Immigrant–non‐immigrant wage differentials in Canada: A comparison between standard and non‐standard jobs
Bibliographic record
Abstract
Abstract It is well established that recent immigrants earn considerably less than their native‐born counterparts even after adjusting for differences in human capital. Another labour market trend has been the growth in non‐standard forms of employment. Since non‐standard forms of work are generally less desirable than standard jobs on a number of dimensions including earnings, this study examines the nexus between immigrant earnings and non‐standard employment to investigate if there is a systemic connection between the two trends. Consistent with earlier research evidence, we find a substantial earnings disadvantage associated with all forms of non‐standard work relative to full‐time, permanent employment. Conditioning on observable characteristics, immigrants are less likely to be employed in full‐time, permanent work. However, when we examine workers in non‐standard jobs, we find that immigrant–non‐immigrant earnings gaps are smaller than those observed among workers in standard jobs. Moreover, the unadjusted mean earnings of long‐term immigrants in part‐time jobs are actually higher than the earnings of similarly employed Canadian‐born workers. Finally, considering immigrants from Western and non‐Western countries, we find that the earnings disadvantage of non‐Western immigrants in non‐standard jobs is smaller than the earnings disadvantage of non‐Western immigrants in standard jobs. These findings suggest that non‐standard jobs provide a point of entry for many new immigrants into the Canadian labour market. But whether these jobs are a bridge to upward mobility or whether they act as traps from which immigrants are unable to escape is a question that needs to be answered with better longitudinal data that track specific cohorts of workers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".