The Gains from More Competitive Regulation Settings in Canada
Bibliographic record
Abstract
This article explores potential gains for Canada from making its regulatory framework as competition friendly as that in the United States. We estimate standard cross-country GDP growth regressions incorporating the OECD's indicators of product market regulations (PMRs) that measure the extent to which regulations, laws and other rules inhibit product market competition. Based on the key point estimate (or the lower bound of its 95 per cent confidence interval), GDP per capita in Canada could be about 2.0 per cent (0.7 per cent) higher in the medium term (i.e. 5 years) and about 5.3 per cent (1.8 per cent) higher after 20 years as a result of making Canada's 2013 regulatory settings related to foreign direct investment (FDI) as competitive as in the United States. However, government actions taken since 2013 have improved the competitiveness of these regulations. As a result, further changes needed to reach the US benchmark are not as great as they were in 2013 and would not generate as substantial gains.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".