Earnings Losses of Displaced Workers: Canadian Evidence from a Large Administrative Database on Firm Closures and Mass Layoffs
Bibliographic record
Abstract
Using Statistics Canada’s Longitudinal Worker File, we document short-term and long-term earnings losses for a large (10%) sample of Canadian workers who lost their job through firm closures or mass layoffs during the late 1980s and the 1990s. Our use of a nationally representative sample allows us to examine how earnings losses vary across age groups, gender, industries and firms of different sizes. Furthermore, we conduct separate analyses for workers displaced only through firm closures and for a broader sample displaced either through firm closures or mass layoffs. Our main finding is that while the long-term earnings losses experienced on average by workers who are displaced through firm closures or mass layoffs are important, those experienced by displaced workers with considerable seniority appear to be even more substantial. Consistent with findings from the United States by Jacobson, Lalonde and Sullivan (1993), high-seniority displaced men experience long-term earnings losses that represent between 18% and 35% of their pre-displacement earnings. For their female counterparts, the corresponding estimates vary between 24% and 35%.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".