NEW ESTIMATES OF MULTIFACTOR PRODUCTIVITY GROWTH FOR THE CANADIAN PROVINCES
Bibliographic record
Abstract
This article presents new estimates of multifactor productivity for the Canadian provinces for the 1997-2007 period. In contrast to earlier estimates, these estimates incorporate both changes in labour and capital composition or quality. Reflecting differences in labour productivity and capital productivity, multifactor productivity growth varies greatly by province. Newfoundland enjoyed the strongest multifactor productivity growth and Alberta the weakest. THE OBJECTIVE OF THIS ARTICLE is to present new estimates of multifactor productivity (MFP) or total factor productivity2 for the Canadian provinces. In contrast to previous estimates of MFP (e.g. CSLS, 2008), these esti-mates for the first time take account of changes in labour composition or quality and changes in capital composition or quality. The estimates have been prepared by Statistics Canada for the Centre for the Study of Living Standards (CSLS), which received financial support from Alberta Finance and Enterprise in producing this report. The estimates are posted on the CSLS website (www.csls.ca/data/mfp.asp) for free public access. This report is divided into three main sec-tions. The first section provides a brief overview of the methodologies and data sources used by Statistics Canada to construct the provincial multifactor productivity database. The third section presents the new estimates of labour productivity, capital productivity, multifactor productivity, labour composition or quality, and sources of growth by province. The third and final section concludes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.022 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".