Unions and wage inequality: The roles of gender, skill and public sector employment
Bibliographic record
Abstract
Abstract We examine the changing relationship between unionization and wage inequality in Canada and the United States. Our study is motivated by profound recent changes in the composition of the unionized workforce. Historically, union jobs were concentrated among low‐skilled men in private sector industries. With the steady decline in private sector unionization and rising influence in the public sector, half of unionized workers are now in the public sector. Accompanying these changes was a remarkable rise in the share of women among unionized workers. Currently, approximately half of unionized employees in North America are women. While early studies of unions and inequality focused on males, recent studies find that unions reduce wage inequality among men but not among women. In both countries, we find striking differences between the private and public sectors in the effects of unionization on wage inequality. At present, unions reduce economy‐wide wage inequality by less than 10%. However, union impacts on wage inequality are much larger in the public sector. Once we disaggregate by sector, the effects of unions on male and female wage inequality no longer differ. The key differences in union impacts are between the public and private sectors—not between males and females.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".