What Explains Uneven Female Labor Force Participation Levels and Trends in Developing Countries?
Bibliographic record
Abstract
Abstract Rapid fertility decline, a strong expansion of female education, and favorable economic conditions should have promoted female labor force participation in developing countries. Yet trends in female labor force participation rates (FLFP) have been quite heterogeneous, rising strongly in Latin America and stagnating in many other regions, while improvements were modest in the Middle East and female participation even fell in South Asia. These trends are inconsistent with secular theories such as the feminization U hypothesis but point to an interplay of initial conditions, economic structure, structural change, and persistent gender norms and values. We find that differences in levels are heavily affected by historical differences in economic structure that circumscribe women's economic opportunities still today. Shocks can bring about drastic changes, with the experience of socialism being the most important shock to women's labor force participation. Trends are heavily affected by how much women's labor force participation depends on their household's economic conditions, how jobs deemed appropriate for more educated women are growing relative to the supply of more educated women, whether growth strategies are promoting female employment, and to what extent women are able to break down occupational barriers within the sectors where women predominantly work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".