Unions and hazard pay for COVID‐19: Evidence from the Canadian Labour Force Survey
Bibliographic record
Abstract
In this article, we examine whether (and by how much) workers in Canada have been compensated for the 'novel' risks associated with COVID-19. We create a unique dataset from a system that scores occupations in the US O*NET database for COVID-19 exposure. We then combine those COVID exposure scores with Canadian occupational data contained in the Public Use Microdata File of the Labour Force Survey. This allows us to categorize Canadian occupations based on COVID-19 exposure risk. We find a long-tailed distribution of COVID-19 risk scores across occupations, with most jobs at the lower end of the risk spectrum and relatively few occupations accounting for most of the high COVID-19 exposure risk. We find that workers who are already more vulnerable in the labour market (i.e. youth, women and immigrants) are also more likely to be employed in occupations with high COVID-19 exposure risk. When we look at the relationship between high-COVID exposure risks in occupation and wages, we find negative compensating differentials both at the mean (negative 8%) and across the earnings distribution. However, when workers are covered by a union, they enjoy a sizeable hazard pay premium (11.7% on average) as compared to their non-union counterparts. Furthermore, we find that the moderating effects of unionization for workers at high risk of COVID exposure to be largest at the bottom of the earnings distribution (i.e. the 10th percentile of unionized earners receives a 12.3% risk premium for high-COVID exposure, whereas the 90th percentile receives only a 2%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".