The Economic Performance of Immigrants with Canadian Education
Bibliographic record
Abstract
According to the literature on the economic situation of immigrants, non-recognition of human capital acquired outside of the host country is one of the greatest challenges that immigrants have to overcome when they arrive in Canada. This article exploits data from a Canadian survey conducted in 2005 involving postsecondary graduates from the Class of 2000 in order to determine whether obtaining a Canadian diploma or degree helps eliminate wage gaps between immigrants and native-born Canadians. The advantage of using these data is that they allow for a comparison of groups who were educated under the same education system and entered the labour market at the same time. Essentially, our results show that age at immigration is an important determinant of labour market integration even after obtaining a Canadian diploma or degree. Those who immigrated at a very young age obtained comparable -- and even, in the case of men, higher -- wages than Canadian-born graduates. For immigrants who arrived as adults (i.e., at age 18 or older), the results reveal, all things being equal, a negative wage gap of 17% for men and 5.2% for women, relative to Canadian-born men and women. Econometric analyses also confirm the important impact of source region on the wages of immigrants who arrived as adults. Immigrants coming from Asia, the principal source of immigration to Canada, have a substantial wage disadvantage, in the case of both men and women. To conclude, the fact of returning to school after immigration and obtaining a Canadian diploma or degree does not guarantee that wage gaps with native-born Canadians will be eliminated; however, immigrant women do relatively better than immigrant men.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".