Female Wage Employment and Fertility in Kenya
Bibliographic record
Abstract
The paper examines the association between fertility and female wage employment in Kenya using nationally representative cross-sectional data collected by the Kenya’s National Bureau of Statistics, a government-owned statistical organization. Two findings emerge from our analysis. The first finding is that female wage employment is negatively correlated with the number of births. Incompatibility of childrearing with wage employment is one of the main explanations for this evidence. The other finding is a much larger magnitude of the negative association between wage employment and male births relative to female newborns, but the difference in the estimated gender-specific coefficients is statistically insignificant. However, there is need for further significance tests on the difference between the gendered coefficients because the larger drop in the number of male births relative to female, as female wage employment expands, has strong support in the biomedical literature. The relevance of the second finding in the context of the biomedical literature on the link between a child’s gender at birth and the environment in which the mother works and lives provides a justification for further research on this issue. The tentative findings of the paper point to labor market policies that could be explored in Kenya and elsewhere in Africa to address the problem of excess fertility, and thus enhance women’s health, agency, and socioeconomic empowerment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".