Wage gap between immigrants and Canadian-born: The impact of immigration status and post- secondary education
Bibliographic record
Abstract
This paper investigates the issue of the wage gap between recent immigrants and their Canadian-born counterparts, when both groups have attained a post-secondary degree. The study builds on previous work carried out by numerous researchers to understand the environment immigrants face upon landing, and especially immigrants landed within a period of five to ten years. Even though immigrants have on average more years of schooling, the research shows that they earn less than their Canadian-born counterparts. Multiple reasons lead to this disparity, and addressing the issue is a complex task. The goal of this paper is to understand the causes of the wage gap. By understanding some of the major causes, this paper hopes to provide policymakers witha fresh view on this problem and a greater understanding of the subject. Finally, the paper proposes a set of recommendations to equip policymakers to act and reverse the trend of the widening wage gap between immigrants and their Canadian-born counterparts. Keywords: wage gap, immigrant, Canadian-born, immigrant status, human capital, post-secondary education, economic development, public policy, immigration, Canada Cet article analyse l’enjeu de l'écart salarial entre les nouveaux arrivants et leurs homologues nés au Canada qui ont obtenu un diplôme d'études postsecondaires. L'étude s’appuie sur des travaux antérieurs réalisés par de nombreux chercheurs pour comprendre l’environnement et les paramètres liés à l’établissement des nouveaux arrivants, particulièrement ceux arrivés au Canada durant les cinq à dix dernières années. La recherche montre que malgré que les nouveaux immigrants aient en moyenne plus d'années d'études, ils gagnent moins que leurs homologues nés au Canada. Plusieurs raisons expliquent cette disparité, et aborder cet enjeu est une tâche complexe. Le but de cet article est de comprendre les causes de l'écart salarial. En comprenant mieux les principales causes, cette étude espère doter les décideurs d’un nouveau regard sur cette problématique. Enfin, cette étude est accompagnée d'une série de recommandations qui visent à outiller les décideurs à agir pour inverser la tendance à l’accroissement des inégalités salariales entre les immigrants et leurs équivalents nés au Canada. Mots-clés: écart salarial, immigrant, né au Canada, statut d'immigrant, capital humain, éducation postsecondaire, développement économique, politique publique, immigration, Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".