Trends in Immigrant Overeducation: The Role of Supply and Demand
Bibliographic record
Abstract
Abstract This study asks whether recent immigrants and Canadian‐born youth have become increasingly overeducated for their jobs because of changes in the supply of university‐educated workers and demand for their human capital. Based on analyses of four Canadian censuses, the study found that over the 2001–2016 period, only about one‐half of the growth in the supply of university‐educated workers was matched with growth in jobs that required a university degree. Recent immigrants bore most of the brunt of this structural imbalance, becoming more concentrated in low‐ and medium‐skill jobs. In comparison, the prevalence of education‐occupation match of young Canadian‐born workers increased over this period. Consequently, the gap in overeducation between these population groups has widened. To address this gap, immigration policy should use a tandem process that selects immigrants based on human capital and demand‐driven criteria.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".