Capital Intensity in Canada and the United States, 1987 to 2003
Bibliographic record
Abstract
Official data from statistical agencies are not always ideal for cross-country comparisons because of differences in data sources and methodology. Analysts who engage in cross-country comparisons need to carefully choose among alternatives and sometimes adapt data especially for their purposes. This paper develops comparable capital stock estimates to examine the relative capital intensity of Canada and the United States. To do so, the paper applies common depreciation rates to Canadian and U.S. assets to come up with comparable capital stock estimates by assets and by industry between the two countries. Based on common depreciation rates, it finds that capital intensity is higher in the Canadian business sector than in the U.S. business sector. This is the net result of quite different ratios at the individual asset level. Canada has as higher intensity of engineering infrastructure assets per dollar of gross domestic product produced. Canada has a lower intensity of information and communications technology (ICT) machinery and equipment (M&E). Non-ICT M&E and building assets intensities are more alike in the two countries. However, these results do not control for the fact that different asset-specific capital intensities between Canada and the United States may be the result of a different industrial structure. When both assets and industry structure are taken into account, the overall picture changes somewhat. Canada's business sector continues to have a higher intensity of engineering infrastructure and about the same intensity of building assets; however, it has a deficit in M&E that goes beyond ICT assets.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".