Estimating Capital Input for Measuring Business Sector Multifactor Productivity Growth in Canada: Response to Diewert and Yu
Bibliographic record
Abstract
Diewert and Yu estimate that multifactor productivity grew at a 1.0 per cent average annual rate in the Canadian business sector from 1961 to 2011, compared to Statistics Canada’s Canadian Productivity Program estimate of 0.3 per cent. The major reason for this difference is that Diewert and Yu find capital services grew at 3.0 per cent per year, compared to Statistics Canada’s estimate of 4.8 per cent. This article identifies and discusses the three reasons for this discrepancy. First, while the Canadian Productivity Program aggregates capital services across industries to derive the capital input measure at the level of the business sector, Diewert and Yu use a top-down approach and directly compute capital and labour input series at the business sector level. Second, there are differences in the way the price of capital services is computed. Third, the Canadian Productivity Program bases its capital measures on a more detailed list of assets than Diewert and Yu. Statistics Canada estimates follow international guidelines and practices adopted by other statistical agencies in order to make estimates internationally comparable.
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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 | 0.005 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".