Unemployment trap or high job turnover? Ethnic penalties and labour market transitions in Italy
Bibliographic record
Abstract
This article aims at analysing the trajectories of immigrants in the Italian labour market, focusing on yearly transitions from unemployment to employment and vice versa. Regression models show that, controlling for age, educational attainment and region, immigrant workers lose their jobs more often than natives but, once being unemployed they have more probabilities of finding a job than natives. As the probabilities of both transitions can be affected by characteristics of the initial status as well, the two transitions have been analysed separately. For the risk of losing a job, the segregation of immigrants in the secondary labour market seems to be the main reason of their penalization, but also the main reason of their advantage in job seeking, since their unemployment spells are shorter than those of natives, although at the cost of accepting worse working conditions. Analyses are based on the yearly transition matrices of Italian Labour Force Surveys, from 2005 to 2008.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".