Immigration System, Labor Market Structures, and Overeducation of High-Skilled Immigrants in the United States and Canada
Bibliographic record
Abstract
Why do high-skilled Canadian immigrants lag behind their US counterparts in labor-market outcomes, despite Canada’s merit-based immigration selection system and more integrative context? This article investigates a mismatch between immigrants’ education and occupations, operationalized by overeducation, as an explanation. Using comparable data and three measures of overeducation, we find that university-educated immigrant workers in Canada are consistently much more likely to be overeducated than their US peers and that the immigrant–native gap in the overeducation rate is remarkably higher in Canada than in the United States. This article further examines how the cross-national differences are related to labor-market structures and selection mechanisms for immigrants. Whereas labor-market demand reduces the likelihood of immigrant overeducation in both countries, the role of supply-side factors varies: a higher supply of university-educated immigrants is positively associated with the likelihood of overeducation in Canada, but not in the United States, pointing to an oversupply of high-skilled immigrants relative to Canada’s smaller economy. Also, in Canada the overeducation rate is significantly lower for immigrants who came through employer selection (i.e., those who worked in Canada before obtaining permanent residence) than for those admitted directly from abroad through the point system. Overall, the findings suggest that a merit-based immigration system likely works better when it takes into consideration domestic labor-market demand and the role of employer selection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".