The Productivity Differential Between the Canadian and U.S. Manufacturing Sectors: A Perspective Drawn from the Early 20th Century
Bibliographic record
Abstract
Many historical comparisons of international productivity use measures of labour productivity (output per worker). Differences in labour productivity can be caused by differences in technical efficiency or differences in capital intensity. Moving to measures of total factor productivity allows international comparisons to ascertain whether differences in labour productivity arise from differences in efficiency or differences in factors utilized in the production process. This paper examines differences in output per worker in the manufacturing sectors of Canada and the United States in 1929 and the extent to which it arises from efficiency differences. It makes corrections for differences in capital and materials intensity per worker in order to derive a measure of total factor efficiency of Canada relative to the United States, using detailed industry data. It finds that while output per worker in Canada was only about 75% of the United States productivity level, the total factor productivity measure of Canada was about the same as the United States level - that is, there was very little difference in technical efficiency in the two countries. Canada's lower output per worker was the result of the use of less capital and materials per worker than the United States.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| 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".