Gender Gaps in Perceived Start-up Ease: Implications of Sex-based Labor Market Segregation for Entrepreneurship across 22 European Countries
Bibliographic record
Abstract
Although scholars have long recognized the consequences of sex-based labor market segregation for gendered outcomes in conventional wage-and-salary employment, comparatively little is known about the implications for entrepreneurship. We call attention to implications stemming from manifestations at distinct levels of analysis, specifically to the differential structural positions that men and women are likely to occupy as employees and to the degree of sex-based labor market segregation in a country overall. We hypothesize that the gendering of labor market positions will have the first-order effect of reducing women’s likelihood of acquiring entrepreneurship-relevant resources, experiencing entrepreneurial career previews, and being exposed to industry opportunity spaces for launching new firms, which will have the second-order effect of lowering their start-up ease perceptions relative to men’s. We further suggest that this gender gap will widen in societies with more highly sex-segregated labor markets. Data from 15,742 employees in 22 European countries provide strong support for these claims. By demonstrating how pre-entry assessments of entrepreneurship are influenced by gendered employment experiences at the individual level and gendered labor market regimes at the country level, this study lays a foundation for further multilevel research on the relationship between institutionalized labor market practices and entrepreneurial activity.
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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 | 0.003 | 0.005 |
| 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".