The Comparative Level of GDP Per Capita in Canada and the United States: A Decomposition into Labour Productivity and Work Intensity Differences
Bibliographic record
Abstract
This study is the third in a series related to the project launched in fall 2003 by the Canadian Productivity Accounts of Statistics Canada in order to compare productivity levels between Canada and the United States. The study's purpose is to examine the comparability of the components of the labour market of these two countries that serve as the sources of the differences in the gross domestic product (GDP) per capita between them. This study can be subdivided into three sections. The first section develops and illustrates the conceptual and methodological framework required to make Canada/United States estimates of labour and population comparable in terms of level. The second section presents revisions and an update to 2005 of the GDP per capita differences and its components, which were presented for the first time in the study by Baldwin, Maynard and Wong (2005), which covered the period from 1994 to 2002, at the time. Lastly, using the year 2000 as an example, this study tries to quantify the "statistical error" that arises from using inadequate statistics or statistics not designed for this type of international comparison. This exercise reveals that the comparability of data on hours worked per job is especially crucial to identifying the origin of the differences in GDP per capita between labour productivity and hours worked per capita. The worst error involves comparing hours worked estimated from an employer survey with those obtained from a household survey. This type of comparison between Canada and the United States results in assigning an estimated 72% of the difference in GDP per capita to labour productivity when, in reality, it counted for barely 36% in 2000.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".