International students, immigration and earnings growth: the effect of a pre-immigration host-country university education
Bibliographic record
Abstract
Abstract While destination-country education provides many potential advantages for immigrants, empirical studies in Australia, Canada and the USA have produced mixed results on the labour outcomes of immigrants who are former international students. This study uses large national longitudinal datasets to examine cross-cohort trends and within-cohort changes in earnings among three groups of young university graduates: immigrants who are former international students in Canada (Canadian-educated immigrants), foreign-educated immigrants who had a university degree before immigrating to Canada and the Canadian-born population. The results show that Canadian-educated immigrants on average had much lower earnings than the Canadian-born population but higher earnings than foreign-educated immigrants both in the short run and in the long run. However, Canadian-educated immigrants are a highly heterogeneous group, and the key factor differentiating their post-immigration earnings from the earnings of the Canadian-born population and foreign-educated immigrants is whether they held a well-paid job in Canada before becoming permanent residents. Furthermore, an extra year of Canadian work experience or an extra year of Canadian education experience before immigration added only a small or no earnings gain after immigration for Canadian-educated immigrants. JEL Classification: J15, J24, J61
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".