Misplaced Talent: The Rising Dispersion of Unemployment Rates
Bibliographic record
Abstract
Canada’s labour market is doing remarkably well, judging by the nationwide numbers. In June 2006, the employment rate stood at a near-record 63.1 percent, and the unemployment rate was at a 32-year low of 6.1 percent. Wages continue to rise faster than inflation. For the 12-month period ended in June, the average hourly wage was up 3.5 percent, well in excess of the most recent 2.8 percent increase in the consumer price index. The red-hot labour market is a result of sustained economic growth in Canada that has outpaced all other G-7 countries since 1997. However, the rosy nationwide numbers hide severe local disparities in labour market performance. While the unemployment rate in south-central Manitoba, for example, stood at an impressive 2.2 percent in March 2006, not far away in northwest Ontario, unemployment was much higher at 8.3 percent. In Quebec’s Gaspésie region, the rate remained a discouraging 22.7 percent! In a well-functioning labour market, we would expect to see limited variation among regions as people move to where the jobs are. Not only are there permanent gaps between regional unemployment rates and the national average, but these gaps have also widened as overall countrywide unemployment came down during the past decade. This seems contrary to what one might expect to see during an economic boom, and it suggests that the boom has been milder than it could have been if Canada were realizing its full economic potential. In this e-brief we examine both the extent of the problem and its primary cause, which can be traced to policy flaws in the Employment Insurance (EI) system.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".