The Short-Term Economic Consequences of COVID-19: Occupation Tasks and Mental Health in Canada
Bibliographic record
Abstract
In this paper, we document the short-term impact of COVID-19 on labour market outcomes in Canada. Following a pre-analysis plan, we investigate the negative impact of the pandemic on unemployment, labour force participation, hours and wages in Canada. We find that COVID-19 had drastic negative effects on labour market outcomes, with the largest effects for younger, not married, and less educated workers. We investigate whether the economic consequences of this pandemic were larger for certain occupations. We then built indices for whether (1) workers are relatively more exposed to disease, (2) work with proximity to coworkers, (3) are essential workers, and (4) can easily work remotely. Our estimates suggest that the impact of the pandemic was significantly more severe for workers more exposed to disease and workers that work in proximity to coworkers, while the effects are significantly less severe for essential workers and workers that can work remotely. Last, we rely on a unique survey, the Canadian Perspective Survey, and show that \nreported mental health is significantly lower among the most affected workers during the pandemic. We also find that those who were absent form work because of COVID-19 are more concerned with meeting their financial obligations and with losing their job than those who remain working outside of home, while those who transition from working outside the home to from home are not as concerned with job loss.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".