Precarious Employment: Labour Market Insecurity among Immigrants in Canada
Bibliographic record
Abstract
Using the Canadian Labour Force Survey (2006-2012), I examine the prevalence of and trends in precarious employment, comparing recent and established immigrants to their Canadian-born counterparts. I define precarious employment as holding jobs that involve involuntary part-time work, temporary contracts, or multiple job holding. Although current Canadian labour market research views precarious work as a vital part of that country’s employment outlook (see Krahn, 1995; Vosko et al, 2003; Goldring, 2009; Kapsalis and Tourigny 2004; Zeytinoglu and Cooke 2005) just one Canadian study specifically examines immigrants employed in this type of work (Godin and Renaud, 2005). As a result, my research is unique in examining immigrant participation in non-standard work within Canadian labour markets offering an interdisciplinary approach combining perspectives from sociology and economics. The findings of my research indicate that recent immigrant males and females are overrepresented in involuntary part-time work and this trend is increasing over time. This trend also exists for established immigrants although to a lesser extent. I also observed that recent immigrant women are nearly twice as likely as Canadian-born women to be employed in temporary jobs, net of the controls. For the purposes of further investigation of precarious work among immigrants I expand my research by running multilevel models at individual and CMA levels and find employment in temporary jobs and multiple jobs by both recent and established immigrant males is affected by a CMA’s median hourly earnings as well as the immigrant representation in a CMA. In addition, cross-level interactions reveal that recent male immigrants less likely to be employed in multiple jobs in CMAs in which the median wage is higher. Finally, I use OLS regression to focus on how these types of jobs may lead to lower earnings among these newcomers. I find that recent immigrants are struggling financially due to wage disparities in part created by precarious employment. Both males and females experience an initial earnings disadvantage that is further exacerbated by being employed in involuntary part-time work, temporary work and multiple jobs. Overall, my findings suggest that some form of government intervention is needed through immigration policy reform if Canada values the economic integration of its large immigrant population.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 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) | 0.003 | 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".