Understanding the Economic Integration of Immigrants: A Wage Decomposition of the Earnings Disparities between Native-Born Canadians and Recent Immigrant Cohorts
Bibliographic record
Abstract
This study assesses whether characteristics relating to ethnic identity and social inclusion influence the earnings of recent immigrants in Canada. Past research has revealed that relevant predictors of immigrant earnings include structural and demographic characteristics, educational credentials and employment-related characteristics. However, due to the unavailability of situational and agency variables in existing surveys, past research has generally been unable to account for the impact of such characteristics on the economic integration of immigrants. Drawing on data from Statistics Canada's Ethnic Diversity Survey, this paper builds on previous research by identifying the relative extent to which sociodemographic, educational and ethnic identity characteristics explain earnings differences between immigrants of two recent cohorts and native-born Canadians. The results indicate that immigrants are disadvantaged in the labor market in terms of characteristics relating to sociodemographics and ethnic identity, but are advantaged in terms of human capital.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".