Long Time Out: Unemployment and Joblessness in Canada and the United States
Bibliographic record
Abstract
We compare patterns of unemployment and joblessness between Canada and the U.S. during the Great Recession. Similar to previous findings for the U.S. in Kroft et al. [2016], we document a rise in long-term unemployment in Canada. This increase is not accounted for by changes in the observable composition of the unemployed. We then extend the matching model in Kroft et al. [2016] to exploit the restricted-access panel data from the Canadian Labor Force Survey which contains information on the time since the last job (“joblessness duration”) for both unemployed individuals and non-participants. This allows us to model duration dependence in all labor force flows involving either unemployment or non-participation. To calibrate the extended matching model, we create a new historical vacancy series for Canada based on relative employment in “recruiting industries”, allowing us to construct a monthly Beveridge curve for Canada. We find that the calibrated model matches the time series of unemployment fairly well, but does less well matching non-participation. Our results also indicate that allowing for duration dependence in flows between unemployment and non-participation is crucial for explaining overall levels in long-term joblessness, and that changes in the duration distribution among the unemployed and non-participants contributed less to the deterioration of labor market conditions in Canada, relative to the U.S. In part, this difference comes from the fact that the U.S. recession was much more severe.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".