Do Immigrants Catch‐up with the Natives in Terms of Earnings? Evidence from Individual Level Data of Canada
Bibliographic record
Abstract
Abstract This article analyses differences in dynamic transitions into and out of any of the five hourly wage quintiles and quintile zero (unemployed and non‐employed people) between immigrants and natives for the period 1993‐2004. Using Longitudinal Level data from Survey of Labour and Income Dynamics ( SLID ) for men aged 25 to 55, we investigate how unobserved heterogeneity factors and initial conditions may affect individuals’ propensity to stay in or leave any of the wage quintiles. We also consider a dynamic multinomial logit model with the random effects approach. Empirical results show that state dependence exists in all hourly wage quintiles. Moreover, education, experience, marital status, immigrant minority status, and age at immigration are significant factors determining hourly wage differentials between immigrants and natives.
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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 | 0.001 |
| 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 | 0.001 | 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".