Seeking Success in Canada and the United States: The Determinants of Labour Market Outcomes Among the Children of Immigrants
Bibliographic record
Abstract
This paper reviews the recent research on labour market outcomes of the children of immigrants in Canada and the United States (i.e., the 2nd generation), and its determinants. The paper focuses on outcome gaps between the 2nd and third-and-higher generations, as well as the intergenerational transmission of earnings between immigrants (the first generation) and their children. Overall, in both Canada and the United States the labour market outcomes of the children of immigrants are positive. On average they have higher levels of education, and similar labour force participation rates and unemployment rates (no controls) as the third and higher generations (i.e. the children with native born parents). Furthermore, the children of immigrants tend to have higher earnings (unadjusted data). The 2nd generation is also more likely to be employed in professional occupations than the 3rd-and-higher generation, reflecting their higher average levels of education, particularly in Canada. However, after accounting for background characteristics, among racial minority groups in Canada the positive earnings gap turns negative. Regarding the determinants of aggregate outcomes, educational attainment may account for up to half of the (positive) earnings gap between the 2nd and third-and-higher generations. Other important determinants of the wage gap include location of residence and community size, ethnic group/source region background, the “degree of stickiness†in educational and earnings transmission between the 1st and 2nd generation, and “ethnic capital†. In both Canada and the United States there are large differences in outcomes by source region/ethnic group background. The U.S the sociological literature in particular focuses on possible “downward assimilation†among children of immigrants with Mexican and other Hispanic backgrounds. In Canada, after controls, the 2nd generation racial minority groups outperform the 3rd plus generation educationally, but the 2nd generation with European and American backgrounds do better in the labour market. Based on the trends in the composition of immigrants since the 1980s, and their correlation with 2nd generation outcomes, the educational and labour market gaps may move in different direction in the two countries in the future; becoming increasingly positive in Canada, and more negative in the U.S.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".