urban Cameroon
Bibliographic record
Abstract
Many studies report empirical relationship either between fertility and labour supply or, between health and labour market outcomes. In this paper, an extension of these ideas involves explicitly considering how fertility and health affect each other, and how they interrelate with labour force participation. A unifying framework is provided and a simultaneous three equations model developed to capture the interdependence between these variables as well as their respective determinants. The model is estimated using a cross-section data set obtained from a survey of the urban Cameroon population. The results indicate that: (i) fertility and health status are significantly interrelated, thus separate estimations of fertility (or health status) and participation will produce misleading results; (ii) working in either sector of the labour market significantly reduces fertility but, unlike many previous studies, fertility has a positive impact on the probability of labour force participation; (iii) there is strong evidence that health and disability status is a significant determinant of employment, but the reverse depend on the labour market sector and on the health indicator used.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".