Sectoral Contributions to Labour Productivity Growth in Canada: Does the Choice of Decomposition Formula Matter?
Bibliographic record
Abstract
Using three decomposition formulas (TRAD, CSLS, and GEAD), this article estimates sectoral contributions to business sector labour productivity growth in Canada during the 2000-2010 period. Although at the aggregate economy level there was substantial agreement among the three formulas, contribution estimates varied widely at the sectoral level. In particular, there were significant differences in the estimated contributions of construction, manufacturing, and mining and oil and gas extraction. Ultimately, these differences reflect the fact that traditional decomposition formulas (TRAD and CSLS) and the GEAD formula measure different economic phenomena. Instead of seeing estimates constructed by the GEAD and traditional formulas as “competing” narratives, the article concludes it is more useful to see them as providing complementing stories about the role of different sectors in driving aggregate labour productivity growth.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".