Human Capital Quality and the Immigrant Wage Gap
Bibliographic record
Abstract
We propose a new methodology for analyzing determinants of the wage gap between immigrants and natives. A Mincerian regression framework is extended to include GDP per capita in an immigrant’s country of birth as a proxy for the quality of education and work experience acquired in that country. In this regard, a central finding is that Canadian immigrants’ returns to schooling and work experience significantly increase with the GDP per capita of their country of birth. The contribution of quality of schooling and work experience to the immigrant wage gap is also examined. It is shown that lower human capital quality completely negates the endowment advantage that immigrants have in the areas of schooling and work experience, so that this factor is key to understanding why they earn less than Canadian natives. Since data on GDP per capita are available for most countries in the world over long periods of time, the proposed methodology can be applied to analyze immigrant wage gaps for a large set of countries for which common statistics on natives and immigrants are available. / Qualité du capital humain et écart salarial entre immigrants et natifs. Nous proposons une nouvelle méthodologie pour analyser les déterminants de l’écart salarial entre les immigrants et les natifs. On ajoute à une régression salariale mincérienne le PIB par habitant du pays de naissance d’un immigrant en tant qu’approximation de la qualité de l’éducation et de l’expérience de travail reçues dans ce pays. À cet égard, un résultat important est que les rendements à l’éducation et à l’expérience des immigrants canadiens augmentent de façon significative avec le PIB par habitant dans leurs pays de naissance. On examine aussi la contribution de la qualité de l’éducation et de l’expérience de travail à l’écart salarial entre immigrants et natifs au Canada. On montre que la faible qualité du capital humain élimine complètement l’avantage des immigrants dans les niveaux d’éducation et d’expérience, ce qui fait que ce facteur est fondamental dans notre compréhension de l’écart salarial. Comme des données sur le PIB par habitant sont disponibles pour la plupart des pays durant de longues périodes, la méthodologie proposée peut être appliquée dans plusieurs pays pour lesquels ilexiste des statistiques salariales sur les natifs et les immigrants.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".