Unionization and Wage Inequality: A Comparative Study of the U.S, the U.K., and Canada
Bibliographic record
Abstract
This paper presents a comparative analysis of the link between unionization and wage inequality in the U.S., the U.K., and Canada. Our main motivation is to see whether unionization can account for differences and trends in wage inequality in industrialized countries. We focus on the U.S., the U.K., and Canada because the institutional arrangements governing unionization and collective bargaining are relatively similar in these three countries. The three countries also share large nonunion sectors that can be used as a comparison group for the union sector. Using comparable micro data for the last two decades, we find that unions have remarkably similar qualitative impacts in all three countries. In particular, unions tend to systematically reduce wage inequality among men, but have little impact on wage inequality for women. We conclude that unionization helps explain a sizable share of cross-country differences in male wage inequality among the three countries. We also conclude that de-unionization explains a substantial part of the growth in male wage inequality in the U.K. and the U.S. since the early 1980s.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.022 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".