Productivity Growth in Canada and the United States: Recent Industry Trends and Potential Explanations
Bibliographic record
Abstract
Labour productivity growth in Canada was weaker than that in the United States from the mid-1980s to 2010, leading to a decline in Canada’s relative productivity level. This situation was mainly due to the lower multifactor productivity (MFP) growth experienced in most Canadian industries in that period. After 2010, however, the pattern reversed itself as labour productivity growth in Canada exceeded that of the United States. Higher labour productivity growth in Canada for the 2010-2014 period was due to a relatively larger capital deepening effect and relatively higher MFP growth. Both these developments were associated with stronger output growth and stronger demand in Canada. In addition, the contributions of ICT producing and ICT intensive using industries to U.S. labour productivity growth waned after 2010. For Canada, ICT producing industries contributed little to overall labour productivity before and after 2010, while ICT intensive using industries exhibited stronger productivity growth after 2010. The latter may reflect the more moderate ICT investment in Canada compared to the United States in the 1990s and early 2000s and the more gradual realization of benefits of ICT usage.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".