Productivity growth and prices in Canada: What can we learn from the U. S. Experience?
Bibliographic record
Abstract
In recent years, there has been increasing discussion about the possible emergence of a new economy. In this paper we review recent developments in productivity growth and prices of final goods and services in the United States in an effort to identify early indicators of whether the Canadian economy is on a path to follow the United States to higher productivity growth. We put particular emphasis on the behaviour of prices, since monetary policy in Canada is directed towards maintaining low and stable inflation. Although there is little evidence to date of a U.S.-style acceleration in productivity growth in Canada, we suggest that there are several reasons to be cautiously optimistic that Canada will follow the U.S. experience to some degree. We formalize one aspect of this hypothesis using estimated, expectations-augmented Phillips curves. We present evidence for the United States of changes in the relationship between prices and output that would be consistent with the emergence of the new economy, the effects of which have been largely concentrated in the provision of final goods. We then provide evidence of a similar break for Canada in 2000. However, with only two quarters of data for 2000, considerable uncertainty remains as to the timing, size, and the duration of any acceleration in productivity growth in Canada.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.015 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".