Human capital valuation of vulnerable visible minority immigrants in Atlantic Canada during a COVID-19 year: evidence from a sample of the CERB recipients
Bibliographic record
Abstract
Purpose This study analyzes returns to education and post-schooling labour market experiences of visible minority immigrants in Atlantic Canada, in comparison to the national labour market in Canada, at the outbreak of COVID_19 pandemic in 2020. It also compares the earnings of visible minority immigrants with non-visible minority immigrants. A review of government policy initiatives to attract and retain immigrants in Atlantic Canada is also undertaken to provide the context. Design/methodology/approach After reviewing some major government policy initiatives in Atlantic Canada to attract and retain immigrants, the study presents some descriptive statistical analysis, followed by an econometric analysis based on the estimation of a human capital earning function using microdata obtained from the 2021 Census Public Use Microdata File. Findings Deliberate government policy measures, that also support community initiatives, have resulted in rising numbers of new immigrant arrivals settling in smaller provinces of Canada, such as those in Atlantic Canada. The labour market returns to human capital of immigrants in Atlantic Canada were not different from those nationally in 2020 which was the year when COVID-19 virus outbreak took place. Earning disadvantage of visible minority immigrants was lower in Atlantic Canada than nationally. Labour market in Atlantic Canada continued to complement the roles of governments and communities in regionalization of immigration. Research limitations/implications This study could not compare recipients of Canada Emergency Relief Benefit with non-recipients. Practical implications The complementarity of the roles of labour markets, governments and community organizations in the attraction and retention of immigrants in smaller areas of host nations. Social implications The economic integration of newcomers in non-traditional destinations of immigrants and visible minorities. Originality/value This is original research conducted by the authors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".