Differences in Skill Requirements Between Jobs Held by Immigrant and Native Women Across Five European Destinations
Bibliographic record
Abstract
Abstract We examine whether the requirements of analytical skills and physical strength in jobs held by immigrant women in five major European destinations (France, Italy, Spain, Sweden, and the UK) converge to those of jobs of native-born women in their first ten years in the destination country. To this aim, we combine data from the European Labour Force Survey (2005–2015) of immigrant women arriving in these five countries and information about skill requirements from the Occupational Information Network (O*NET). At arrival, migrants in Spain and France use much less analytical skills, but for the period before the Great Recession, that gap closes relatively fast over time compared to other destinations, particularly in the low end of the skill distribution. Physical strength requirements of immigrant’s jobs increase over time and the gaps open in countries where immigrants depart from relatively more strength-intense jobs, while they close in Spain where immigrants use less strength than natives at arrival. Our estimates are also robust to selection into employment both at the average and across the skill distribution using recent techniques. Since the Labour Force Survey does not have information on wages, we use wage information from the European Union Structure of Earnings Survey to proxy average immigrant-native wage gaps implied by our estimated skill gaps by country.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".