Immigrant Earnings Differences Across Admission Categories and Landing Cohorts in Canada
Bibliographic record
Abstract
This study uses longitudinal IMDB micro data to document the annual earnings outcomes of Canadian immigrants in four major admission categories (skill-assessed independent economic principal applicants, accompanying economic immigrants, family class immigrants, and refugees) and three annual landing cohorts (those for the years 1982, 1988, and 1994) over the first ten years following their landing in Canada as permanent residents. The findings provide a ten-year earnings signature for the four broad immigrant admission categories in Canada. The study’s first major finding is that skill-assessed economic immigrants had consistently and substantially the highest annual earnings levels among the four admission categories for both male and female immigrants in all three landing cohorts. Family class immigrants or refugees generally had the lowest earnings levels. An important related finding is that refugees exhibited substantially the highest earnings growth rates for both male and female immigrants in all three landing cohorts, while independent economic or family class immigrants generally had the lowest earnings growth rates over their first post-landing decade in Canada. The study’s second major finding is that economic recessions appear to have had clearly discernible negative effects on immigrants’ earnings levels and growth rates; moreover, these adverse effects were much more pronounced for male immigrants than for female immigrants.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".