The Implications of US Worker Choice Laws for British Columbia and Ontario
Bibliographic record
Abstract
This study examines the effects of worker choice laws in the US — commonly referred to as “right to work” (RTW) policies — and applies the findings to British Columbia and to Ontario. RTW laws have been enacted by 24 US states; these laws prohibit collective bargaining agreements between employers and unions from forcing workers represented by a union to pay dues for its representation.The scholarly literature generally finds that RTW laws reduce the percentage of workers covered by union contracts, and increase economic and employment growth. A new econometric analysis reported in this study finds that RTW laws in the US increase economic growth by about 1.8% and employment by about 1% in the states enacting such laws.The scholarly literature also finds that RTW laws have the effect of increasing manufacturing employment and output. Oklahoma, which became a RTW state in 2001, shares a border with seven states, four of which adopted RTW laws earlier; the others are not RTW states. The data suggest that the faster manufacturing growth observed in Oklahoma after 2001 was due, to some substantial degree, to the adoption of a RTW policy.A conservative application of the econometric findings reported here suggest that a RTW policy would increase manufacturing output in British Columbia and Ontario by about $200 million (0.2%) and $4.0 billion (0.5%), respectively. A conservative estimate is that a RTW policy would increase total economic output in British Columbia by $3.9 billion (about $844 per capita) and total employment by a bit less than 19,000. The respective figures for Ontario are $11.8 billion (about $874 per capita) and almost 57,000.These predicted effects are not trivial, and the prospective benefits should engender a debate in Canada and in the provinces about the policy reforms needed to maintain and enhance competitive positions. A RTW law should be prominent among them.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".