New Canadians Working amid a New Normal: Recent Immigrant Wage Penalties in Canada during the COVID-19 Pandemic
Bibliographic record
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic has exposed and arguably intensified many existing inequalities. This analysis explores the relationship between recent immigrant earnings and the pandemic. Specifically, we attempt to empirically answer the question "Has the COVID-19 pandemic exacerbated (or mitigated) recent immigrant-non-immigrant employment and wage gaps?" We find that the pandemic did not change the labour force activity profile of recent or long-term immigrants. Moreover, the pandemic did not disproportionately disadvantage recent immigrants' earnings. In fact, recent immigrant men who were employed during the COVID-19 crisis experienced a small but statistically significant earnings premium. This was insufficient, however, to overcome the overall earnings discount associated with being a recent immigrant. In addition, we find that the recent immigrant COVID-19 earnings boost is observable only at and below the median of the earnings distribution. We also use Heckman selection correction to attempt to adjust for unobserved sample selection into employment during the pandemic. The fact that COVID-19 has not worsened recent immigrant earnings gaps should not overshadow the large, recent immigrant earnings disparities that existed before the pandemic and continue to exist regardless of the COVID-19 crisis.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".