Labour Market Flows and Worker Trajectories in Canada During COVID-19
Bibliographic record
Abstract
We use the confidential-use files of the Labour Force Survey (LFS) to study the employment dynamics in Canada from the beginning of the COVID-19 pandemic through to mid-summer. Using the longitudinal dimension of this dataset, we measure the size of worker reallocation and document the presence of high labour market churning, that persists even after the easing of social-distancing restrictions. As of July, many of the recent job losers - especially those who had been temporarily laid-off between February and April - have regained employment. However, this apparent strong recovery dynamics hides important heterogeneity, and large groups of workers, such as those who were not employed prior to the pandemic, face important difficulties with finding a job. Three factors appear to be key in accounting for the incomplete employment recovery of July: (1) the unusually high separation flows that characterize the labour market in the reopening phase; (2) the low reemployment probability of recent job losers who were classified as out of the labour force during the lockdown; and (3), the low job-finding rate of individuals who were out of work prior to the pandemic. Our results further suggest that gross job losses were higher among women and young workers during the shutdown and that older workers were more likely to leave the labour force when the economy reopened.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".