Bridges or Barriers? The Relationship between Immigrants’ Early Labor Market Adversities and Long-term Earnings
Bibliographic record
Abstract
Using data from the Extended Longitudinal Survey of Immigrants to Canada (LSIC-IMDB), this article investigates the association between early adverse labor market experiences in the host country and immigrants’ long-term earnings. We use Growth Curve Modeling (GCM) to estimate how months of joblessness, part-time status, and occupational mismatch during the first four years in Canada relate to immigrant men’s and women’s earnings trajectories over the following 10 years. Part-time employment, we find, is negatively associated with long-term earnings trajectories for both male and female immigrants, and male immigrants who are occupationally mismatched in the medium term also face a long-term wage penalty. Months of joblessness early on, however, is associated with relatively less wage disadvantage in later years. Since immigrants’ early difficulties are associated with long-term economic scarring, it is imperative to introduce early interventions to promote rapid assimilation.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".