Migration, social stratification, and labor market attainment: An analysis of the ethnic penalty in 12 Western European countries
Bibliographic record
Abstract
This article presents a comprehensive investigation into the socioeconomic integration of migrants across 12 Western European countries, considering their likelihood of employment and socioeconomic status. Using the data from the European Social Survey, the study employs linear regression and probit models to achieve two aims: (a) to quantify the penalty for male and female migrants in terms of employment and socioeconomic status attainment; (b) to assess how the ethnic penalty for men and women changes based on their education and social background of origin. Results reveal that male and female migrants face a penalty in most countries under consideration, albeit with varying degrees of magnitude and characteristics. Migrants in Southern European countries exhibit a trade-off between employment and socioeconomic status attainment, while those in Central-Northern Europe experience a double penalty on both outcomes. Moreover, it emerges that the ethnic penalty in labor market attainment is more heterogeneous across migrants with different educational levels than with different social classes of origin: migrants’ social background of origin affects to a lesser extent their labor market outcomes, if compared with their human capital. Migrants with high education and social origin suffer the largest penalty, due to hurdles in leveraging their educational qualifications and social position. This pattern is particularly evident in Southern Europe, where the socioeconomic integration of migrant workers is characterized by a leveling-down process , pushing them into the lowest strata of the occupational hierarchy regardless of their education and social background.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".