Right-to-Work Laws, Unionization, and Wage Setting
Bibliographic record
Abstract
Abstract This paper uses two complementary approaches to estimate the effect of right-to-work (RTW) laws on wages and unionization rates. The first approach uses an event study design to analyze the impact of the adoption of RTW laws in five US states since 2011. The second approach relies on a differential exposure design that exploits the differential impact of RTW laws on industries with high unionization rates relative to industries with low unionization rates. Both approaches indicate that RTW laws lower wages and unionization rates. Under the assumption that RTW laws only affect wages by lowering the unionization rate, RTW can be used as an instrumental variable (IV) to estimate the causal effect of unions on wages. In our preferred specification based on the differential exposure design, the IV estimate of the effect of unions on log wages is 0.35, which substantially exceeds the corresponding OLS estimate of 0.16. This large wage effect suggests that RTW may also directly affect wages due to a reduced union threat effect.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".