Canada-United States Labour Productivity Gap Across Firm Size Classes
Bibliographic record
Abstract
This paper examines and compares labour productivity in Canada and the United States for small and large firms over the period from 2002 to 2008. It quantifies the relative importance of small and large firms in Canada and the United States and measures the relative productivity levels of small versus large firms. Small firms are relatively more important in the Canadian economy. Small firms are less productive than large firms in both countries. But the productivity disadvantage of small relative to large firms was higher in Canada. The paper provides an estimate of the impact that these differences have on the gap in productivity levels between Canada and the United States. It first estimates the changes that would occur in Canadian aggregate labour productivity if the share of hours worked of large firms in Canada was increased to the U.S. level. It then quantifies the impact of increasing the relative productivity of small to large firms in Canada up to the relative productivity ratio of small firms to large firms that existed in the United States. Together, decreasing the relative importance of small firms in the economy and increasing their relative productivity compared to large firms accounts for most of the gap in productivity levels between Canada and the United States in 2002. However, changes in the economy that occurred between 2002 and 2008 reduced the contribution of the small-firm sector to the gap in productivity levels.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".