Beyond the Gender Binary: Wage Inequality and Occupational Segregation among Transgender and Nonbinary Workers

Beyond the Gender Binary: Wage Inequality and Occupational Segregation among Transgender and Nonbinary Workers | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond the Gender Binary: Wage Inequality and Occupational Segregation among Transgender and Nonbinary Workers Yihong Bai, Antony Chum, Qian Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8080057/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version 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 wage gap labor market outcomes gender identity transgender non-binary people Canadian Census Introduction Research on gender and wage inequality has devoted significant attention to estimating differences in wages between men and women (Blau & Kahn, 2017). In recent years, individuals who identify outside traditional cisgender categories, specifically transgender and non-binary individuals, have gained greater public visibility. However, due to limited empirical evidence, our understanding of wage differences across gender identities remains incomplete. In 2021, the Canadian Census included, for the first time, a question on respondents’ current gender, which could differ from their sex assigned at birth. By combining responses to the gender and sex-at-birth questions, individuals can be classified as cisgender (same sex at birth and gender), transgender (assigned male at birth but identify as women, or assigned female at birth but identify as men), and nonbinary (whose current gender is neither man nor woman). To our knowledge, this is the first large-scale national census to collect information that enables the identification of transgender and nonbinary individuals (Statistics Canada of Canada, 2022). In this paper, we leverage this novel gender identity information in the 2021 Canadian Census to examine labor market outcomes across gender identities. We analyze differences in employment probabilities, annual wages, weeks and hours worked, and hourly wages. We also examine the distribution of individuals across occupations and conduct subsample analyses to explore wage gaps within specific occupational sectors. We find that transgender and nonbinary individuals are about 8 to 14 percentage points less likely to be in paid employment than cisgender men. Conditional on employment, they also work fewer weeks and hours. Their raw hourly wages are, on average, 20 to 30 percent lower than those of cisgender men—a gap that is substantially larger than the roughly 10 percent difference observed between cisgender women and men—with nonbinary individuals assigned female at birth (AFAB) facing the largest penalty. After controlling for demographics and job characteristics such as occupation, industry, and training requirements, the wage gaps shrink to about 8 to 17 percent, though they remain largest for nonbinary AFAB individuals. Subsample analyses by occupation reveal several notable patterns. In legislative and senior management, transgender and nonbinary individuals are underrepresented and face large wage gaps within the sector. By contrast, health-related fields are more equitable, with greater representation of gender minorities and smaller wage disparities. Sales and service occupations also attract more transgender and nonbinary individuals, though significant wage gaps persist. A distinct pattern emerges for transgender men, who are more represented than other gender minority groups in traditionally male-dominated fields such as trades, natural resources, and manufacturing, where their wages are not significantly different from those of cisgender men, highlighting the salience of maleness in these occupations. For nonbinary individuals, representation is particularly high in arts and sports, where they experience relatively favorable wage outcomes. We also conducted Oaxaca–Blinder decompositions and found that about half of the hourly wage differences between noncisgender and cisgender individuals can be explained by observable characteristics, while the other half remains unexplained. The relatively larger unexplained component, compared to the traditional male–female wage gap, suggests that discrimination may play a more prominent role. A growing body of empirical research has documented significant labor market disparities faced by gender-diverse populations. 1 Several U.S.-based studies using survey data have highlighted persistent employment and earnings penalties for individuals who identify as transgender (Carpenter et al., 2020, 2022; Ciprikis et al., 2020; Shannon, 2022). 2 For example, using the Behavioral Risk Factor Surveillance System (BRFSS), which only can identify tansgender people but not non-binary individuals, Carpenter et al., (2020) find that transgender adults report significantly lower levels of employment and income, as well as poorer health, compared to cisgender men. However, the BRFSS includes only self-reported categorical income data and lacks information on occupation or industry. More recent work using administrative records has produced more precise estimates of wage gaps. In a landmark study using Social Security and IRS data, Carpenter et al., (2024) document a wage penalty of 6–13 log points for individuals who identify as transgender, based on within-person and sibling fixed-effects models. These gaps are most pronounced among those who identify as transgender women. In addition to individuals who identify as transgender, nonbinary individuals also face disadvantages in the labor market and have become more visible in recent literature due to improved data availability. For example, Shannon, (2022) documents that individuals assigned female at birth (AFAB) who identify as transgender or nonbinary tend to earn less than their assigned male at birth (AMAB) peers, suggesting a persistent “maleness premium.” Coffman et al., (2024) find that individuals who identify as nonbinary report distinct preferences and experience higher levels of discrimination compared to both men and women. Non-U.S. contexts also offer valuable comparative insights (Carpenter, Kirkpatrick, et al., 2025) on New Zealand, Nettuno, (2024) on Chile, and Waite et al., (2019) on Canada). Carpenter, Feir, et al., (2024) conduct the first nationally representative study of labor market disparities among transgender and nonbinary individuals in Canada using 2021 Census data. Specifically, they find that nonbinary individuals assigned female at birth earn approximately 40% less annually than otherwise comparable cisgender men, with the gap narrowing slightly (to ~27%) after controlling for occupation and industry. Transgender women and nonbinary individuals assigned male at birth also face significant, albeit smaller, earnings penalties, ranging from 10% to 20%. Building on their findings, our study expands the scope in several key dimensions. First, we examine the distribution of transgender and nonbinary individuals across occupations, industries, and categories defined by training requirements. We identify patterns of underrepresentation in some sectors and concentration in others, which, though descriptive, suggest potential barriers to entry in certain occupations and relatively favorable conditions in others. Second, we conduct subgroup wage analyses across occupational sectors, revealing variation in wage outcomes for gender minorities depending on the occupation. Third, we implement Oaxaca–Blinder decompositions to quantify the extent to which wage gaps are explained by compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination. Taken together, although both studies use the same foundational data, our analysis provides a more comprehensive decomposition of labor market inequality by gender identity in the Canadian context. Data and Method Data and descriptive statistics This study uses data from the 2021 Canadian long-form Census, which was distributed to approximately one-quarter of all households nationwide. The long-form questionnaire is administered through a stratified random sampling process designed to ensure representativeness across provinces, territories, and population groups (Statistics Canada, 2024 ). Selected households are required by law to respond, yielding exceptionally high coverage (97%) and response rates (98%) compared to typical survey data (Statistics Canada, 2021b , 2024 ). The 2021 Canadian Census was among the first large-scale national censuses globally to collect information that enables researchers to identify transgender and nonbinary individuals (Statistics Canada, 2022b ). Historically, Canadian censuses collected information on sex but not gender. In 2021, respondents aged 15 and older were asked, “What is this person’s gender?” accompanied by an explanatory note defining gender as “current gender, which may differ from sex assigned at birth and from legal documents.” (Statistics Canada, 2022a ) By combining responses on sex assigned at birth with those on current gender identity, individuals can be classified into the following categories: cisgender men, cisgender women, transgender men, transgender women, nonbinary individuals assigned male at birth (NB-AMAB), and nonbinary individuals assigned female at birth (NB-AFAB). Our sample is restricted to individuals aged 18 to 64, excluding students. 3 When analyzing labor market outcomes such as occupation and industry, and estimating log wage regressions, we restrict the sample to individuals who are employed, excluding the self-employed. This ensures that all observations have valid occupation and industry codes and positive wage values. Accordingly, our analysis focuses on wage and salary workers to capture labor market outcomes within the formal employment sector for gender minorities. Census weights are used in all of our analyses. Table 1 presents descriptive statistics for our sample without restrictions on labor market status. The sample sizes are weighted counts, representing the number of individuals in each category within the Canadian population. 4 Approximately 0.33% of the full sample of Canadians aged 18 to 64 who are not students identify as transgender or nonbinary. The largest group is transgender women (0.11%), followed by nonbinary individuals assigned female at birth (0.09%), transgender men (0.09%), and nonbinary individuals assigned male at birth (0.04%). Despite their relatively small share of the population, the absolute numbers of transgender and nonbinary individuals are large enough to allow for meaningful subgroup analysis. Several patterns stand out from Table 1. Compared to cisgender individuals, transgender and nonbinary individuals are, on average, younger, less likely to be immigrants, and less likely to have children, with the differences being more pronounced for nonbinary individuals. Nonbinary individuals are also more likely to be White or Indigenous. In terms of marital status, transgender and nonbinary individuals are less likely to be in opposite-sex marriages or common-law relationships, and more likely to be single or in same-sex marriages or common-law relationships, compared to cisgender individuals. Transgender men and transgender women are less likely to have a Bachelor's degree or higher—20.8% for transgender men and 25.0% for transgender women—compared to 27.0% of cisgender men and 33.9% of cisgender women. In contrast, the proportion with a post-secondary degree is higher among nonbinary individuals: 32.1% for those assigned male at birth (AMAB), and 37.8% for those assigned female at birth (AFAB). Both transgender and nonbinary individuals are less likely to live in rural areas and more likely to reside in large urban population centres. In terms of province of residence, notable differences emerge: transgender and nonbinary individuals are less likely to live in Quebec and more likely to live in British Columbia. Table 1 Descriptive statistics: individuals aged 18–64, not in school Cisgender men Cisgender women Transgender men Transgender women Non-binary AMAB Non-binary AFAB N (%) 9,799,090 (50.13) 9,681,040 (49.53) 16,910 (0.09) 21,870 (0.11) 8,700 (0.04) 18,390 (0.09) Age (SD) 43.4 (12.7) 44.3 (12.3) 36.1 (13.4) 40.1 (13.4) 34.8 (10.6) 31.7 (9.7) Immigrants 28.42% 30.25% 21.17% 26.15% 12.87% 11.96% Has kid 32.67% 37.11% 16.03% 20.21% 9.66% 11.26% Race White 69.50% 67.81% 69.66% 69.96% 77.24% 75.10% South Asian & Southeast Asian 11.08% 11.49% 8.63% 10.70% 3.56% 2.99% East Asian 5.15% 6.24% 3.96% 4.16% 2.07% 3.26% Black 3.76% 3.83% 4.44% 3.84% 2.41% 3.37% West Asian & Arab 2.89% 2.63% 2.07% 2.47% 1.61% 0.92% Latin American 1.81% 1.86% 1.71% 1.97% 1.26% 1.20% Other 1.18% 1.26% 1.42% 1.55% 2.41% 2.18% Indigenous 4.63% 4.88% 8.10% 5.35% 9.43% 11.09% Marital status Not married or common-law 38.37% 35.25% 61.38% 52.49% 65.63% 63.19% Opposite sex married or common law 60.83% 63.98% 33.53% 43.03% 30.11% 29.09% Same sex married or common law 0.80% 0.77% 5.14% 4.48% 4.25% 7.72% Education Level No certificate, diploma or degree 12.14% 9.09% 14.61% 14.08% 9.77% 8.86% Secondary (high) school diploma or equivalency certificate 26.79% 23.50% 37.43% 33.33% 31.61% 29.74% Post-secondary no degree 34.04% 33.49% 27.14% 27.57% 26.44% 23.65% Bachelor's degree 17.58% 22.82% 14.31% 16.64% 22.53% 25.07% Graduate degree 9.44% 11.11% 6.51% 8.32% 9.54% 12.72% Areas of residence Rural area 18.11% 17.26% 11.65% 12.21% 7.47% 6.85% Small population centre 12.01% 12.10% 12.60% 10.65% 7.24% 7.94% Medium population centre 8.38% 8.43% 10.29% 9.01% 8.16% 9.46% Large urban population centre 61.51% 62.22% 65.46% 68.08% 77.01% 75.80% Provinces of residence Newfoundland and Labrador 1.36% 1.42% 1.54% 1.51% 1.03% 1.09% Prince Edward Island 0.41% 0.42% 0.35% 0.46% 0.34% 0.49% Nova Scotia 2.60% 2.68% 3.61% 2.83% 4.71% 4.62% New Brunswick 2.11% 2.15% 2.48% 2.10% 2.18% 1.96% Quebec 22.32% 21.60% 17.33% 16.64% 15.06% 11.53% Ontario 38.69% 39.40% 40.27% 40.42% 37.82% 37.74% Manitoba 3.60% 3.54% 3.37% 2.97% 3.68% 4.08% Saskatchewan 3.01% 2.96% 2.37% 3.06% 2.76% 2.39% Alberta 11.95% 11.71% 12.18% 12.57% 13.33% 12.40% British Columbia 13.61% 13.79% 16.14% 17.15% 18.97% 23.06% Territories 0.34% 0.33% 0.41% 0.27% 0.23% 0.65% Notes: Weighted descriptive statistics are calculated for individuals aged 18–64 who were not attending school at the time of the 2021 Canadian long-form Census. Gender identity is derived by combining information on current gender and sex assigned at birth, classifying individuals as cisgender men and women, transgender men and women, and nonbinary individuals assigned male (AMAB) or female (AFAB) at birth. Population centre classifications follow Statistics Canada definitions: small (1,000–29,999 residents), medium (30,000–99,999), and large urban centres (100,000 or more). All areas outside population centres are classified as rural areas. The number of observations is rounded to the nearest ten as required by Statistics Canada. Empirical Analysis One of the primary objectives of this study is to examine differences in labor market outcomes across gender identities. Specifically, we estimate the following regression model: $$\:{Y}_{i}=\:{\beta\:}_{0}+{\beta\:}_{1}cisgender\:wome{n}_{i}+{\beta\:}_{2}transgender\:me{n}_{i}+{\beta\:}_{3}transgender{\:women}_{i}+{\beta\:}_{4}NB﹘AMA{B}_{i}+\:{\beta\:}_{5}NB﹘AFA{B}_{i}+\gamma\:{X}_{i}+{\mu\:}_{i}$$ 1 We use various labor market outcomes as dependent variables, including employment status and log wages. Employment status is a binary variable equal to one if the individual had paid employment in 2020 and zero otherwise. When employment status is the dependent variable, we estimate a logistic regression model using the full sample. When log wages are the dependent variable, we estimate a linear regression model, restricting the sample to employed individuals with positive wages. For wage measures, we use hourly wages as our main outcome. Hourly wages are calculated by dividing 2020 gross wages and salaries by the product of usual weekly hours and weeks worked. The 2021 Census reports gross wages and salaries for 2020, prior to deductions such as income tax, pension plan contributions, and employment insurance premiums. It also provides the number of weeks worked in 2020 and usual weekly working hours. 