Productivity Growth in Service Industries: A Canadian Success Story
Bibliographic record
Abstract
The Canadian service sector has performed well in recent years in terms of labour and multifactor productivity growth, both in absolute terms and relative to the United States, offsetting much of the poorer performance of the manufacturing sector. Service sector labour productivity growth has also shown a marked acceleration in both Canada and the United States in recent years relative to earlier periods. The objective of this paper is to identify the factors behind this relative Canadian success story. The sources of the acceleration in service sector labour productivity growth were different in the two countries. In Canada, increased multifactor productivity growth was responsible for 70 per cent of the labour productivity growth acceleration. In the United States, on the other hand, increased capital intensity and intermediate input intensity were the most important contributors to the service sector labour productivity growth acceleration. In Canada, the contribution of capital intensity growth to service sector labour productivity growth actually fell between 1981-1995 and 1995-2000. The factor driving Canada’s superior service sector labour productivity growth has been better multifactor productivity growth, suggesting a productivity convergence to the U.S. level. A faster pace of human capital accumulation relative to the United States, as measured by growth in the proportion of workers with a university degree, fostered the catch-up process of Canadian service industries.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".