Aggregate Labour Productivity Growth in Canada and the United States: Definitions, Trends and Measurement Issues
Bibliographic record
Abstract
The purpose of this paper is to provide a thorough discussion of the definitional and data issues associated with the measurement of aggregate labour productivity growth in Canada and the United States. The paper examines all data sources for output, employment and hours estimates in the two countries, and attempts to identify the series that are the most appropriate for the calculation of aggregate labour productivity ?both from the perspective of the methodological merits of each series and of cross-country comparability. It also assesses the sensitivity of Canada-U.S. aggregate labour productivity growth comparisons to the choice of monitoring trends at the total economy or business sector level, investigates the sources of the differences between trends and comparisons assessed at each level, and discusses the advantages and disadvantages of making comparisons at each level. The paper finds compelling reasons to believe that the monitoring of total economy productivity trends is desirable in addition to the more common practice of focusing on the business sector. Canada has lagged the United States in terms of aggregate labour productivity growth over 1981-2003 to a much smaller degree according to total economy trends than according to business sector trends. This is caused by very high measured labour productivity growth in the non-business sector in Canada relative to the United States, which calls into question the reliability of productivity growth comparisons made at the total economy level. This also raises questions about the comparability of GDP growth between the two countries.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.012 | 0.053 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".