Catching Up or Falling Behind? Continuing Wealth Disparities for Immigrants to Canada by Region of Origin and Cohort
Bibliographic record
Abstract
This paper investigates wealth disparities among first-generation immigrants using data from the 2012 Survey of Financial Security. We apply logistic and linear regression models to estimate disparities in homeownership and household equivalent net worth by immigrant status, region of origin, and time since arrival. By focusing on immigrant families from different regions who entered Canada at different points in time, this research applies theories related to assimilation, human capital, and structural barriers to wealth. Our findings demonstrate that even though many immigrant families transition into homeownership and grow their wealth over time, certain first-generation immigrant groups continue to experience wealth disparities many years after their arrival to Canada. In particular, immigrant families from African, Asian, and Middle Eastern countries experienced the largest wealth gaps. Cet article examine les disparités de richesse entre les immigrants de première génération en utilisant les données de l'Enquête 2012 sur la sécurité financière. Nous appliquons des modèles de régression logistique et linéaire pour estimer les disparités dans la propriété et valeur nette des ménages équivalente par le statut d'immigrant, la région d'origine, et le temps écoulé depuis leur arrivée. En se concentrant sur les familles d'immigrants de différentes régions qui sont entrés au Canada à différents points dans le temps, cette recherche applique les théories liées à l'assimilation, le capital humain, et les obstacles structurels à la richesse. Nos résultats démontrent que même si de nombreuses familles d'immigrants transition vers la propriété et de croître leur richesse au fil du temps, certains groupes d'immigrants de première génération continuent d'éprouver des disparités de richesse de nombreuses années après leur arrivée au Canada. En particulier, les familles d'immigrants d'Afrique, d'Asie, et les pays du Moyen-Orient ont connu les plus grands écarts de richesse.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".