5 However, the measure of usual weekly working hours refers to hours across all jobs during the reference week (May 2 to May 8) in 2021, which may differ from individuals’ usual weekly hours in 2020. As this is the only measure of hours available in the data, our construction of hourly wages relies on the assumption that weekly hours did not change substantially between 2020 and 2021. As robustness checks, we also report results using annual wages in 2020 and weekly earnings in 2020 in the Appendix. Given concerns that the 2020 labor market may have been affected by COVID-19, we further report results using annual wages in 2019, which are also available in the 2021 Census. Regarding the control variables \(\:{X}_{i}\) , we estimate the regression in a progressive manner. First, we run the regression without any control variables. Second, we include basic demographic controls: potential experience (defined as age minus years of education minus six) and its square, racial background (White vs. non-White), parental status (with or without children), marital status (opposite-sex married/common-law, same-sex married/common-law, and not married/common-law), immigration status (immigrant vs. Canadian-born), area of residence (rural areas, small population centers, medium population centers, and large urban centers), and province fixed effects. Lastly, we further control industry, occupation, and the skill requirements of the job. Industry sectors are classified according to the North American Industry Classification System (NAICS) Canada 2017 Version 3.0 (Statistics Canada, 2018 ). The NAICS classification system groups businesses based on similar economic activities, typically defined by common production processes. It divides the economy into 20 industry sectors, such as agriculture, health care, finance, and manufacturing. Occupation is described by the National Occupational Classification (NOC) 2021 Version 1.0, Canada’s official occupation classification system (Statistics Canada, 2021a ). In contrast to NAICS, which describes the type of business or industry, the NOC focuses on the type of work performed by individuals, regardless of the industry. It organizes occupations into ten broad categories (e.g., trades, transport, and equipment operators, and sales and service occupations), based on the first digit of the NOC code. Skill requirement is measured by the TEER system (Training, Education, Experience, and Responsibilities), which reflects the typical qualifications needed for each role. There are six TEER categories, ranging from 0 to 5. TEER 0 includes management roles; TEER 1 covers professional jobs usually requiring a university degree; TEER 2 includes positions needing a college diploma or long-term apprenticeship; TEER 3 applies to jobs requiring shorter college or apprenticeship training; TEER 4 includes roles needing a high school diploma or brief training; and TEER 5 covers jobs with minimal or no formal education requirements. In addition to the regressions using the full sample, we also conducted subsample analyses, which quantify gender earning disparities within each occupational group (10) and occupational TEER category (6). This detailed segmentation helps in understanding gender differences across different economic sectors and professional categories. Finally, we employ Oaxaca decomposition to analyze earnings discrepancies between cisgender versus non-cisgender individuals. This analytic approach highlights the extent to which wage disparities can be explained by measurable factors such as occupation and industry, as opposed to those that may arise from potential discrimination or other unobserved characteristics associated with gender identity. Results Distribution of occupations, industries, and TEER Before turning to the regression results, we first present descriptive evidence on lab Distribution of occupations, industries, and TEER Before turning to the regression results, we first present descriptive evidence on labor market outcomes, as these provide additional insights into the disparities faced by gender minorities. Table 2 presents labor market outcomes for our working sample, including annual wages, weeks and hours worked, and the distribution across occupation groups, industry sectors, and TEER categories, separately for each gender identity. Cisgender men exhibit the most favorable labor market outcomes. On average, they work 41 hours per week and 44 weeks annually, earning the highest average annual wage of about $69,000. In contrast, cisgender women work fewer hours per week (36) and weeks annually (42), with an average annual wage of about $49,000, much lower than that of cisgender men, reflecting the traditional gender wage gap. Transgender individuals work slightly fewer weeks than cisgender women and earn less, with an average annual wage of approximately $44,000 to $45,000. Among nonbinary individuals, those AMAB have outcomes closer to cisgender women, working similar hours and earning about $50,000 annually. In contrast, nonbinary AFAB people earn the lowest average wage, approximately $36,000. These patterns reveal a clear hierarchy of labor market outcomes, with cisgender men at the top, followed by NB-AMAB individuals, cisgender women, transgender individuals, and NB-AFAB individuals. Next, we examine the distribution of occupations by gender identity, which reveals notable differences. 6 Compared to cisgender men, cisgender women are more likely to work in business, health, education-related, sales, and service occupations, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. The gap is especially pronounced in the trades: 30 percent of cisgender men are employed in this sector, compared to only 3 percent of cisgender women. Transgender individuals are more concentrated in sales and service occupations compared to cisgender individuals. Relative to cisgender men, they are also more likely to work in health, education, and arts, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. However, their representation in these latter occupations is higher than that of cisgender women. For nonbinary individuals, we observe patterns broadly similar to those of transgender individuals, with some notable differences. Nonbinary individuals are even more likely to work in education and arts related fields, particularly NB-AFAB, among whom more than 20 percent are employed in education, law and social occupations. Additionally, more than 20 percent of NB-AMAB are employed in natural and applied sciences, making them the largest group in relative terms. Nonbinary individuals’ representation in trades, natural resources, and manufacturing is lower than that of transgender individuals but generally higher than that of cisgender women. Table 2 also presents the distribution by industry sector. Several patterns are evident: transgender and nonbinary individuals are less likely than cisgender men to be employed in utilities, construction, and manufacturing, but more likely to work in retail trade, health care, and service sectors such as educational services. While industry refers to the type of business, occupation captures the specific tasks performed; we therefore view occupation categories as a more accurate reflection of the type of work undertaken. Accordingly, our main analysis focuses on occupation groups. Lastly, we examine the distribution of occupations by TEER category. Cisgender men are more likely to hold management positions (TEER 0). In contrast, professional occupations (TEER 1) are more common among cisgender women, transgender women, and nonbinary individuals. Occupations with lower qualification requirements (TEER 4 and 5) are also more prevalent among transgender and nonbinary individuals compared to cisgender individuals. Regression Analysis: Employment Status We first estimate the regression model (equation (1)) using employment status as the dependent variable. Since employment status is a binary variable equal to one for individuals working for pay in 2020 and zero for all others, we estimate a logistic model using our full sample (the same as in Table 1). This definition allows us to examine differences in formal labor market employment by gender identity. Table 3 reports the average marginal effects (AME) on the probability of employment for various gender identities, with cisgender men as the reference group, from the logit model after controlling for covariates including potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The AME for cisgender women is -0.063, which indicates that, on average, cisgender women are 6.3 percentage points less likely to be employed compared to cisgender men, holding other factors constant. Transgender men and women are, respectively, 9.5 and 14.1 percentage points less likely to be employed than cisgender men. NB-AMAB and NB-AFAB people are, respectively, 7.5 and 11.2 percentage points less likely to be employed than cisgender men. All estimates are statistically significant at the 1% level, indicating that individuals in these gender identity groups are significantly less likely to be employed compared to cisgender men. The disparity is largest for transgender women and NB-AFAB people, highlighting potential disparities in employment access for these groups. Regression Analysis: Wages Table 4 reports the regression results using log hourly wage as the dependent variable to examine wage gaps across gender identities relative to cisgender men. Column 1 includes no control variables. Column 2 adds controls for potential experience and its square, immigration status, race, marital status, parental status, education, area of residence, and province fixed effects. Column 3 further incorporates occupation, TEER, and industry fixed effects. The bottom panel of Table 4 reports the p-values from tests of coefficient equality across all possible pairs of gender identity groups. Column 1, without any controls, reflects the raw average differences in hourly wages. The estimated coefficient for cisgender women is -0.110, implying they earn about 10.4 percent less (exp(-0.110) – 1 = -0.104) than cisgender men on average. The gaps are larger for all gender-diverse groups: approximately 18.0 percent for transgender women, 19.4 percent for NB-AMAB individuals, 22.3 percent for transgender men, and 29.6 percent for NB-AFAB individuals. In Column 2, after adding demographic characteristics, the traditional men–women wage gap widens to about 14.4 percent, primarily driven by women’s well-documented educational advantage in recent years (Blau & Kahn, 2017). For gender-diverse groups, the wage gaps narrow, most notably for transgender men, whose gap decreases by nearly half to 11.6 percent. NB-AMAB individuals, transgender women, and NB-AFAB individuals continue to face gaps of approximately 15.4, 15.7, and 24.0 percent, respectively, relative to cisgender men. In Column 3, after adding controls for occupation, TEER, and industry fixed effects, the earnings gaps narrow for all groups compared to Column 2: cisgender women’s gap falls to 12.2 percent, transgender men’s gap declines further to 7.7 percent, transgender women’s gap decreases to 13.1 percent, NB-AMAB individuals’ gap narrows to 13.0 percent, and NB-AFAB individuals’ gap decreases to 17.4 percent. The decline in wage gaps from Column 2 to Column 3 suggests that sorting across occupations and industries accounts for an important share of the observed disparities, particularly for transgender men and NB-AFAB individuals. For the robustness check, we restrict the sample to full-time workers—defined as those employed at least 30 hours per week for at least 27 weeks in 2020—and re-estimate the regressions. The results, reported in Table A2 of the Appendix, show a broadly similar pattern and indicate that wage disparities persist among full-time workers. Moreover, in the full specification with all controls, the earnings gaps for full-time transgender men and women relative to full-time cisgender men widen to approximately 14.6 percent and 20.3 percent, respectively, which are substantially larger than the corresponding estimates in Table 4. This suggests that full-time transgender workers experience even larger gaps compared to their cisgender male peers than do part-time workers. One concern is that wages in 2020 may be affected by COVID-19, so we also use 2019 annual wages, which are available in the data, as the dependent variable. The results are reported in Table A3 in the Appendix. For comparison, we also include results using 2020 annual wages. The patterns are largely similar, although transgender women and nonbinary individuals show slightly larger gaps in 2020, suggesting that these minority groups may have been more adversely affected by COVID-19. Carpenter, Feir, et al., (2025) use 2019 annual earnings as their main outcome but also show and acknowledge that their results remain unaffected when using 2020 data. Since weeks worked, hours, and occupation measures are all from 2020 or 2021, we use 2020 wages as our preferred measure. We also use alternative outcome variables—including log total annual earnings in 2020, log weekly wages, log weekly earnings, weeks worked, and hours worked—and re-estimate the regressions with the full set of covariates. The results, also reported in Table A3 of the Appendix, confirm the disadvantages faced by gender minorities in the labor market: transgender and nonbinary individuals work significantly fewer hours and weeks than cisgender men. The combination of reduced work time and lower hourly wages translates into even larger gaps in annual wages. The annual earnings variable we use in the Appendix is employment income in 2020, which includes self-employment income. 