Part-time employment as a way to increase women’s employment: (Where) does it work?
Bibliographic record
Abstract
Part-time employment has repeatedly been proposed as a solution for integrating women into the labor market; however, empirical evidence supporting a causal link is mixed. In this text, we investigate the extent to which increasing part-time employment is a valid means of augmenting women’s labor market participation. We pay particular attention to the institutional context and the related characteristics of part-time employment in European countries to test the conditions under which this solution is a viable option. The results reveal that part-time employment may strengthen female employment in Continental Europe and especially in Southern Europe, where an increase in part-time employment—even if it is demand-side driven—leads to greater employment participation among women. We also discuss some policy implications and trade-offs: Although part-time work can lead to higher numbers of employed women, it does so at the cost of increasing gendered labor market segregation. We analyze data from the European Labor Force Survey (EU-LFS) 1992–2011 for 19 countries and 188 regions and exploit regional variation over time while controlling for time-constant regional characteristics, time-varying regional labor market features, and (time-varying) confounding factors at the national level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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, 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".