Relative Multifactor Productivity Levels in Canada and the United States: A Sectoral Analysis
Bibliographic record
Abstract
This paper has three main objectives. First, it examines the level of multifactor productivity (MFP) in Canada relative to that of the United States for the 1994-to-2003 period. Second, it examines the relative importance of differences in capital intensity and MFP in accounting for the labour productivity differences between the two countries. Third, it traces the overall MFP difference between Canada and the United States to its industry origins and estimates the contributions of the goods, services and engineering sectors to the overall MFP gap. Our main findings are as follows. First, the overall capital intensity is as high in Canada as in the United States; but there are considerable differences in Canada's capital intensity across asset classes. Canada has considerably less machinery and equipment, about the same amount of buildings and considerably more engineering construction. Second, most of the differences in labour productivity between Canada and the United States are due to the differences in MFP. Third, our industry results show that the levels of labour productivity and MFP in the goods and the engineering sectors are closer to those of the United States. But, the level of labour and multifactor productivity in the services sector is much lower in Canada. The lower levels of labour productivity and MFP in the Canadian services sector account for most of the overall productivity level difference between the two countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".