7 By using log annual earnings as the dependent variable, we incorporate the self-employed into the regression sample. The results using annual wages versus annual earnings are very similar, suggesting that the impact of excluding the self-employed and self-employment income is likely limited. Regression Analysis: Heterogeneity by Occupation To better understand wage gaps across gender identities, we estimate log hourly wage regressions separately for each occupation while controlling for demographic variables (the same controls as in Column 2 of Table 4). The coefficients on the gender identity dummies are reported in Table 5. The estimates highlight how wage gaps vary across occupations. Cisgender women face relatively smaller penalties in health (4.7 percent) and arts and recreation (8.4 percent), but substantially larger gaps in natural resources (29.8 percent). Transgender men exhibit small and statistically insignificant differences from cisgender men in health, trades, manufacturing, and natural resources, suggesting comparable wage outcomes in these fields; however, they experience significant penalties in legislative and management (64.4 percent) and arts and recreation (33.8 percent). Transgender women show relatively favorable outcomes in manufacturing, health, and natural sciences, but face disadvantages in education (22.7 percent) and arts and recreation (27.6 percent), with an especially large but imprecisely estimated penalty in legislative and management (54.8 percent). For nonbinary individuals, disparities are likewise occupation-specific: AMAB individuals exhibit relatively better outcomes in health and natural sciences but worse outcomes in legislative and management (65.5 percent) and sales (23.1 percent), whereas AFAB individuals perform better in arts but face significant penalties in legislative and management (40.6 percent) and natural resources (34.6 percent). Taken together, several patterns emerge. First, health occupations stand out as the sector where wage gaps across gender minority groups are consistently small (with the exception of NB-AFAB), suggesting a more equitable pay structure. In contrast, legislative and management positions exhibit the largest and most consistent disparities, particularly for transgender and nonbinary groups, though small sample sizes in some subgroups warrant cautious interpretation. Finally, in trades, manufacturing, and natural resources, transgender men experience relatively favorable wage outcomes, whereas other gender minority groups tend to face disadvantages. This pattern may reflect that these fields reward characteristics or norms traditionally associated with maleness. Regression Analysis: Heterogeneity by TEER We next estimate separate regressions of log hourly wages on gender identity within each TEER category, using cisgender men as the reference group. Table 6 reports the results and reveals heterogeneity across occupational skill levels. Cisgender women earn between 9 and 17 percent less than cisgender men across TEER levels, with the largest penalties observed in TEER 2 (16.3 percent) and TEER 5 (14.3 percent). Transgender men earn roughly 29 percent less than cisgender men in TEER 0, though the gap narrows substantially at lower TEER levels, with insignificant differences in TEER 3 through TEER 5. Transgender women face persistent penalties across all TEER levels except TEER 5, ranging from approximately 22 percent in TEER 0 to 12 percent in TEER 2. Similarly, NB-AMAB individuals experience large penalties in TEER 0 (36 percent) and consistent disadvantages across other categories, except TEER 3. NB-AFAB individuals face significant penalties across all occupations, including a 31 percent gap in TEER 0 and a 29 percent gap in TEER 2. Overall, the TEER-specific regressions reveal distinct gradients in wage inequality. Cisgender women face modest but relatively consistent penalties across all skill levels. In contrast, wage gaps for gender minorities are larger and more variable, with the steepest penalties observed in higher-skill occupations (particularly TEER 0). At the lower end of the skill distribution, such as TEER 5, penalties are much smaller for transgender individuals, though nonbinary individuals continue to experience sizable gaps. Oaxaca–Blinder Decompositions To further examine the extent to which wage gaps are attributable to compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination, we implement Oaxaca–Blinder decompositions. Table 7 reports the decomposition of the log hourly wage gap between cisgender individuals (cisgender men and women combined) and non-cisgender individuals (nonbinary and transgender people combined) among full-time workers. We restrict the sample to full-time workers to eliminate the influence of differences in hours worked. The decomposition separates the mean gender wage gap into two main components: the endowment effects (explained portion), which reflect differences in the average values of predictors across genders, and the coefficient effects (unexplained portion), which arise from variations in the relationship between these predictors and earnings across different gender identities. Because the primary explanatory variables—occupation and industry—are categorical, the choice of a reference or omitted group complicates interpretation. For instance, it becomes difficult to distinguish the part of the unexplained wage gap that is truly attributable to a group from the part driven by differences in the coefficients of the omitted category. To address this issue, we adopt a modified approach that normalizes coefficients by transforming the categorical variables prior to estimation, following the methods proposed by Oaxaca & Ransom, (1999), Gardeazabal & Ugidos, (2004), and M. Yun, (2005; 2008). The last three rows of Table 7 indicate that approximately 48 percent of the total wage gap is explained by observable characteristics, while the remaining 52 percent is unexplained. Experience emerges as the most substantial factor, accounting for approximately 40 percent of the wage gap. Industry also plays an important role, explaining about 10.2 percent of the observed disparity. Other notable contributors include marital status (6.0 percent), occupation (5.9 percent), occupational skill level as indicated by TEER (2.5 percent), and parental status (2.1 percent). By contrast, some factors contribute negatively to the wage gap, suggesting areas where noncisgender individuals hold relative advantages compared to cisgender counterparts. Immigration status accounts for a negative 3.9 percent, while province and rurality each contribute around -3.5 percent, reflecting geographic differences in opportunities. Education also contributes negatively by about 1.8 percent, consistent with earlier evidence of higher educational attainment among nonbinary individuals. Overall, these decomposition results suggest that while experience and occupational sorting are major drivers of wage disparities, certain demographic characteristics partly offset these gaps. or market outcomes, as these provide additional insights into the disparities faced by gender minorities. Table 2 presents labor market outcomes for our working sample, including annual wages, weeks and hours worked, and the distribution across occupation groups, industry sectors, and TEER categories, separately for each gender identity. Cisgender men exhibit the most favorable labor market outcomes. On average, they work 41 hours per week and 44 weeks annually, earning the highest average annual wage of about $ 69,000. In contrast, cisgender women work fewer hours per week (36) and weeks annually (42), with an average annual wage of about $ 49,000, much lower than that of cisgender men, reflecting the traditional gender wage gap. Transgender individuals work slightly fewer weeks than cisgender women and earn less, with an average annual wage of approximately $ 44,000 to $ 45,000. Among nonbinary individuals, those AMAB have outcomes closer to cisgender women, working similar hours and earning about $ 50,000 annually. In contrast, nonbinary AFAB people earn the lowest average wage, approximately $ 36,000. These patterns reveal a clear hierarchy of labor market outcomes, with cisgender men at the top, followed by NB-AMAB individuals, cisgender women, transgender individuals, and NB-AFAB individuals. Table 2 Descriptive statistics: individuals aged 18–64, not in school, and employed in 2020 Cisgender men Cisgender women Transgender men Transgender women Non-binary AMAB Non-binary AFAB N (%) 6,287,510 (53.36) 5,461,620 (46.35) 8,790 (0.07) 10,120 (0.09) 4,740 (0.04) 9,730 (0.08) Annual Wages ($) 69070.94 49422.38 44961.19 43817.53 49975.57 35616.45 Working Weeks 43.74 42.28 40.10 39.89 39.77 38.04 Working Hours 40.81 36.07 37.56 36.36 38.24 34.14 Occupational Group Legislative and senior management 2.04% 1.04% 1.14% 0.99% 0.63% 0.51% Business, finance and administration 11.71% 28.74% 12.06% 19.17% 16.03% 17.47% Natural and applied sciences 14.70% 5.25% 8.76% 10.08% 21.10% 8.02% Health 3.02% 14.59% 5.57% 10.28% 2.95% 6.27% Education, law and social 8.32% 18.59% 11.72% 15.02% 12.03% 20.97% Art, culture, recreation and sport 2.21% 2.73% 3.87% 3.95% 8.02% 13.05% Sales and service 17.47% 22.07% 29.58% 24.80% 23.00% 27.03% Trades, transport and equipment 30.20% 3.18% 19.68% 10.28% 10.97% 3.49% Natural resources, agriculture 3.21% 0.86% 2.16% 1.48% 1.90% 1.54% Manufacturing and utilities 7.11% 2.95% 5.46% 4.05% 3.38% 1.75% Industry sectors Agriculture, forestry, fishing and hunting 2.17% 1.09% 1.82% 1.28% 1.05% 1.03% Mining, quarrying, and oil and gas 2.34% 0.58% 1.37% 0.69% 1.27% 0.41% Utilities 1.49% 0.56% 0.57% 0.59% 0.63% 0.21% Construction 12.59% 2.31% 7.96% 5.34% 3.59% 1.34% Manufacturing 13.24% 5.69% 9.22% 6.72% 6.12% 3.91% Wholesale trade 4.90% 2.70% 3.19% 2.47% 2.53% 1.54% Retail trade 9.25% 10.68% 15.70% 12.94% 12.45% 13.46% Transportation and warehousing 7.30% 2.81% 5.46% 4.55% 3.80% 1.44% Information and cultural industries 2.77% 2.06% 3.07% 3.85% 7.81% 6.89% Finance and insurance 4.20% 6.37% 2.96% 3.75% 3.16% 2.16% Real estate and rental and leasing 1.42% 1.42% 1.37% 0.99% 0.42% 0.72% Professional, scientific and technical services 9.15% 7.79% 6.94% 9.58% 16.67% 10.48% Management of companies and enterprises 0.27% 0.34% 0.46% 0.30% 0.42% 0.21% Administrative and support, waste management and remediation services 3.98% 2.98% 3.98% 4.55% 5.49% 4.21% Educational services 4.53% 11.88% 5.92% 8.50% 5.70% 11.31% Health care and social assistance 4.80% 23.32% 11.26% 17.69% 7.17% 14.70% Arts, entertainment and recreation 1.12% 1.12% 1.48% 1.58% 3.38% 4.93% Accommodation and food services 3.30% 4.33% 7.39% 4.94% 6.54% 7.09% Other services 3.50% 3.62% 4.10% 3.56% 4.01% 6.06% Public administration 7.69% 8.36% 6.03% 6.42% 8.23% 8.02% Occupational Teer Teer 0 15.23% 11.30% 9.56% 9.09% 8.44% 7.81% Teer 1 18.63% 25.81% 15.36% 24.11% 29.75% 27.65% Teer 2 27.48% 17.74% 23.78% 18.87% 23.42% 20.76% Teer 3 13.91% 19.16% 15.81% 16.40% 11.18% 14.29% Teer 4 12.80% 16.29% 17.29% 17.49% 15.61% 17.16% Teer 5 11.94% 9.72% 18.20% 13.93% 11.39% 12.33% Notes: Weighted descriptive statistics are based on individuals aged 18–64 who were not enrolled in school and were employed in 2020, as reported in the 2021 Canadian long-form Census. We exclude self-employed individuals and restrict the sample to those with valid occupation and industry codes. Annual wages refer to gross wages and salaries for the calendar year 2020 (before deductions). Weeks worked are reported for 2020, including paid leave or training. Working hours are measured for the 2021 reference week (May 2–8) across all jobs. Gender identity is derived by combining current gender and sex assigned at birth, classifying individuals as cisgender men and women, transgender men and women, and nonbinary individuals assigned male (AMAB) or female (AFAB) at birth. The number of observations is rounded to the nearest ten as required by Statistics Canada. Next, we examine the distribution of occupations by gender identity, which reveals notable differences. Compared to cisgender men, cisgender women are more likely to work in business, health, education-related, sales, and service occupations, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. The gap is especially pronounced in the trades: 30 percent of cisgender men are employed in this sector, compared to only 3 percent of cisgender women. Transgender individuals are more concentrated in sales and service occupations compared to cisgender individuals. Relative to cisgender men, they are also more likely to work in health, education, and arts, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. However, their representation in these latter occupations is higher than that of cisgender women. For nonbinary individuals, we observe patterns broadly similar to those of transgender individuals, with some notable differences. Nonbinary individuals are even more likely to work in education and arts related fields, particularly NB-AFAB, among whom more than 20 percent are employed in education, law and social occupations. Additionally, more than 20 percent of NB-AMAB are employed in natural and applied sciences, making them the largest group in relative terms. Nonbinary individuals’ representation in trades, natural resources, and manufacturing is lower than that of transgender individuals but generally higher than that of cisgender women. Table 2 also presents the distribution by