The Development Benefits of Maternity Leave
Bibliographic record
Abstract
Within developing countries, studies addressing the effects of maternity benefits on fertility, infant/child health, and women’s labor force participation are limited and provide contradictory findings. Yet, knowledge regarding the implementation of maternity provisions is essential, as such policies could significantly improve women and children’s well-‐being. We add to this literature by using fixed effects panel regression from 1999 through 2012 across 121 developing countries to explore whether different types of maternity leave policies affect infant/child mortality rates, fertility, and women’s labor force participation, and whether those effects are shaped by disparities in GDP per Capita and Secondary School Enrollment. Our findings demonstrate: 1) both infant and child mortality rates are expected to decline in countries that institute any leave policy, policies that last 12 weeks or longer, and policies that increase in duration and payment as a percentage of total annual salary, 2) fertility is expected to decline in countries that have higher weekly paid compensation, 3) maternity leave provisions decrease fertility and infant/child mortality rates most in countries with lower GDP per capita and countries with middle range secondary enrollment rates, and 4) labor force participation does not increase. Our results suggest that policy makers must consider the duration, compensation, and goals (addressing fertility versus mortality rates) of a policy alongside a country’s economic development and secondary school enrollment when determining which maternity leave provisions to apply within developing-‐country contexts.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".