Combating Inequality: The Between- and Within-Group Effects of Unionization on Earnings for People with Different Disabilities
Bibliographic record
Abstract
This paper addresses whether and how unions help to dismantle workplace inequality experienced by people with different types of disabilities. Using pooled 2009-2018 CPS MORG data of 630,799 respondents covering almost a decade, we find that union membership is especially beneficial for people with disabilities compared to the larger population, as well as other status groups. Furthermore, people with the severest disabilities benefit the most from being in unionized work, increasing weekly earnings by 36% for people with self-care and independent living-related disabilities. Because union membership increases disabled workers’ weekly earnings by more than double the increase experienced by people without disabilities, it brings unionized disabled workers closer to overall average earnings with important implications for inequality. Unionized work reduces earnings inequality between disabled and non-disabled workers, but earnings boosts associated with union membership generate more pronounced inequality within groups of workers with disabilities depending on whether individuals have access to unionized employment. We find that gaps among employed unionized and non-unionized disabled workers are significantly larger than those experienced by unionized and non-unionized female, Black, and Hispanic workers.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".