Must Canada Change its Labour and Employment Laws to Compete with the United States
Bibliographic record
Abstract
Globalization has created new and increasingly complex market pressures that governments must cope with. In the United States, there is evidence that states compete with each other in a “race to the bottom”, weakening labour and employment regulation in order to attract industrial development. Given that Canada is more exposed to US competition than ever, the author considers whether such pressures will require Canadian jurisdictions to do the same in order to remain competitive. The theory underpinning the race to the bottom suggests that only in select circumstances is it advantageous to pursue regulatory convergence, since countries with strong labour and employment protections tend to have other national advantages that offset the higher costs associated with those protections. A series of studies have examined the relationship between protections offered by labour and employment laws with trade and investment success, but the results have not been uniform. Building on those studies, the author develops a theory of that relationship which he assess against econometric analyses that try to measure the effects of those relationships globally. Analyzing Canada-US competitive dynamics through this theoretical framework, he concludes that Canada’s stronger labour and employment law protections are not likely to diminish its economic success. Deepening economic integration between Canada and the US drives regulatory competition in labour and employment law only if they are a predominant factor in competition between the two jurisdictions. Where that is not the case, competition in labour and employment laws is more likely to be the product of anxious political discourse. The author considers the total proportion of the cost of Canadian goods and services exports that can be attributed to labour and employment laws and the extent to which Canadian producers can exploit competitive advantages not available in the US. He argues that Canadian workplace laws are not likely to affect competitiveness with the US because the direct cost implications of those laws are small both in relation to total production costs in traded industries and to other competitive advantages. Nor is there evidence that labour and employment laws are holding back Canadian productivity growth. He concludes that Canada need not adjust its workplace laws to compete with the US and that Canadian policy makers have room to establish laws that meet workers’ needs without downgrading its labour and employment law protections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".