industry sector. Several patterns are evident: transgender and nonbinary individuals are less likely than cisgender men to be employed in utilities, construction, and manufacturing, but more likely to work in retail trade, health care, and service sectors such as educational services. While industry refers to the type of business, occupation captures the specific tasks performed; we therefore view occupation categories as a more accurate reflection of the type of work undertaken. Accordingly, our main analysis focuses on occupation groups. Lastly, we examine the distribution of occupations by TEER category. Cisgender men are more likely to hold management positions (TEER 0). In contrast, professional occupations (TEER 1) are more common among cisgender women, transgender women, and nonbinary individuals. Occupations with lower qualification requirements (TEER 4 and 5) are also more prevalent among transgender and nonbinary individuals compared to cisgender individuals. Regression Analysis: Employment Status We first estimate the regression model (Eq. ( 1 )) using employment status as the dependent variable. Since employment status is a binary variable equal to one for individuals working for pay in 2020 and zero for all others, we estimate a logistic model using our full sample (the same as in Table 1 ). This definition allows us to examine differences in formal labor market employment by gender identity. Table 3 reports the average marginal effects (AME) on the probability of employment for various gender identities, with cisgender men as the reference group, from the logit model after controlling for covariates including potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The AME for cisgender women is -0.063, which indicates that, on average, cisgender women are 6.3 percentage points less likely to be employed compared to cisgender men, holding other factors constant. Transgender men and women are, respectively, 9.5 and 14.1 percentage points less likely to be employed than cisgender men. NB-AMAB and NB-AFAB people are, respectively, 7.5 and 11.2 percentage points less likely to be employed than cisgender men. All estimates are statistically significant at the 1% level, indicating that individuals in these gender identity groups are significantly less likely to be employed compared to cisgender men. The disparity is largest for transgender women and NB-AFAB people, highlighting potential disparities in employment access for these groups. Table 3 Average marginal effect of gender identities (relative to cisgender men) on employment status, results from logit model dy/dx std. err. z P > z [95% conf. interval] Cisgender women -0.063 0.000 -153.240 0.000 -0.063 -0.062 Transgender men -0.095 0.007 -12.950 0.000 -0.110 -0.081 Transgender women -0.141 0.007 -21.440 0.000 -0.154 -0.128 Non-binary AMAB -0.075 0.010 -7.380 0.000 -0.095 -0.055 Non-binary AFAB -0.112 0.007 -15.570 0.000 -0.127 -0.098 Weighted N = 19,546,000; Pseudo R Square = 0.07 Notes: Average marginal effects are estimated from logistic regressions of employment status (1 = employed for pay in 2020, 0 = otherwise) on gender identity, using cisgender men as the reference category. The sample includes individuals aged 18–64 who were not enrolled in school, as reported in the 2021 Canadian long-form Census. Estimation controls for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The number of observations is rounded to the nearest ten as required by Statistics Canada. AMAB = assigned male at birth; AFAB = assigned female at birth. Regression Analysis: Wages Table 4 reports the regression results using log hourly wage as the dependent variable to examine wage gaps across gender identities relative to cisgender men. Column 1 includes no control variables. Column 2 adds controls for potential experience and its square, immigration status, race, marital status, parental status, education, area of residence, and province fixed effects. Column 3 further incorporates occupation, TEER, and industry fixed effects. The bottom panel of Table 4 reports the p-values from tests of coefficient equality across all possible pairs of gender identity groups. Table 4 Estimated log hourly wage across gender identities relative to cisgender men (1) (2) (3) Cisgender women -0.110*** -0.155*** -0.130*** (0.001) (0.001) (0.001) Transgender men -0.252*** -0.123*** -0.080*** (0.024) (0.023) (0.023) Transgender women -0.198*** -0.171*** -0.140*** (0.022) (0.022) (0.022) Non-binary AMAB -0.216*** -0.167*** -0.139*** (0.032) (0.031) (0.030) Non-binary AFAB -0.351*** -0.275*** -0.191*** (0.022) (0.021) (0.021) Potential experience and its square x x Immigration x x Race x x Marital Status x x Parent x x Education x x Areas of residence x x Provinces x x Occupational group x Teer x Industry group x Weighted N 11,611,050 11,611,050 11,611,050 R Square 0.004 0.088 0.136 p-value testing two coefficients being equal Cis-women = Transmen 0.000 0.176 0.028 Cis-women = Transwomen 0.000 0.452 0.654 Cis-women = NB-AMAB 0.001 0.696 0.773 Cis-women = NB-AFAB 0.000 0.000 0.004 Transmen = Transwomen 0.103 0.134 0.056 Transmen = NB-AMAB 0.369 0.260 0.117 Transmen = NB-AFAB 0.002 0.000 0.000 Transwomen = NB-AMAB 0.650 0.903 0.976 Transwomen = NB-AFAB 0.000 0.001 0.093 NB-AMAB = NB-AFAB 0.001 0.004 0.158 Notes: Estimated coefficients are from linear regressions of log hourly wages on gender identity, with cisgender men as the reference group. Model (1) is unadjusted; Model (2) adds controls for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province; Model (3) further adjusts for occupation group, TEER category, and industry sector. Robust standard errors are reported in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.10. AMAB = assigned male at birth; AFAB = assigned female at birth. Column 1, without any controls, reflects the raw average differences in hourly wages. The estimated coefficient for cisgender women is -0.110, implying they earn about 10.4 percent less (exp(-0.110) – 1 = -0.104) than cisgender men on average. The gaps are larger for all gender-diverse groups: approximately 18.0 percent for transgender women, 19.4 percent for NB-AMAB individuals, 22.3 percent for transgender men, and 29.6 percent for NB-AFAB individuals. In Column 2, after adding demographic characteristics, the traditional men–women wage gap widens to about 14.4 percent, primarily driven by women’s well-documented educational advantage in recent years (Blau & Kahn, 2017 ). For gender-diverse groups, the wage gaps narrow, most notably for transgender men, whose gap decreases by nearly half to 11.6 percent. NB-AMAB individuals, transgender women, and NB-AFAB individuals continue to face gaps of approximately 15.4, 15.7, and 24.0 percent, respectively, relative to cisgender men. In Column 3, after adding controls for occupation, TEER, and industry fixed effects, the earnings gaps narrow for all groups compared to Column 2: cisgender women’s gap falls to 12.2 percent, transgender men’s gap declines further to 7.7 percent, transgender women’s gap decreases to 13.1 percent, NB-AMAB individuals’ gap narrows to 13.0 percent, and NB-AFAB individuals’ gap decreases to 17.4 percent. The decline in wage gaps from Column 2 to Column 3 suggests that sorting across occupations and industries accounts for an important share of the observed disparities, particularly for transgender men and NB-AFAB individuals. For the robustness check, we restrict the sample to full-time workers—defined as those employed at least 30 hours per week for at least 27 weeks in 2020—and re-estimate the regressions. The results, reported in Table A2 of the Appendix, show a broadly similar pattern and indicate that wage disparities persist among full-time workers. Moreover, in the full specification with all controls, the earnings gaps for full-time transgender men and women relative to full-time cisgender men widen to approximately 14.6 percent and 20.3 percent, respectively, which are substantially larger than the corresponding estimates in Table 4 . This suggests that full-time transgender workers experience even larger gaps compared to their cisgender male peers than do part-time workers. One concern is that wages in 2020 may be affected by COVID-19, so we also use 2019 annual wages, which are available in the data, as the dependent variable. The results are reported in Table A3 in the Appendix. For comparison, we also include results using 2020 annual wages. The patterns are largely similar, although transgender women and nonbinary individuals show slightly larger gaps in 2020, suggesting that these minority groups may have been more adversely affected by COVID-19. Carpenter, Feir, et al., ( 2025 ) use 2019 annual earnings as their main outcome but also show and acknowledge that their results remain unaffected when using 2020 data. Since weeks worked, hours, and occupation measures are all from 2020 or 2021, we use 2020 wages as our preferred measure. We also use alternative outcome variables—including log total annual earnings in 2020, log weekly wages, log weekly earnings, weeks worked, and hours worked—and re-estimate the regressions with the full set of covariates. The results, also reported in Table A3 of the Appendix, confirm the disadvantages faced by gender minorities in the labor market: transgender and nonbinary individuals work significantly fewer hours and weeks than cisgender men. The combination of reduced work time and lower hourly wages translates into even larger gaps in annual wages. The annual earnings variable we use in the Appendix is employment income in 2020, which includes self-employment income. By using log annual earnings as the dependent variable, we incorporate the self-employed into the regression sample. The results using annual wages versus annual earnings are very similar, suggesting that the impact of excluding the self-employed and self-employment income is likely limited. Regression Analysis: Heterogeneity by Occupation To better understand wage gaps across gender identities, we estimate log hourly wage regressions separately for each occupation while controlling for demographic variables (the same controls as in Column 2 of Table 4 ). The coefficients on the gender identity dummies are reported in Table 5 . The estimates highlight how wage gaps vary across occupations. Cisgender women face relatively smaller penalties in health (4.7 percent) and arts and recreation (8.4 percent), but substantially larger gaps in natural resources (29.8 percent). Transgender men exhibit small and statistically insignificant differences from cisgender men in health, trades, manufacturing, and natural resources, suggesting comparable wage outcomes in these fields; however, they experience significant penalties in legislative and management (64.4 percent) and arts and recreation (33.8 percent). Transgender women show relatively favorable outcomes in manufacturing, health, and natural sciences, but face disadvantages in education (22.7 percent) and arts and recreation (27.6 percent), with an especially large but imprecisely estimated penalty in legislative and management (54.8 percent). For nonbinary individuals, disparities are likewise occupation-specific: AMAB individuals exhibit relatively better outcomes in health and natural sciences but worse outcomes in legislative and management (65.5 percent) and sales (23.1 percent), whereas AFAB individuals perform better in arts but face significant penalties in legislative and management (40.6 percent) and natural resources (34.6 percent). Taken together, several patterns emerge. First, health occupations stand out as the sector where wage gaps across gender minority groups are consistently small (with the exception of NB-AFAB), suggesting a more equitable pay structure. In contrast, legislative and management positions exhibit the largest and most consistent disparities, particularly for transgender and nonbinary groups, though small sample sizes in some subgroups warrant cautious interpretation. Finally, in trades, manufacturing, and natural resources, transgender men experience relatively favorable wage outcomes, whereas other gender minority groups tend to face disadvantages. This pattern may reflect that these fields reward characteristics or norms traditionally associated with maleness. Table 5 Estimated difference in log hourly wage across gender identities relative to cisgender men in each occupation group Legislative and senior management Business, finance and administration Natural and applied sciences Health Education, law and social Art, culture, recreation and sport Sales and service Trades, transport and equipment Natural resources, agriculture Manufacturing and utilities Cisgender women -0.161*** -0.158*** -0.117*** -0.048*** -0.238*** -0.088*** -0.170*** -0.097*** -0.354*** -0.230*** (0.011) (0.003) (0.003) (0.005) (0.003) (0.010) (0.003) (0.005) (0.014) (0.005) Transgender men -1.032*** -0.157*** -0.118** 0.012 -0.251*** -0.413** -0.092** -0.014 0.126 -0.04 (0.389) (0.056) (0.060) (0.073) (0.064) (0.171) (0.044) (0.051) (0.228) (0.081) Transgender women -0.794 -0.166*** -0.097 -0.091 -0.257*** -0.323** -0.159*** -0.156** -0.163 -0.045 (0.526) (0.045) (0.075) (0.061) (0.044) (0.160) (0.043) (0.068) (0.210) (0.114) Non-binary AMAB -1.063* -0.248*** -0.025 0.075 -0.150** -0.085 -0.263*** -0.158** -0.158 -0.215 (0.552) (0.070) (0.066) (0.125) (0.072) (0.146) (0.059) (0.078) (0.418) (0.156) Non-binary AFAB -0.521** -0.232*** -0.242*** -0.237** -0.248*** -0.101 -0.296*** -0.197 -0.425*** -0.246** (0.258) (0.042) (0.057) (0.094) (0.041) (0.076) (0.039) (0.156) (0.154) (0.112) Weighted N 185,410 2,275,100 1,181,400 968,950 1,558,320 283,680 2,271,460 2,044,820 246,970 594,940 R Square 0.125 0.086 0.092 0.053 0.109 0.028 0.066 0.058 0.084 0.111 Notes: Estimates are from separate linear regressions of log hourly wages on gender identity for each major occupational group, using cisgender men as the reference category. All models control for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province of residence. Robust standard errors are shown in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.10. AMAB = assigned male at birth; AFAB = assigned female at birth. Regression Analysis: Heterogeneity by TEER We next estimate separate regressions of log hourly wages on gender identity within each TEER category, using cisgender men as the reference group. Table 6 reports the results and reveals heterogeneity across occupational skill levels. Cisgender