The effects of interaction between location of birth and location of study on immigrant workers' wages in Canada
Bibliographic record
Abstract
Previous studies have suggested that the wage gap between immigrants and the native-born can be accounted for by human capital factors, including education and work experience and, more importantly, where they are acquired. However, current Canadian economic immigration policies do not consider either a potential immigrant's location of birth or location of study. In this paper, we attempt to study the effects of the interaction between a worker's location of birth and location of study on his or her wage with data from the 2011 National Household Survey. Using both OLS and median regression LAD, performed in STATA, we show that (1) the location of birth is not generally indicative of a workers earning potential; (2) without the interactions, all foreign degrees lead to a lower wage compared with Canadian peers, with a U.S. degree being the least punitive; (3) a U.S. degree would lead to a wage premium for workers from some countries; and (4) when a worker from a nontraditional foreign student source country receives a degree in a culturally and geographically distant location, there is a significant wage premium.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".