Multiple jobs? The prevalence, intensity and determinants of multiple jobholding in Canada
Bibliographic record
Abstract
While traditional labour market estimates indicate 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 CAN-WSH and 2019 C-QWEL studies). Almost twenty percent of workers in 2019 report 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 thirty percent higher than in the 2011 CAN-WSH study, multiple jobholders in 2019 were less likely to report longer work hours in secondary employment. Analyses also reveal 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".