women earn between 9 and 17 percent less than cisgender men across TEER levels, with the largest penalties observed in TEER 2 (16.3 percent) and TEER 5 (14.3 percent). Transgender men earn roughly 29 percent less than cisgender men in TEER 0, though the gap narrows substantially at lower TEER levels, with insignificant differences in TEER 3 through TEER 5. Transgender women face persistent penalties across all TEER levels except TEER 5, ranging from approximately 22 percent in TEER 0 to 12 percent in TEER 2. Similarly, NB-AMAB individuals experience large penalties in TEER 0 (36 percent) and consistent disadvantages across other categories, except TEER 3. NB-AFAB individuals face significant penalties across all occupations, including a 31 percent gap in TEER 0 and a 29 percent gap in TEER 2. Overall, the TEER-specific regressions reveal distinct gradients in wage inequality. Cisgender women face modest but relatively consistent penalties across all skill levels. In contrast, wage gaps for gender minorities are larger and more variable, with the steepest penalties observed in higher-skill occupations (particularly TEER 0). At the lower end of the skill distribution, such as TEER 5, penalties are much smaller for transgender individuals, though nonbinary individuals continue to experience sizable gaps. Table 6 Estimated difference in log hourly wage across gender identities relative to cisgender men in each TEER Teer 0 Teer 1 Teer 2 Teer 3 Teer 4 Teer 5 Cisgender women -0.137*** -0.109*** -0.178*** -0.097*** -0.136*** -0.154*** (0.003) (0.002) (0.002) (0.003) (0.003) (0.003) Transgender men -0.342*** -0.164*** -0.095** -0.057 -0.05 -0.029 (0.091) (0.059) (0.042) (0.059) (0.056) (0.051) Transgender women -0.247*** -0.194*** -0.126*** -0.156*** -0.166*** -0.032 (0.083) (0.040) (0.048) (0.056) (0.050) (0.064) Non-binary AMAB -0.448*** -0.093* -0.123** -0.077 -0.203** -0.130* (0.152) (0.055) (0.056) (0.078) (0.086) (0.068) Non-binary AFAB -0.374*** -0.203*** -0.337*** -0.163*** -0.247*** -0.133** (0.055) (0.040) (0.053) (0.062) (0.040) (0.058) Weighted N 1,550,850 2,536,560 2,664,710 1,909,920 1,675,630 1,273,380 R Square 0.121 0.056 0.068 0.033 0.049 0.041 Notes: Estimates are derived from separate linear regressions of log hourly wages on gender identity within each TEER (Training, Education, Experience, and Responsibilities) category, using cisgender men as the reference group. All regressions control for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province of residence. Robust standard errors are reported in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.10. AMAB = assigned male at birth; AFAB = assigned female at birth. Oaxaca–Blinder Decompositions To further examine the extent to which wage gaps are attributable to compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination, we implement Oaxaca–Blinder decompositions. Table 7 reports the decomposition of the log hourly wage gap between cisgender individuals (cisgender men and women combined) and non-cisgender individuals (nonbinary and transgender people combined) among full-time workers. We restrict the sample to full-time workers to eliminate the influence of differences in hours worked. The decomposition separates the mean gender wage gap into two main components: the endowment effects (explained portion), which reflect differences in the average values of predictors across genders, and the coefficient effects (unexplained portion), which arise from variations in the relationship between these predictors and earnings across different gender identities. Because the primary explanatory variables—occupation and industry—are categorical, the choice of a reference or omitted group complicates interpretation. For instance, it becomes difficult to distinguish the part of the unexplained wage gap that is truly attributable to a group from the part driven by differences in the coefficients of the omitted category. To address this issue, we adopt a modified approach that normalizes coefficients by transforming the categorical variables prior to estimation, following the methods proposed by Oaxaca & Ransom, ( 1999 ), Gardeazabal & Ugidos, ( 2004 ), and M. Yun, ( 2005 ; 2008 ). Table 7 Decomposition of wage gap between cisgender (combined cisgender men and women) and non-cisgender (combined nonbinary and transgender people) full-time workers Coefficient P > t [95% conf. interval] Percentage Experience 0.080 0.000 0.076 0.085 40.1% Marriage 0.012 0.000 0.011 0.013 6.0% Immigration -0.008 0.000 -0.009 -0.007 -3.9% Race -0.005 0.000 -0.006 -0.004 -2.4% Parent 0.004 0.000 0.004 0.005 2.1% Province -0.007 0.000 -0.009 -0.005 -3.5% Rurality -0.007 0.000 -0.008 -0.006 -3.4% Education -0.004 0.032 -0.007 0.000 -1.8% Occupation 0.012 0.000 0.010 0.014 5.9% Teer 0.005 0.006 0.001 0.009 2.5% Industry 0.020 0.000 0.017 0.024 10.2% Total explained 0.104 0.000 0.094 0.114 51.8% Total unexplained 0.097 0.000 0.080 0.114 48.2% Total wage gap 0.200 0.000 0.181 0.220 100.0% Weighted N = 11,201,270 Notes: This table presents Oaxaca–Blinder decompositions of the mean log hourly wage gap between cisgender (cisgender men and women combined) and non-cisgender (transgender and non-binary) full-time workers. The decomposition separates the total gap into an explained component—attributable to group differences in characteristics such as experience, marital status, immigration, race, parental status, province, rurality, education, occupation, TEER category, and industry—and an unexplained component, reflecting differences in returns to these characteristics. The percentage column shows each factor’s contribution to the total explained portion of the wage gap. The number of observations is rounded to the nearest ten as required by Statistics Canada. AMAB = assigned male at birth; AFAB = assigned female at birth. The last three rows of Table 7 indicate that approximately 48 percent of the total wage gap is explained by observable characteristics, while the remaining 52 percent is unexplained. Experience emerges as the most substantial factor, accounting for approximately 40 percent of the wage gap. Industry also plays an important role, explaining about 10.2 percent of the observed disparity. Other notable contributors include marital status (6.0 percent), occupation (5.9 percent), occupational skill level as indicated by TEER (2.5 percent), and parental status (2.1 percent). By contrast, some factors contribute negatively to the wage gap, suggesting areas where noncisgender individuals hold relative advantages compared to cisgender counterparts. Immigration status accounts for a negative 3.9 percent, while province and rurality each contribute around − 3.5 percent, reflecting geographic differences in opportunities. Education also contributes negatively by about 1.8 percent, consistent with earlier evidence of higher educational attainment among nonbinary individuals. Overall, these decomposition results suggest that while experience and occupational sorting are major drivers of wage disparities, certain demographic characteristics partly offset these gaps. Discussion and Conclusion In this paper, we use the 2021 Canadian Census to examine the labor market outcomes of transgender and nonbinary individuals. Our findings highlight persistent and sizable earnings disparities for transgender and nonbinary individuals in the Canadian labor market. Transgender women and nonbinary individuals earn roughly 15 percent less per hour than comparable cisgender men, after adjusting for demographic, occupational, and industrial factors. Transgender men fare somewhat better: although their raw wage gaps are sizable, about two-thirds of the difference is explained by demographic characteristics and sorting across jobs and sectors, and in the full specification their earnings penalties shrink to less than 10 percent. Combining the distribution by occupation group in Table 2 with the subgroup analyses in Table 5 reveals several important patterns. In legislative and senior management occupations, transgender and nonbinary individuals are underrepresented relative to cisgender men, and this category also shows the largest wage gaps across all occupations, indicating both barriers to entry and disadvantageous treatment within the field. In health-related occupations, by contrast, transgender and nonbinary individuals are more represented than cisgender men, and wage gaps within the occupation are much smaller (except for NB-AFAB), suggesting fewer barriers to entry and a more equitable pay structure. Transgender and nonbinary individuals are also overrepresented in sales and service compared to cisgender individuals, but wage gaps in this sector remain sizable and significant. In arts and sports, nonbinary individuals are heavily represented and experience relatively favorable wage outcomes. In traditionally male-dominated occupations such as trades, natural resources, and manufacturing, transgender men are more represented than other gender minority groups, and their wages are not significantly different from those of cisgender men. Taken together, these patterns highlight substantial heterogeneity across occupations—variation that has not been systematically examined in the existing literature. We also decompose the wage gap between cisgender and non-cisgender individuals and find that roughly half can be explained by observable characteristics, while the remaining half is unexplained. Compared to the literature showing that most of the traditional male–female wage gap is accounted for by observable factors (Blau & Kahn, 2017 ), our results suggest a different pattern for transgender and nonbinary individuals. While the unexplained component cannot be fully attributed to discrimination, since it may also reflect unobserved productivity-related characteristics, the relatively larger unexplained share in the gap indicates that discrimination may play a more prominent role than in the male–female gap. Our findings are broadly consistent with prior evidence from the United States and other OECD countries, which has documented persistent wage and employment disadvantages for transgender and nonbinary individuals (Aksoy et al., 2025 ; Badgett et al., 2024 ; Carpenter et al., 2020 ; Carpenter, Goodman, et al., 2024). Using BRFSS data, Carpenter et al., ( 2020 ) reported that transgender adults in the United States were 11 percentage points less likely to be employed and earned 16–20 percent less than cisgender men, even after adjusting for demographics. Drawing on linked U.S. Social Security and IRS administrative records, Carpenter, Goodman, et al., (2024) estimated a 6–13 log-point wage penalty for transgender workers, with larger gaps among transgender women. Experimental evidence from Aksoy et al., ( 2025 ) revealed that transgender applicants in Europe were 22–25 percent less likely to receive callbacks than comparable cisgender applicants, while Shannon, ( 2022 ) found that individuals assigned female at birth who identify as transgender or nonbinary earn 10–15 percent less than their assigned-male peers, underscoring a persistent “maleness premium.” In a Latin American context, Nettuno, ( 2024 ) reported that transgender and nonbinary workers in Chile face an average 18 percent earnings penalty relative to cisgender men, with nonbinary individuals experiencing the largest gaps. Together with Badgett et al., ( 2024 ), who show global LGBTQ + wage gaps ranging from 5 to 30 percent, these studies suggest pervasive, cross-national inequities. Our Canadian results fall toward the upper end of this range, with wage penalties of 15–20 percent for transgender women and nonbinary individuals after covariate adjustment. A key advantage of our study lies in the unprecedented sample size of the 2021 Canadian Census, which enables detailed subgroup analysis across occupational and skill sectors—something unattainable in prior survey-based studies. This granularity reveals that wage gaps are not uniform but concentrated in specific occupational contexts, providing new evidence on the structural sources of gender-diverse wage inequality. Several limitations should be acknowledged. Although the 2021 Census provides an unprecedented opportunity to examine labor market outcomes by gender identity, the Census does not collect information on sexual orientation, workplace climate, discrimination experiences, or gender transition history, all of which could shed light on the mechanisms underlying observed wage disparities. Future research could integrate Census microdata with administrative earnings records or specialized survey data to capture these dimensions and explore how they interact with labor market processes. As more countries incorporate gender identity measures into national censuses, cross-national comparative studies will be essential for understanding how institutional and policy environments shape the economic inclusion of gender-diverse populations. Discussion and Conclusion In this paper, we use the 2021 Canadian Census to examine the labor market outcomes of transgender and nonbinary individuals. Our findings highlight persistent and sizable earnings disparities for transgender and nonbinary individuals in the Canadian labor market. Transgender women and nonbinary individuals earn roughly 15 percent less per hour than comparable cisgender men, after adjusting for demographic, occupational, and industrial factors. Transgender men fare somewhat better: although their raw wage gaps are sizable, about two-thirds of the difference is explained by demographic characteristics and sorting across jobs and sectors, and in the full specification their earnings penalties shrink to less than 10 percent. Combining the distribution by occupation group in Table 2 with the subgroup analyses in Table 5 reveals several important patterns. In legislative and senior management occupations, transgender and nonbinary individuals are underrepresented relative to cisgender men, and this category also shows the largest wage gaps across all occupations, indicating both barriers to entry and disadvantageous treatment within the field. In health-related occupations, by contrast, transgender and nonbinary individuals are more represented than cisgender men, and wage gaps within the occupation are much smaller (except for NB-AFAB), suggesting fewer barriers to entry and a more equitable pay structure. Transgender and nonbinary individuals are also overrepresented in sales and service compared to cisgender