Are Immigrants’ Pay and Benefits Satisfaction Different than Canadian-born?
Bibliographic record
Abstract
This study contributes to the emerging literature on immigrants’ life, job, and pay satisfaction by focusing on a relatively understudied aspect of the immigrant experience – satisfaction with pay and benefits. The purpose of the study is to first examine whether there are differences in satisfaction with pay and benefits between Canadian-born and immigrant workers, and if so, to then examine factors associated with immigrants’ pay and benefits satisfaction using discrepancy and equity theoretical frameworks. Immigrants are examined in four cohorts based on the year of arrival. We use Statistics Canada’s 2005 Workplace and Employee Survey (WES), which is a large Canadian dataset containing responses from both employers and employees enabling us to control for individual and workplace heterogeneity. Both descriptive and multivariate regression results found that, with the exception of the pre-1965 cohort, all immigrant cohorts report significantly lower pay and benefit satisfaction compared to Canadian-born workers. Further, we find that for Canadian-born workers, external and internal referents, non-wage benefits, and pay-for-performance are positively related to pay and benefit satisfaction, whereas pay-for-output is important for the 1986 to 1995 and 1996 to 2005 immigrant cohorts. We conclude that the lack of consistency in the factors contributing to pay and benefits satisfaction across Canadian-born and immigrant groups suggests that the theories and traditional models for pay and benefit satisfaction may not be as relevant when studying immigrants. We recommend that further studies of a qualitative nature tease out factors associated with immigrants’ pay and benefits satisfaction and contribute to the refinement of existing theories. The results can also assist human resource managers and government policy-makers to facilitate more successful integration and retention of immigrants.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".