Long-term Productivity Growth in Manufacturing in Canada and the United States, 1961 to 2003
Bibliographic record
Abstract
In this paper, we provide an international comparison of the growth in Canadian and U.S. manufacturing industries over the 1961-to-2003 period. We find that average annual growth rates of labour productivity growth were almost identical in the Canadian and U.S. manufacturing sectors during this period. But the sources of labour productivity growth differed in the two countries. Intermediate input deepening was a more important source of labour productivity growth in Canada than in the United States, while investment in capital and multifactor productivity (MFP) growth were more important in the United States than in Canada. After 1996, labour productivity growth in Canada was lower than in the United States. The post-1996 slower labour productivity growth in Canada relative to the United States was due to slower growth in MFP and slower growth in capital intensity. The slower MFP growth in Canada accounted for 60% of Canada - United States labour productivity growth difference, and slower growth in capital intensity accounted for 30%. The slower MFP growth in the Canadian manufacturing sector relative to that of the United States after 1996 was due to lower MFP growth in the computer and electronic products industry. The slower growth in capital'labour ratio in the Canadian manufacturing compared with the United States after 1996 is related to the changes in relative prices of capital and labour inputs in the two countries.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".