individuals, but wage gaps in this sector remain sizable and significant. In arts and sports, nonbinary individuals are heavily represented and experience relatively favorable wage outcomes. In traditionally male-dominated occupations such as trades, natural resources, and manufacturing, transgender men are more represented than other gender minority groups, and their wages are not significantly different from those of cisgender men. Taken together, these patterns highlight substantial heterogeneity across occupations—variation that has not been systematically examined in the existing literature. We also decompose the wage gap between cisgender and non-cisgender individuals and find that roughly half can be explained by observable characteristics, while the remaining half is unexplained. Compared to the literature showing that most of the traditional male–female wage gap is accounted for by observable factors (Blau & Kahn, 2017), our results suggest a different pattern for transgender and nonbinary individuals. While the unexplained component cannot be fully attributed to discrimination, since it may also reflect unobserved productivity-related characteristics, the relatively larger unexplained share in the gap indicates that discrimination may play a more prominent role than in the male–female gap. Our findings are broadly consistent with prior evidence from the United States and other OECD countries, which has documented persistent wage and employment disadvantages for transgender and nonbinary individuals (Aksoy et al., 2025; Badgett et al., 2024; Carpenter et al., 2020; Carpenter, Goodman, et al., 2024). Using BRFSS data, Carpenter et al., (2020) reported that transgender adults in the United States were 11 percentage points less likely to be employed and earned 16–20 percent less than cisgender men, even after adjusting for demographics. Drawing on linked U.S. Social Security and IRS administrative records, Carpenter, Goodman, et al., (2024) estimated a 6–13 log-point wage penalty for transgender workers, with larger gaps among transgender women. Experimental evidence from Aksoy et al., (2025) revealed that transgender applicants in Europe were 22–25 percent less likely to receive callbacks than comparable cisgender applicants, while Shannon, (2022) found that individuals assigned female at birth who identify as transgender or nonbinary earn 10–15 percent less than their assigned-male peers, underscoring a persistent “maleness premium.” In a Latin American context, Nettuno, (2024) reported that transgender and nonbinary workers in Chile face an average 18 percent earnings penalty relative to cisgender men, with nonbinary individuals experiencing the largest gaps. Together with Badgett et al., (2024), who show global LGBTQ+ wage gaps ranging from 5 to 30 percent, these studies suggest pervasive, cross-national inequities. Our Canadian results fall toward the upper end of this range, with wage penalties of 15–20 percent for transgender women and nonbinary individuals after covariate adjustment. A key advantage of our study lies in the unprecedented sample size of the 2021 Canadian Census, which enables detailed subgroup analysis across occupational and skill sectors—something unattainable in prior survey-based studies. This granularity reveals that wage gaps are not uniform but concentrated in specific occupational contexts, providing new evidence on the structural sources of gender-diverse wage inequality. Several limitations should be acknowledged. Although the 2021 Census provides an unprecedented opportunity to examine labor market outcomes by gender identity, the Census does not collect information on sexual orientation, workplace climate, discrimination experiences, or gender transition history, all of which could shed light on the mechanisms underlying observed wage disparities. Future research could integrate Census microdata with administrative earnings records or specialized survey data to capture these dimensions and explore how they interact with labor market processes. As more countries incorporate gender identity measures into national censuses, cross-national comparative studies will be essential for understanding how institutional and policy environments shape the economic inclusion of gender-diverse populations. Declarations Conflict of Interest The authors declare that they have no conflict of interest. Funding: The study is supported by Antony Chum through the Canada Research Chair program (CRC-2021-00269). The project was also partially funded by the Social Sciences and Humanities Research (SSHRC) Project Grant, File no: 435-2023-1102, and the Canadian Institutes of Health Research (CIHR) Project Grant, File no: 497334. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Author Contribution Y.B., A.C., and Q.L. wrote the main manuscript text, and Y.B. prepared the tables and figures. All authors reviewed the manuscript. Data Availability This study is based on confidential microdata accessed through the Statistics Canada Research Data Centre (RDC). Researchers can apply for access to these data through Statistics Canada’s Research Data Centres Program at [](https:/www.statcan.gc.ca/en/microdata/data-centres/access) [https://www.statcan.gc.ca/en/microdata/data-centres/access](https:/www.statcan.gc.ca/en/microdata/data-centres/access) . References Aksoy, B., Carpenter, C. S., & Sansone, D. (2025). Understanding labor market discrimination against transgender people: Evidence from a double list experiment and a survey. Management Science , 71 (1), 659–677. Badgett, M. L., Carpenter, C. S., Lee, M. J., & Sansone, D. (2024). A review of the economics of sexual orientation and gender identity. Journal of Economic Literature , 62 (3), 948–994. Blau, F. D., & Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. Journal of Economic Literature , 55 (3), 789–865. Carpenter, C. S., Eppink, S. T., & Gonzales, G. (2020). Transgender status, gender identity, and socioeconomic outcomes in the United States. ILR Review , 73 (3), 573–599. Carpenter, C. S., Feir, D. L., Pendakur, K., & Warman, C. (2024). Nonbinary Gender Identities and Earnings: Evidence from a National Census . National Bureau of Economic Research. Carpenter, C. S., Feir, D., Pendakur, K., & Warman, C. (2025). Nonbinary and Transgender Identities and Earnings: Evidence from a National Census. American Economic Review: Insights . Carpenter, C. S., Goodman, L., & Lee, M. J. (2024). Transgender earnings gaps in the United States: Evidence from administrative data . National Bureau of Economic Research. Carpenter, C. S., Kirkpatrick, L., Lee, M. J., & Plum, A. (2025). Economic outcomes of gender diverse people: New evidence from linked administrative data in New Zealand. Economics Letters , 247 , 112155. Carpenter, C. S., Lee, M. J., & Nettuno, L. (2022). Economic outcomes for transgender people and other gender minorities in the United States: First estimates from a nationally representative sample. Southern Economic Journal , 89 (2), 280–304. Ciprikis, K., Cassells, D., & Berrill, J. (2020). Transgender labour market outcomes: Evidence from the United States. Gender, Work & Organization , 27 (6), 1378–1401. Coffman, K., Coffman, L., & Ericson, K. M. (2024). Non-binary gender economics . Drydakis, N. (2022). Sexual orientation and earnings: A meta-analysis 2012–2020. Journal of Population Economics , 35 (2), 409–440. Gardeazabal, J., & Ugidos, A. (2004). More on identification in detailed wage decompositions. Review of Economics and Statistics , 86 (4), 1034–1036. Geijtenbeek, L., & Plug, E. (2018). Is there a penalty for registered women? Is there a premium for registered men? Evidence from a sample of transsexual workers. European Economic Review , 109 , 334–347. Nettuno, L. (2024). Gender identity, labor market outcomes, and socioeconomic status: Evidence from Chile. Labour Economics , 87 , 102487. Oaxaca, R. L., & Ransom, M. R. (1999). Identification in detailed wage decompositions. Review of Economics and Statistics , 81 (1), 154–157. Schilt, K., & Wiswall, M. (2008). Before and after: Gender transitions, human capital, and workplace experiences. The BE Journal of Economic Analysis & Policy , 8 (1). Shannon, M. (2022). The labour market outcomes of transgender individuals. Labour Economics , 77 , 102006. Statistics Canada. (n.d.). Guide to the Census of Population, 2021, Chapter 12 – Sampling and weighting for the long form . Retrieved October 15, 2025, from https://www12.statcan.gc.ca/census-recensement/2021/ref/98-304/2021001/chap12-eng.cfm Statistics Canada. (2018, August 17). North American Industry Classification System (NAICS) Canada 2017 Version 3.0 . https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=1181553 Statistics Canada. (2021a, September 15). National Occupational Classification (NOC) 2021 Version 1.0 . https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD&TVD=1322554 Statistics Canada. (2021b, November 17). 2021 Census of Population collection response rates . https://www12.statcan.gc.ca/census-recensement/2021/ref/response-rates-eng.cfm Statistics Canada. (2022a, April 6). Filling the gaps: Information on gender in the 2021 Census . https://www12.statcan.gc.ca/census-recensement/2021/ref/98-20-0001/982000012021001-eng.cfm Statistics Canada. (2022b, April 27). The Daily—Canada is the first country to provide census data on transgender and non-binary people . https://www150.statcan.gc.ca/n1/daily-quotidien/220427/dq220427b-eng.htm Statistics Canada. (2024, October 23). Coverage Technical Report, Census of Population, 2021 . https://www12.statcan.gc.ca/census-recensement/2021/ref/98-303/index-eng.cfm Statistics Canada of Canada. (2022, April 27). The Daily—Canada is the first country to provide census data on transgender and non-binary people . https://www150.statcan.gc.ca/n1/daily-quotidien/220427/dq220427b-eng.htm Waite, S., Ecker, J., & Ross, L. E. (2019). A systematic review and thematic synthesis of Canada’s LGBTQ2S+ employment, labour market and earnings literature. PloS One , 14 (10), e0223372. Yun, M. (2005). A simple solution to the identification problem in detailed wage decompositions. Economic Inquiry , 43 (4), 766–772. Yun, M.-S. (2008). Identification problem and detailed Oaxaca decomposition: A general solution and inference. Journal of Economic and Social Measurement , 33 (1), 27–38. Footnotes See Badgett et al., ( 2024 ) for a recent review. There is also a growing literature on labor market outcomes of LGBT + population; see Drydakis, ( 2022 ) for a meta-analysis. A smaller literature has examined the effects of gender transition on individual earnings trajectories (Geijtenbeek & Plug, 2018 ; Schilt & Wiswall, 2008 ). We use a variable on school attendance that indicates whether a person attended, either full-time or part-time, any accredited educational institution or program at any point during the nine-month period from September 2020 to May 11, 2021. Individuals who answered “yes” are classified as students and are excluded from our sample. The number of observations is rounded to the nearest ten, as required by Statistics Canada. The number of weeks worked includes paid vacation, sick leave, and training, and is set to 52 weeks for individuals paid year-round. Table 2 reports column percentages (i.e., the values within each column sum to 100 percent). Row percentages are provided separately in Table A1 in the Appendix. According to the data codebook, employment income refers to all income received as wages, salaries, and commissions from paid employment, as well as net self-employment income from farm or non-farm unincorporated businesses and/or professional practice during 2020. Additional Declarations No competing interests reported. Supplementary Files AppendixREH.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 06 Apr, 2026 Reviewers agreed at journal 17 Dec, 2025 Reviews received at journal 09 Dec, 2025 Reviewers agreed at journal 12 Nov, 2025 Reviewers invited by journal 12 Nov, 2025 Editor assigned by journal 12 Nov, 2025 Submission checks completed at journal 11 Nov, 2025 First submitted to journal 10 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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In recent years, individuals who identify outside traditional cisgender categories, specifically transgender and non-binary individuals, have gained greater public visibility. However, due to limited empirical evidence, our understanding of wage differences across gender identities remains incomplete.\u003c/p\u003e\n\u003cp\u003eIn 2021, the Canadian Census included, for the first time, a question on respondents\u0026rsquo; current gender, which could differ from their sex assigned at birth. By combining responses to the gender and sex-at-birth questions, individuals can be classified as cisgender (same sex at birth and gender), transgender (assigned male at birth but identify as women, or assigned female at birth but identify as men), and nonbinary (whose current gender is neither man nor woman). To our knowledge, this is the first large-scale national census to collect information that enables the identification of transgender and nonbinary individuals (Statistics Canada of Canada, 2022).\u003c/p\u003e\n\u003cp\u003eIn this paper, we leverage this novel gender identity information in the 2021 Canadian Census to examine labor market outcomes across gender identities. We analyze differences in employment probabilities, annual wages, weeks and hours worked, and hourly wages. We also examine the distribution of individuals across occupations and conduct subsample analyses to explore wage gaps within specific occupational sectors. \u003c/p\u003e\n\u003cp\u003eWe find that transgender and nonbinary individuals are about 8 to 14 percentage points less likely to be in paid employment than cisgender men. Conditional on employment, they also work fewer weeks and hours. Their raw hourly wages are, on average, 20 to 30 percent lower than those of cisgender men\u0026mdash;a gap that is substantially larger than the roughly 10 percent difference observed between cisgender women and men\u0026mdash;with nonbinary individuals assigned female at birth (AFAB) facing the largest penalty. After controlling for demographics and job characteristics such as occupation, industry, and training requirements, the wage gaps shrink to about 8 to 17 percent, though they remain largest for nonbinary AFAB individuals. Subsample analyses by occupation reveal several notable patterns. In legislative and senior management, transgender and nonbinary individuals are underrepresented and face large wage gaps within the sector. By contrast, health-related fields are more equitable, with greater representation of gender minorities and smaller wage disparities. Sales and service occupations also attract more transgender and nonbinary individuals, though significant wage gaps persist. A distinct pattern emerges for transgender men, who are more represented than other gender minority groups in traditionally male-dominated fields such as trades, natural resources, and manufacturing, where their wages are not significantly different from those of cisgender men, highlighting the salience of maleness in these occupations. For nonbinary individuals, representation is particularly high in arts and sports, where they experience relatively favorable wage outcomes. We also conducted Oaxaca\u0026ndash;Blinder decompositions and found that about half of the hourly wage differences between noncisgender and cisgender individuals can be explained by observable characteristics, while the other half remains unexplained. The relatively larger unexplained component, compared to the traditional male\u0026ndash;female wage gap, suggests that discrimination may play a more prominent role.