Economic and Productivity Growth in Canadian Industries
Bibliographic record
Abstract
Most OECD economies, including that of Canada, experienced a slowdown in economic growth from the 1961–1973 period to the 1973– 1988 period, and to the 1988–1995 period. Consequently, progress in the standard of living, as measured by GDP per capita, also slowed down in most OECD countries over the three subperiods. This paper analyses the sources of output growth in 122 industries and in the private business sector to gain an additional perspective on the slowdown of the Canadian economy. We adopt the constant-quality indexes of capital and labor inputs introduced by Dale W. Jorgenson and Zvi Griliches (1967) and later used extensively in Jorgenson et al. (1987), Jorgenson (1995a, b), and Jorgenson and Eric Yip (2000) to identify the sources of growth. These measures allow us to take into account the changing composition of the labor force and the capital stock. At the industry level, we adjust for capital quality by aggregating the capital stock across five asset types by means of the rental prices of capital rather than the asset prices of capital. The use of rental prices allows us to incorporate differences in depreciation rates and tax treatment across different asset types for each industry. At the same time, we combine hours worked by each type of worker using the share of labor compensation to reflect labor quality. At the aggregate level, we apply the same framework by aggregating the capital stock across different asset types and hours worked across different types of workers. A number of studies have compared Canada’s economic growth performance with that of its competitors using this framework (Chrysostom Dougherty, 1991; Dougherty and Jorgenson, 1997; Jorgenson and Yip, 2000). However, this is the first attempt at using this framework to assess Canada’s economic performance at the industry level.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".