Job-Education Mismatch and Its Impact on the Earnings of Immigrants: Evidence from Recent Arrivals to Canada
Bibliographic record
Abstract
Using the most recent Longitudinal Survey of Immigrants to Canada, this paper measures the incidence of job-education mismatch, particularly over-education, examines its determinants, and estimates its impact on the earnings of immigrants. Job-education mismatch is measured using the realized match method, and the corresponding earnings impact is estimated using an over-required-under education technique. Determinants of over-education are examined using a bivariate probit model to account for selectivity into employment. Panel data estimation methods are used to estimate earnings equations and the analysis is stratified by gender. Results show that recent immigrants to Canada have a persistent high incidence and intensity of over-education with a substantial negative impact on their earnings. In particular, two-thirds of recent immigrants to Canada are over-educated with a wage loss of 8%, while an under-educated immigrant loses around 2% on average. Results also show that proficiency in English or French and post-immigration investment in education and training significantly reduce the likelihood of being over-educated. The findings of this study could benefit policies directed to help immigrants integrate in the labour market.
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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 | 0.001 | 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) | 0.001 | 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 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".