\u003c/p\u003e\n\u003cp\u003eA growing body of empirical research has documented significant labor market disparities faced by gender-diverse populations.\u003csup\u003e1\u003c/sup\u003e Several U.S.-based studies using survey data have highlighted persistent employment and earnings penalties for individuals who identify as transgender (Carpenter et al., 2020, 2022; Ciprikis et al., 2020; Shannon, 2022).\u003csup\u003e2\u003c/sup\u003e For example, using the Behavioral Risk Factor Surveillance System (BRFSS), which only can identify tansgender people but not non-binary individuals, Carpenter et al., (2020) find that transgender adults report significantly lower levels of employment and income, as well as poorer health, compared to cisgender men. However, the BRFSS includes only self-reported categorical income data and lacks information on occupation or industry. More recent work using administrative records has produced more precise estimates of wage gaps. In a landmark study using Social Security and IRS data, Carpenter et al., (2024) document a wage penalty of 6\u0026ndash;13 log points for individuals who identify as transgender, based on within-person and sibling fixed-effects models. These gaps are most pronounced among those who identify as transgender women.\u003c/p\u003e\n\u003cp\u003eIn addition to individuals who identify as transgender, nonbinary individuals also face disadvantages in the labor market and have become more visible in recent literature due to improved data availability. For example, Shannon, (2022) documents that individuals assigned female at birth (AFAB) who identify as transgender or nonbinary tend to earn less than their assigned male at birth (AMAB) peers, suggesting a persistent \u0026ldquo;maleness premium.\u0026rdquo; Coffman et al., (2024) find that individuals who identify as nonbinary report distinct preferences and experience higher levels of discrimination compared to both men and women.\u003c/p\u003e\n\u003cp\u003eNon-U.S. contexts also offer valuable comparative insights (Carpenter, Kirkpatrick, et al., 2025) on New Zealand, Nettuno, (2024) on Chile, and Waite et al., (2019) on Canada). Carpenter, Feir, et al., (2024) conduct the first nationally representative study of labor market disparities among transgender and nonbinary individuals in Canada using 2021 Census data. Specifically, they find that nonbinary individuals assigned female at birth earn approximately 40% less annually than otherwise comparable cisgender men, with the gap narrowing slightly (to ~27%) after controlling for occupation and industry. Transgender women and nonbinary individuals assigned male at birth also face significant, albeit smaller, earnings penalties, ranging from 10% to 20%. \u003c/p\u003e\n\u003cp\u003eBuilding on their findings, our study expands the scope in several key dimensions. First, we examine the distribution of transgender and nonbinary individuals across occupations, industries, and categories defined by training requirements. We identify patterns of underrepresentation in some sectors and concentration in others, which, though descriptive, suggest potential barriers to entry in certain occupations and relatively favorable conditions in others. Second, we conduct subgroup wage analyses across occupational sectors, revealing variation in wage outcomes for gender minorities depending on the occupation. Third, we implement Oaxaca\u0026ndash;Blinder decompositions to quantify the extent to which wage gaps are explained by compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination. Taken together, although both studies use the same foundational data, our analysis provides a more comprehensive decomposition of labor market inequality by gender identity in the Canadian context.\u003c/p\u003e"},{"header":"Data and Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eData and descriptive statistics\u003c/h2\u003e\n \u003cp\u003eThis study uses data from the 2021 Canadian long-form Census, which was distributed to approximately one-quarter of all households nationwide. The long-form questionnaire is administered through a stratified random sampling process designed to ensure representativeness across provinces, territories, and population groups (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Selected households are required by law to respond, yielding exceptionally high coverage (97%) and response rates (98%) compared to typical survey data (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The 2021 Canadian Census was among the first large-scale national censuses globally to collect information that enables researchers to identify transgender and nonbinary individuals (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Historically, Canadian censuses collected information on sex but not gender. In 2021, respondents aged 15 and older were asked, \u0026ldquo;What is this person\u0026rsquo;s gender?\u0026rdquo; accompanied by an explanatory note defining gender as \u0026ldquo;current gender, which may differ from sex assigned at birth and from legal documents.\u0026rdquo; (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2022a\u003c/span\u003e) By combining responses on sex assigned at birth with those on current gender identity, individuals can be classified into the following categories: cisgender men, cisgender women, transgender men, transgender women, nonbinary individuals assigned male at birth (NB-AMAB), and nonbinary individuals assigned female at birth (NB-AFAB).\u003c/p\u003e\n \u003cp\u003eOur sample is restricted to individuals aged 18 to 64, excluding students.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e3\u003c/sup\u003e When analyzing labor market outcomes such as occupation and industry, and estimating log wage regressions, we restrict the sample to individuals who are employed, excluding the self-employed. This ensures that all observations have valid occupation and industry codes and positive wage values. Accordingly, our analysis focuses on wage and salary workers to capture labor market outcomes within the formal employment sector for gender minorities. Census weights are used in all of our analyses.\u003c/p\u003e\n \u003cp\u003eTable 1 presents descriptive statistics for our sample without restrictions on labor market status. The sample sizes are weighted counts, representing the number of individuals in each category within the Canadian population.\u003ca href=\"#_ftn2\" name=\"_ftnref2\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e4\u003c/sup\u003e Approximately 0.33% of the full sample of Canadians aged 18 to 64 who are not students identify as transgender or nonbinary. The largest group is transgender women (0.11%), followed by nonbinary individuals assigned female at birth (0.09%), transgender men (0.09%), and nonbinary individuals assigned male at birth (0.04%). Despite their relatively small share of the population, the absolute numbers of transgender and nonbinary individuals are large enough to allow for meaningful subgroup analysis. Several patterns stand out from Table 1. Compared to cisgender individuals, transgender and nonbinary individuals are, on average, younger, less likely to be immigrants, and less likely to have children, with the differences being more pronounced for nonbinary individuals. Nonbinary individuals are also more likely to be White or Indigenous. In terms of marital status, transgender and nonbinary individuals are less likely to be in opposite-sex marriages or common-law relationships, and more likely to be single or in same-sex marriages or common-law relationships, compared to cisgender individuals. Transgender men and transgender women are less likely to have a Bachelor\u0026apos;s degree or higher\u0026mdash;20.8% for transgender men and 25.0% for transgender women\u0026mdash;compared to 27.0% of cisgender men and 33.9% of cisgender women. In contrast, the proportion with a post-secondary degree is higher among nonbinary individuals: 32.1% for those assigned male at birth (AMAB), and 37.8% for those assigned female at birth (AFAB). Both transgender and nonbinary individuals are less likely to live in rural areas and more likely to reside in large urban population centres. In terms of province of residence, notable differences emerge: transgender and nonbinary individuals are less likely to live in Quebec and more likely to live in British Columbia.\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescriptive statistics: individuals aged 18\u0026ndash;64, not in school\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCisgender\u003c/p\u003e\n \u003cp\u003emen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCisgender\u003c/p\u003e\n \u003cp\u003ewomen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTransgender\u003c/p\u003e\n \u003cp\u003emen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTransgender\u003c/p\u003e\n \u003cp\u003ewomen\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-binary\u003c/p\u003e\n \u003cp\u003eAMAB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-binary\u003c/p\u003e\n \u003cp\u003eAFAB\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,799,090\u003c/p\u003e\n \u003cp\u003e(50.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9,681,040\u003c/p\u003e\n \u003cp\u003e(49.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16,910\u003c/p\u003e\n \u003cp\u003e(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21,870\u003c/p\u003e\n \u003cp\u003e(0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,700\u003c/p\u003e\n \u003cp\u003e(0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18,390\u003c/p\u003e\n \u003cp\u003e(0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.4 (12.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44.3 (12.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.1 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.1 (13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.8 (10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.7 (9.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmigrants\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.17%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHas kid\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Asian \u0026amp; Southeast Asian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.99%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEast\u003c/p\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWest Asian \u0026amp; Arab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLatin American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarital status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot married or common-law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.19%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOpposite sex married or common law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63.98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSame sex married or common law\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo certificate, diploma or degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.77%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.86%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary (high) school diploma\u003c/p\u003e\n \u003cp\u003eor equivalency certificate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.50%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePost-secondary no degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.31%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.07%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGraduate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAreas of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.26%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.21%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.85%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall population centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.24%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium population centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.29%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.16%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge urban population centre\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e61.