Explaining the Deteriorating Entry Earnings of Canada’s Immigrant
Bibliographic record
Abstract
The study explores causes of the deterioration in entry earnings of Canadian immigrant cohorts by estimating an empirical specification that nests a number of competing explanations found in the Canadian literature. To do this, we use the pooled sample of Canadian-born and immigrant men employed full-year, full-time from the complete 20 percent samples of the 1981, 1986, 1991, 1996 and 2001 Canadian Censuses. Our results indicate that no more than one-third of the deterioration can be explained by compositional shifts in the knowledge of an official language, mother tongue and region of origin of recent immigrant cohorts. We also find little or no evidence that declining returns to foreign education are responsible. Roughly one-third of the deterioration appears to be due to a persistent decline in the returns to foreign labour market experience which has occurred almost exclusively among immigrants originating from non-traditional source countries. We are able to explain two-thirds of the overall decline in the entry earnings of Canada’s most recent immigrants without any reference to entry labour market conditions. When we also account for entry conditions, our results suggest that Canada’s immigrants who arrived in the 1995-1999 period would otherwise be enjoying entry earnings that were significantly higher than the entry earnings of the 1965-1969 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".