Changes in the Prevalence of Nonstandard Employment during the COVID-19 Pandemic
Bibliographic record
Abstract
This paper addressed two research questions related to employment throughout the COVID-19 pandemic. First, how did the prevalence of different types of nonstandard employment change before and during the COVID-19 pandemic? Second, how did these changes differ by gender, immigration status, and age group? These questions are important to understanding how economic uncertainty and downturn may impact the types of employment that workers enter and who is impacted. This study pools together 10 Canadian Labour Force Surveys from May 2017 to November 2021 and employs a multivariate linear regression analysis to answer the previously stated research objectives. Within these regression models, we examined the likelihood of entering temporary employment, part-time employment, and nonstandard self-employment before and throughout the pandemic. We also ran several interaction models to test whether changes to different types of nonstandard employment differed by sex, immigration status, and age. These interactions tested whether the likelihood of nonstandard employment differs by each demographic group before and during the pandemic. The findings demonstrate that the COVID-19 pandemic differed from previous economic crises in its impact on nonstandard employment. The main finding was that rates of nonstandard wage work (temporary and part-time employment) decreased during the first initial lockdown and returned to pre-pandemic levels by the end of 2020. Meanwhile, own-account and part-time self-employment increased during the first wave of the pandemic. During the first few months of the pandemic, the rate of nonstandard employment had a narrower gender gap and a wider immigrant/non-immigrant gap. There is also some evidence that the nonstandard self-employment rate increased among immigrants and women during the first few months.AbstractThe COVID-19 pandemic has drastically impacted employment across Canada. While several reports show an increase in job loss and unemployment, there is little mention of changes in types of employment during the pandemic. Drawing on the Canadian Labour Force Surveys from 2017-2021, this article explored how the pandemic affected nonstandard employment rates while examining whether these impacts differed by certain sociodemographic variables. Namely, differences in rates of nonstandard employment were explored by gender, immigrant status, and age group. The main finding was that rates of nonstandard wage work (temporary and part-time employment) decreased during the first initial lockdown and returned to pre-pandemic levels by the end of 2020. Meanwhile, own-account and part-time self-employment increased during the first wave of the pandemic. While these increases were uniformly experienced across different groups of workers, there is some evidence of widening or narrowing gaps in rates of nonstandard employment depending on the sociodemographic group.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".