Comparing Outcomes: The Relative Job-Market Performance of Former International Students
Bibliographic record
Abstract
Canada is increasingly looking to international students as a source of post-secondary tuition revenues and new immigrants. We compare the labour-market performance of former international students (FISs) who studied at Canadian institutions through the first decade of the 2000s to their Canadian born-andeducated (CBE), as well as to their foreign born-and-educated (FBE) counterparts. We find FISs outperform FBE immigrants by a substantial margin, but underperform CBE graduates from similar post-secondary programs. We also find evidence of a deterioration in FIS outcomes relative to both comparison groups. The contribution of our analysis is threefold. First, in comparing FIS and FBE immigrants, we obtain evidence that giving preference to Canadian-educated applicants in the Express Entry immigration system is optimal. Second, in comparing FISs with CBE individuals graduating from similar academic programs, the results are consistent with FISs experiencing job search frictions, discrimination, and language difficulties, thereby requiring better immigrant settlement policies. Finally, with three cohorts of FISs spanning the first decade of the 2000s, we find that there has been a deterioration in the labour-market performance of FISs as post-secondary institutions and governments have reached deeper into foreign student pools to meet their student and immigration demands. We argue that this deterioration is most consistent with a trade-off that has occurred, as the quality and supply of international students has not kept pace with the growth in demand. As Canada moves to increase its reliance on international students, monitoring the relative labour-market performance of FISs is critical
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".