Entry Earnings of Canada's Immigrants over the Past Quarter Century: the Roles of Changing Characteristics and Returns to Skills
Bibliographic record
Abstract
We examine whether the factors associated with the rise in the Canadian born - immigrant entry earnings gap played different roles in the 1980s, the 1990s, and the early 2000s. We find that for recent immigrant men, shifts in population characteristics had the most important effect in the 1980s when their earnings gap expanded the most, but this “compositional†effect diminished in the 1990s and early 2000s. The effect of changes in returns to Canadian experience and education was small for men, but stronger for women in all three periods. During the early 2000s the IT bust, combined with a heavy concentration of immigrants in IT-related occupations, was the primary explanation of the increase in their earnings gap. Furthermore, returns to foreign experience declined in the 1980s and 1990s, but recovered moderately in the early 2000s. In contrast, the relative return to immigrant education declined in the early 2000s.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".