Mind the Gap: Sexual Orientation Wage Gaps for Non-White and Immigrant Minorities in the United States
Bibliographic record
Abstract
A growing body of literature has found that sexual orientation and gender impact labor market outcomes, including earnings. This literature generally finds that gay and bisexual men earn less than heterosexual men. Despite being the highest earners among women, lesbians earn less than heterosexual men, and bisexual women earn the least. Far less research has explored intersectional disadvantages/advantages of being a lesbian, gay, or bisexual (LGB) individual and belonging to other minority groups. Using data from the 2013 to 2018 US National Health Interview Survey, this paper explores whether being an LGB racial minority or LGB immigrant results in cumulative earning disadvantages/advantages. This study finds that regardless of race or immigrant status, gay men's earnings did not statistically differ from white/US-born heterosexual men's earnings. For white and US-born women, their earnings followed the same pattern, with lesbians earning the most, followed by heterosexual women, then bisexuals; however, for nonwhite women, bisexuals earned the most and lesbians earned the least. The results for immigrant sexual minorities were not statistically significant. These findings suggest that disadvantage/advantage is multilayered-sexual minorities who occupy multiple minority positions may experience different levels of disadvantage/advantage.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".