Beyond the Gender Binary: Wage Inequality and Occupational Segregation among Transgender and Nonbinary Workers
Bibliographic record
Abstract
Abstract This study examines labor market disparities across gender identities in Canada using data from the 2021 Canadian Census, the first national census to identify transgender and nonbinary individuals. We analyze employment probabilities, work hours, and hourly wages among six gender groups: cisgender men, cisgender women, transgender men, transgender women, and nonbinary individuals assigned male or female at birth. Transgender and nonbinary individuals are 8–14 percentage points less likely to be employed than cisgender men and earn 20–30 percent lower hourly wages on average. After adjusting for demographic, occupational, and industrial characteristics, earnings gaps remain substantial—approximately 8–17 percent— and are largest for nonbinary individuals assigned female at birth. Subgroup analyses reveal pronounced heterogeneity across occupations: wage gaps are smallest in health-related fields but largest in management and leadership positions, where gender minorities are also underrepresented. Transgender men fare relatively better in male-dominated fields such as trades and manufacturing, while nonbinary individuals show higher representation in arts and education. Oaxaca–Blinder decompositions indicate that about half of the overall wage gap is explained by differences in observable characteristics, with the remainder unexplained. The findings document persistent and uneven economic disadvantages for gender-diverse populations and highlight occupational contexts where barriers to inclusion are most pronounced. JEL code: J0
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".