The Effects of Child Care Subsidies on Women’s Economic Opportunities in the Slums of Nairobi
Bibliographic record
Abstract
Studies from North America, Europe, and Latin America show that women's disproportionate child care responsibilities significantly impede their labor force participation.Yet, some have questioned whether similar barriers exist in sub-Saharan Africa, where women primarily work in the informal sector and may receive extensive kin support.To test whether child care obligations limit African women from engaging in paid work, we conducted a randomized study which provided subsidized early child care (ECC) to selected mothers living in a slum area of Nairobi, Kenya.We found that not only are mothers eager to send their children to ECC centers, but also that women who were given subsidized ECC were, on average, 8.5 percentage points (or over 17%) more likely than those who were not to be employed.This effect rose to over 20 percentage points among women who actually used the ECC services.Furthermore, working mothers who were given subsidized ECC were able to work fewer hours than those not given ECC without any loss to their earnings.These findings provide strong evidence that subsidizing child care for women in poor urban settings could be a powerful mechanism to improve female labor outcomes and reduce gender inequalities in Africa.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Dataset About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".