Immigrants and Workplace Training: Evidence from Canadian Linked Employer–Employee Data
Bibliographic record
Abstract
Job training is one of the most important aspects of skill formation and human capital accumulation. In this study, we use longitudinal Canadian linked employer–employee data to examine whether white/visible minority immigrants and Canadian‐born emplooyees experience different opportunities in two well‐defined measures of firm‐sponsored training: on‐the‐job training and classroom training. While we find no differences in on‐the‐job training between different groups, our results suggest that visible minority immigrants are significantly less likely to receive classroom training, and receive fewer and shorter classroom training courses, an experience that is not shared by white immigrants. For male visible minority immigrants, these gaps are entirely driven by their differential sorting into workplaces with fewer training opportunities. For their female counterparts, however, they are mainly driven by differences that emerge within workplaces. We find no evidence that years spent in Canada or education level can appreciably reduce these gaps. Accounting for potential differences in career paths and hierarchical level also fails to explain these differences. We find, however, that these gaps are only experienced by visible minority immigrants who work in the for‐profit sector, with those in the nonprofit sector experiencing positive or no gaps in training. Finally, we show that other poor labor market outcomes of visible minority immigrants, including their wages and promotion opportunities, stem in part from these training gaps.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".