The Widening Canada-US Productivity Gap in Manufaturing
Why this work is in the frame
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Bibliographic record
Abstract
In this article, Jeffrey I. Bernstein of Carleton University, Richard G. Harris from Simon Fraser University, and Andrew Sharpe from the Centre for the Study of Living Standards provide a comprehensive analysis of the widening of the Canada-US manufacturing productivity gap. Since 1994, labour productivity growth in manufacturing in the United States has greatly exceeded that recorded in Canada. Output per hour in Canada fell 20 percentage points from 87 per cent of the US level in 1994 to 67 per cent in 2001. This development has been responsible for most of the widening of the aggregate Canada-US labour productivity gap. The authors find that the growth in the gap largely reflects the acceleration of productivity growth in US high-tech manufacturing sector. The Canadian high-sector is smaller than its US counterpart and experienced much weaker productivity growth. It is estimated that these two factors themselves account for 70 per cent of the widening of the gap over the 1994-2000 period. Faster growth in capital intensity of production in the United States also played a complementary role in the growth of the gap, a development in part fostered by the greater increase in the price of labour relative to that of investment goods in the United States than in Canada. This was due to slower labour compensation growth and, to a lesser extent, a smaller decline in the price of investment goods in Canada. The depreciation of the value of the Canadian dollar relative to the US dollar played some role in this latter development. The authors conclude that Canadian economic policies have not directly contributed in any significant manner to the widening of the gap.
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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 | 0.001 | 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 | 0.000 |
| 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 it