Earnings Mobility of Canadian Immigrants: A Transition Matrix Approach
Bibliographic record
Abstract
This Study examines the earnings mobility of Canadian immigrants using the large IMDB microdata file. We examine earnings transition matrices of immigrants over ten years after landing in Canada for three landing cohorts – 1982, 1988, and 1994. Immigrants also arrive under four separate admission classes: independent economic, other economic, family class, and refugees. The study reports five major empirical findings. First, overall earnings mobility was slightly greater for male immigrant earners than for male workers as a whole in the Canadian labour market, but was considerably greater for female immigrant earners than for all female earners in Canada. But both male and female immigrants over their first decade in Canada were much more likely to experience downward earnings mobility than were all earners of the same gender in Canada. Second, across the four immigrant admission classes, independent economic immigrants have markedly the highest average probability of moving up and the lowest probability of moving down the earnings distribution. Third, overall earnings mobility is slightly higher for female than male immigrants – opposite to the situation for workers as whole in Canada. Fourth, the degree of immigrant earnings mobility declines over immigrants’ first ten post-landing years in Canada as they integrate into the Canadian labour market. Fifth, overall earnings mobility across landing cohorts has shown only minor changes between the 1982 and 1994 cohorts, where the average probability of moving up has significantly increased and the average probability of moving down has significantly decreased. The early 1990s economic recession is seen to have had substantial negative or dampening effects on immigrant earnings mobility for the 1988 landing cohort.
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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 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".