Cyclical Behavior of Unemployment and Job Vacancies: A Comparison between Canada and the United States
Bibliographic record
Abstract
As long as workers do not value their leisure much, the Mortensen-Pissarides search and matching model implies that unemployment and job vacancies would be much less responsive to changes in labor productivity than what we observe in the business cycles of the Canadian labor market. These findings parallel the work of Shimer (2005) for the United States. The combined data from both countries present an additional difficulty for the model. Even if the unobserved value of leisure is allowed to be as high as required to fit the business cycle in the United States or in Canada, as proposed by Hagedorn and Manovskii (2007), another failure arises. The model lacks ability to reconcile the similar labor cycles with the large policy differences in the UI benefits and income taxes in the two countries when the value of leisure is assumed to be the same in both countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".