A Stalled Revolution? Change in Women's Labor Force Participation during Child‐Rearing Years, Europe and the United States 1996–2016
Bibliographic record
Abstract
Abstract While women's labor force participation rates (LFPRs) in the United States stalled over the last quarter‐century, European countries exhibited a variety of trajectories. We draw on demographic and gender theories of women's life course to understand changes in women's LFPR during their prime child‐rearing years. We build expectations about how aggregate trends may be driven by shifts in the prevalence of key demographic events such as child‐rearing (i.e., compositional) versus shifts in the association of these events with women's LFP (i.e., behavioral). We use data from the European Union Labour Force Surveys and the US Current Population Survey in Kitagawa–Blinder–Oaxaca decomposition models to decompose trends in women's LFPR from 1996 to 2016 across 18 countries by educational attainment, partnership status, and parental status for women aged 20–44. Compositional and behavioral shifts positively contribute to higher LFPR in most countries, but lower rates in several others. Behavioral change is not widely shared across groups of women. Partnered mothers without college degrees are the main contributors to behavioral change and show the greatest variability across countries. We suggest greater research attention to this “missing middle,” as their LFP is key to understanding change during this period.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".