Recent Productivity Development in the United States and Canada: Implications for the Canada-U.S. Productivity and Income Gap
Bibliographic record
Abstract
The economic slowdown of 2001 reduced productivity growth in both the United States and Canada. This development has raised the question of the sustainability or permanency of the pace of productivity growth experienced during the 1995-2000 period in the United States and the likelihood of robust U.S. productivity growth spreading to Canada. In this article, Andrew Sharpe from the Centre for the Study of Living Standards compares productivity trends in the United States and Canada in 2001 to those during earlier postwar downturns and recessions. He finds that aggregate labour productivity growth held up better in 2001 in both countries than it did on average in the past, a development which may suggest an upward shift in trend productivity growth. Productivity growth in 2001 in the United States was faster than in Canada, as it was during the second half of the 1990s. This resulted in a further widening of the Canada-U.S. productivity gap and, since productivity is the key driver of income trends, in the income gap as well. If U.S. productivity growth continues at the pace experienced during the second half of the 1990s, as appears likely, Canada will need a major acceleration in productivity growth to prevent further deterioration in our relative productivity and income positions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".