Can pay gaps between gay men and lesbians shed light on male–female pay gaps?
Bibliographic record
Abstract
Purpose The purpose of this paper is to estimate the relative importance of gender discrimination and differences in household responsibilities as determinants of the male–female pay gap. Design/methodology/approach It parses out the relative importance of those two factors by using the pay between gay men vs lesbian women as a comparison group that should reflect only gender discrimination. Subtracting the pay gap between gay men and lesbians (reflecting only gender discrimination) from the male–female pay gap for their heterosexual counterparts (reflecting both gender discrimination and household responsibilities) provides evidence of the relative importance of gender discrimination and household responsibilities in explaining the male–female pay gap. Findings The results show that essentially all of the male–female pay gap is attributed to differences in household responsibilities. Originality/value This paper advances the literature of gender wage gap by using a novel comparison group – gay men vs lesbian women – to estimate the relative importance of gender discrimination and differences in household responsibilities as determinants of the male–female pay gap.
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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 | 0.004 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".