An Analysis of the Labour Productivity Growth Slowdown in Canada since 2000
Bibliographic record
Abstract
After accelerating in the second half of the 1990s, aggregate labour productivity growth in Canada has fallen off significantly since 2000. This paper examines the factors behind this development, which is puzzling given the recent acceleration of productivity growth in the United States and the apparent strength of most productivity drivers in Canada. Factors that may have contributed to the post-2000 productivity growth slowdown include: the weakness of information and communications technologies (ICT) manufacturing; the slower growth of machinery and equipment (M&E) investment; slower economic growth; and higher commodity prices. But the authors argue that in recent years Canada has suffered no major macroeconomic shock (excluding exchange rate shocks) and has undergone no policy development or reorientation that would have significantly impeded productivity growth. In addition, the pick-up in U.S. productivity growth after 2000, which appears to be related to the faster pace of technological change, may augur well for a return to stronger productivity growth in this country. Yet they note that the dangers of complacency are very real. They conclude by pointing out that future trends in productivity in Canada are largely in the hands of the private sector. Nevertheless, Canadian governments can faciliatate productivity-enhancing investments by fostering a highly competitive business climate.
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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 | 0.005 | 0.018 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".