Immigration System, Labor Market Structures, and Overeducation of High-Skilled Immigrants in the United States and Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it