Multiple jobs? The prevalence, intensity and determinants of multiple jobholding in Canada
Bibliographic record
Abstract
While traditional labour market estimates indicate that there has been little change in the proportion of workers holding multiple jobs in North America, survey instrument deficiencies may be hiding more substantial growth driven by the gig economy. To address this possibility, I test a broader measure of multiple jobholding to examine its prevalence in the Canadian workforce based on two national studies of workers (2011 Canadian Work Stress and Health Study and 2019 Canadian Quality of Work and Economic Life Study). Almost 20% of workers in 2019 reported multiple jobholding – a rate that is three times higher than Statistics Canada estimates. While multivariate analyses reveal that the multiple jobholding rate in 2019 was 30% higher than in the 2011 Canadian Work Stress and Health Study, multiple jobholders in 2019 were less likely to report longer work hours in secondary employment. Analyses also revealed that having financial difficulties is consistently associated with multiple jobholding in 2011 and 2019. Collectively, these findings suggest that while the spread of short-term work arrangements has facilitated Canadians’ secondary employment decisions, for many workers these decisions may reflect underlying problems in the quality of primary employment in Canada, rather than labour market opportunity. I discuss the potential links between multiple jobholding, the gig economy and employment precariousness. JEL Code: J21
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".