Unemployment Compensation and Adjustment Assistance for Displaced Workers: Policy Options for Canada
Bibliographic record
Abstract
This paper examines the role of EI in providing support to “displaced workers, †those who permanently lose their jobs because of changing circumstances. Adjusting to change benefits Canadians as a whole. However, some workers suffer much more from job loss than do others. Those who have held their jobs for an extended period experience substantial earnings losses, while those who have been employed for brief periods experience small losses. Like other job losers, long-tenure displaced workers experience earnings losses due to reduced income during unemployment following displacement. However, unlike other job losers, many long-tenure displaced workers become re-employed at significantly lower wages. EI does not take into account these consequences of job loss. Long-tenure displaced workers constitute a small minority of job losers. My analysis indicates that job losers with 5 or more years of job tenure constitute about 5% of unemployment and 15-20% of permanent job losers. The paper makes several policy recommendations. Some address gaps in research and knowledge, while others recommend enhanced EI benefits for those who suffer greatly from job loss. Since most loss from displacement occurs after reemployment, wage insurance seems the most promising approach for insuring against large losses.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".