Income Trajectories of Latin American Refugee and Non-Refugee Immigrant Workers in Canada
Bibliographic record
Abstract
Monitoring labour market outcomes of immigrants such as their earnings over time is crucial to pinpointing a successful economic integration. Over the past decades, thousands of Latin American immigrants have been admitted to Canada as permanent residents. Using a sample of tax filers drawn from the Longitudinal Immigration Database (IMDB), this study explored the income trajectories of 60,060 male and female Latin American refugee and non-refugee workers aged 25-54, who immigrated during the period from 2000-2009. Employment earnings of male and female Latino workers were observed at three tax reporting years: 2010, 2014, and 2018. Six immigrant intake class groups were examined: economic class principal applicants, economic class spouses or dependents, family class, government-assisted refugees (GARs), privately sponsored refugees (PSRs) and landed-in-Canada (LICs) refugees. The study found that, between 2010 and 2018, the average employment earnings of workers grew by approximately one quarter of their initial amount. Notable income improvements, however, were not seen across the board. Economic class principal applicants, as well as their spouses and dependents, had the strongest earning trajectories while landed-in Canada refugees and family class immigrants displayed moderate ones. Government-assisted refugees and privately sponsored refugees ranked at bottom levels across the three tax year observation points, having the lowest starting points and the shallowest earning trajectories. Multivariate analysis using cross-classifications found that, controlling for other covariates such as gender, university education and/or region of admission, immigrant intake class was a strong predictor of employment incomes. Although average incomes increased over time for all groups, government-assisted refugees and privately sponsored refugees experienced the greatest income penalties of the six immigrant intake classes examined. The march towards economic integration, thus, appears to be faster for some Latino immigrant workers and slower for others.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".