Measuring Labour Markets in Canada and the United States 2011
Bibliographic record
Abstract
This study examines the performance of labour markets in Canada and the United States based on a number of factors that help identify healthy, high-performing labour markets. The first section, an index of labour market performance, measures the 60 jurisdictions across five indicators from 2006 to 2010: average total employment growth, average private-sector employment growth, average unemployment rate, average duration of unemployment, and average labour productivity. These five indicators yield an overall score for labour-market performance. Alberta ranked first on the labour-market performance index with an overall score of 9.0 out of 10, owing to its top scores on employment growth and private-sector employment growth, second-place score in duration of unemployment, and top-10 placement in average unemployment rate. Three Canadian provinces besides Alberta are in the top 10: Saskatchewan (2nd overall, score of 8.4), Manitoba (4th, score of 7.2), and British Columbia (6th, score of 7.0)While Canada’s two largest provinces Quebec (12th) and Ontario (16th) rank in the top 20, they continue to grapple with sluggish labour markets with overall scores of 5.8 and 5.5. Indeed, their rankings are more a reflection of poor labour market performance in the United States than robust performance at home.The second section of the study, labour-market characteristics and regulation, examines four key aspects of labour markets that contribute to their performance: public-sector employment levels, minimum wages, unionization levels, and labour-relations laws. Public-sector employment in Canadian provinces is markedly higher than in most US states. Alberta was the highest-ranked Canadian province, ranking 31st. Seven of the bottom 10 jurisdictions, including last place Newfoundland & Labrador, were Canadian provinces.Canadian provinces also fare poorly compared to US states on the measure of minimum wages. Nine of the 10 Canadian provinces occupy the bottom 10 rankings overall. On the unionization measure, the top-ranked Canadian province was Alberta (24.5%). There is a stark divide between Canada — average total unionization rate of 31.5% — and the United States — average total unionization rate of 13.1%.The high rate of unionization among Canadian provinces is the result of biased, overly prescriptive labour-relations laws that inhibit the proper and efficient functioning of the labour market by favouring one group over another and inhibiting innovation and flexibility.
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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) | 0.000 | 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 | 0.001 |
| 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".