Insights into Canada’s Abysmal Post-2000 Productivity Performance from Decompositions of Labour Productivity Growth by Industry and Province
Bibliographic record
Abstract
The Centre for the Study of Living Standards has released new estimates of labour, capital and multifactor productivity growth and levels at the market sector, two-digit, and three-digit NAICS industry level for the Canadian provinces during the 1997-2007 period. This article exploits this database to shed light on the nature of the slowdown in labour productivity growth in Canada after 2000. It identifies manufacturing as the sector that has accounted for most of the slowdown. Within manufacturing, transportation equipment and computers and electronics are found to be the industries that accounted for the lion’s share of the sector’s fall-off in labour productivity growth. Ontario was the province that contributed proportionately the most to the slowdown because of the concentration of manufacturing in this province. A fall in manufacturing output growth is identified as the factor most responsible for the decline in productivity growth in the sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".