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.22%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvinces of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNewfoundland and Labrador\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.51%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrince Edward Island\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNova Scotia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Brunswick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQuebec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.53%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOntario\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38.69%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.74%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManitoba\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.60%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.54%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.08%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaskatchewan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.01%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlberta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.18%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBritish Columbia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.14%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTerritories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.34%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.65%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNotes: Weighted descriptive statistics are calculated for individuals aged 18\u0026ndash;64 who were not attending school at the time of the 2021 Canadian long-form Census. Gender identity is derived by combining information on current gender and sex assigned at birth, classifying individuals as cisgender men and women, transgender men and women, and nonbinary individuals assigned male (AMAB) or female (AFAB) at birth. Population centre classifications follow Statistics Canada definitions: small (1,000\u0026ndash;29,999 residents), medium (30,000\u0026ndash;99,999), and large urban centres (100,000 or more). All areas outside population centres are classified as rural areas. The number of observations is rounded to the nearest ten as required by Statistics Canada.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eEmpirical Analysis\u003c/h3\u003e\n\u003cp\u003eOne of the primary objectives of this study is to examine differences in labor market outcomes across gender identities. Specifically, we estimate the following regression model:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{Y}_{i}=\\:{\\beta\\:}_{0}+{\\beta\\:}_{1}cisgender\\:wome{n}_{i}+{\\beta\\:}_{2}transgender\\:me{n}_{i}+{\\beta\\:}_{3}transgender{\\:women}_{i}+{\\beta\\:}_{4}NB﹘AMA{B}_{i}+\\:{\\beta\\:}_{5}NB﹘AFA{B}_{i}+\\gamma\\:{X}_{i}+{\\mu\\:}_{i}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWe use various labor market outcomes as dependent variables, including employment status and log wages. Employment status is a binary variable equal to one if the individual had paid employment in 2020 and zero otherwise. When employment status is the dependent variable, we estimate a logistic regression model using the full sample. When log wages are the dependent variable, we estimate a linear regression model, restricting the sample to employed individuals with positive wages. For wage measures, we use hourly wages as our main outcome. Hourly wages are calculated by dividing 2020 gross wages and salaries by the product of usual weekly hours and weeks worked. The 2021 Census reports gross wages and salaries for 2020, prior to deductions such as income tax, pension plan contributions, and employment insurance premiums. It also provides the number of weeks worked in 2020 and usual weekly working hours.\u003ca href=\"#_ftn1\" name=\"_ftnref1\" title=\"\"\u003e\u003c/a\u003e\u003csup\u003e5\u003c/sup\u003e However, the \u0026nbsp;measure of usual weekly working hours refers to hours across all jobs during the reference week (May 2 to May 8) in 2021, which may differ from individuals\u0026rsquo; usual weekly hours in 2020. As this is the only measure of hours available in the data, our construction of hourly wages relies on the assumption that weekly hours did not change substantially between 2020 and 2021. As robustness checks, we also report results using annual wages in 2020 and weekly earnings in 2020 in the Appendix. Given concerns that the 2020 labor market may have been affected by COVID-19, we further report results using annual wages in 2019, which are also available in the 2021 Census.\u003c/p\u003e\n\u003cp\u003eRegarding the control variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{i}\\)\u003c/span\u003e\u003c/span\u003e, we estimate the regression in a progressive manner. First, we run the regression without any control variables. Second, we include basic demographic controls: potential experience (defined as age minus years of education minus six) and its square, racial background (White vs. non-White), parental status (with or without children), marital status (opposite-sex married/common-law, same-sex married/common-law, and not married/common-law), immigration status (immigrant vs. Canadian-born), area of residence (rural areas, small population centers, medium population centers, and large urban centers), and province fixed effects. Lastly, we further control industry, occupation, and the skill requirements of the job.\u003c/p\u003e\n\u003cp\u003eIndustry sectors are classified according to the North American Industry Classification System (NAICS) Canada 2017 Version 3.0 (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The NAICS classification system groups businesses based on similar economic activities, typically defined by common production processes. It divides the economy into 20 industry sectors, such as agriculture, health care, finance, and manufacturing. Occupation is described by the National Occupational Classification (NOC) 2021 Version 1.0, Canada\u0026rsquo;s official occupation classification system (Statistics Canada, \u003cspan class=\"CitationRef\"\u003e2021a\u003c/span\u003e). In contrast to NAICS, which describes the type of business or industry, the NOC focuses on the type of work performed by individuals, regardless of the industry. It organizes occupations into ten broad categories (e.g., trades, transport, and equipment operators, and sales and service occupations), based on the first digit of the NOC code. Skill requirement is measured by the TEER system (Training, Education, Experience, and Responsibilities), which reflects the typical qualifications needed for each role. There are six TEER categories, ranging from 0 to 5. TEER 0 includes management roles; TEER 1 covers professional jobs usually requiring a university degree; TEER 2 includes positions needing a college diploma or long-term apprenticeship; TEER 3 applies to jobs requiring shorter college or apprenticeship training; TEER 4 includes roles needing a high school diploma or brief training; and TEER 5 covers jobs with minimal or no formal education requirements.\u003c/p\u003e\n\u003cp\u003eIn addition to the regressions using the full sample, we also conducted subsample analyses, which quantify gender earning disparities within each occupational group (10) and occupational TEER category (6). This detailed segmentation helps in understanding gender differences across different economic sectors and professional categories.\u003c/p\u003e\n\u003cp\u003eFinally, we employ Oaxaca decomposition to analyze earnings discrepancies between cisgender versus non-cisgender individuals. This analytic approach highlights the extent to which wage disparities can be explained by measurable factors such as occupation and industry, as opposed to those that may arise from potential discrimination or other unobserved characteristics associated with gender identity.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eDistribution of occupations, industries, and TEER\u003c/h2\u003e\u003cp\u003eBefore turning to the regression results, we first present descriptive evidence on lab\u003cp\u003e\u003cstrong\u003eDistribution of occupations, industries, and TEER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore turning to the regression results, we first present descriptive evidence on labor market outcomes, as these provide additional insights into the disparities faced by gender minorities. Table 2 presents labor market outcomes for our working sample, including annual wages, weeks and hours worked, and the distribution across occupation groups, industry sectors, and TEER categories, separately for each gender identity. Cisgender men exhibit the most favorable labor market outcomes. On average, they work 41 hours per week and 44 weeks annually, earning the highest average annual wage of about $69,000. In contrast, cisgender women work fewer hours per week (36) and weeks annually (42), with an average annual wage of about $49,000, much lower than that of cisgender men, reflecting the traditional gender wage gap. Transgender individuals work slightly fewer weeks than cisgender women and earn less, with an average annual wage of approximately $44,000 to $45,000. Among nonbinary individuals, those AMAB have outcomes closer to cisgender women, working similar hours and earning about $50,000 annually. In contrast, nonbinary AFAB people earn the lowest average wage, approximately $36,000. These patterns reveal a clear hierarchy of labor market outcomes, with cisgender men at the top, followed by NB-AMAB individuals, cisgender women, transgender individuals, and NB-AFAB individuals. \u003c/p\u003e\n\u003cp\u003eNext, we examine the distribution of occupations by gender identity, which reveals notable differences.\u003csup\u003e6\u003c/sup\u003e Compared to cisgender men, cisgender women are more likely to work in business, health, education-related, sales, and service occupations, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. The gap is especially pronounced in the trades: 30 percent of cisgender men are employed in this sector, compared to only 3 percent of cisgender women. Transgender individuals are more concentrated in sales and service occupations compared to cisgender individuals. Relative to cisgender men, they are also more likely to work in health, education, and arts, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. However, their representation in these latter occupations is higher than that of cisgender women. For nonbinary individuals, we observe patterns broadly similar to those of transgender individuals, with some notable differences. Nonbinary individuals are even more likely to work in education and arts related fields, particularly NB-AFAB, among whom more than 20 percent are employed in education, law and social occupations. Additionally, more than 20 percent of NB-AMAB are employed in natural and applied sciences, making them the largest group in relative terms. Nonbinary individuals\u0026rsquo; representation in trades, natural resources, and manufacturing is lower than that of transgender individuals but generally higher than that of cisgender women. Table 2 also presents the distribution by industry sector. Several patterns are evident: transgender and nonbinary individuals are less likely than cisgender men to be employed in utilities, construction, and manufacturing, but more likely to work in retail trade, health care, and service sectors such as educational services. While industry refers to the type of business, occupation captures the specific tasks performed; we therefore view occupation categories as a more accurate reflection of the type of work undertaken. Accordingly, our main analysis focuses on occupation groups. Lastly, we examine the distribution of occupations by TEER category. Cisgender men are more likely to hold management positions (TEER 0). In contrast, professional occupations (TEER 1) are more common among cisgender women, transgender women, and nonbinary individuals. Occupations with lower qualification requirements (TEER 4 and 5) are also more prevalent among transgender and nonbinary individuals compared to cisgender individuals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis: Employment Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe first estimate the regression model (equation (1)) using employment status as the dependent variable. Since employment status is a binary variable equal to one for individuals working for pay in 2020 and zero for all others, we estimate a logistic model using our full sample (the same as in Table 1). This definition allows us to examine differences in formal labor market employment by gender identity. Table 3 reports the average marginal effects (AME) on the probability of employment for various gender identities, with cisgender men as the reference group, from the logit model after controlling for covariates including potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The AME for cisgender women is -0.063, which indicates that, on average, cisgender women are 6.3 percentage points less likely to be employed compared to cisgender men, holding other factors constant. Transgender men and women are, respectively, 9.5 and 14.1 percentage points less likely to be employed than cisgender men. NB-AMAB and NB-AFAB people are, respectively, 7.5 and 11.2 percentage points less likely to be employed than cisgender men. All estimates are statistically significant at the 1% level, indicating that individuals in these gender identity groups are significantly less likely to be employed compared to cisgender men. The disparity is largest for transgender women and NB-AFAB people, highlighting potential disparities in employment access for these groups. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis: Wages\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 4 reports the regression results using log hourly wage as the dependent variable to examine wage gaps across gender identities relative to cisgender men. Column 1 includes no control variables. Column 2 adds controls for potential experience and its square, immigration status, race, marital status, parental status, education, area of residence, and province fixed effects. Column 3 further incorporates occupation, TEER, and industry fixed effects. The bottom panel of Table 4 reports the p-values from tests of coefficient equality across all possible pairs of gender identity groups. \u003c/p\u003e\n\u003cp\u003eColumn 1, without any controls, reflects the raw average differences in hourly wages. The estimated coefficient for cisgender women is -0.110, implying they earn about 10.4 percent less (exp(-0.110) \u0026ndash; 1 = -0.104) than cisgender men on average. The gaps are larger for all gender-diverse groups: approximately 18.0 percent for transgender women, 19.4 percent for NB-AMAB individuals, 22.3 percent for transgender men, and 29.6 percent for NB-AFAB individuals. In Column 2, after adding demographic characteristics, the traditional men\u0026ndash;women wage gap widens to about 14.4 percent, primarily driven by women\u0026rsquo;s well-documented educational advantage in recent years (Blau \u0026amp; Kahn, 2017). For gender-diverse groups, the wage gaps narrow, most notably for transgender men, whose gap decreases by nearly half to 11.6 percent. NB-AMAB individuals, transgender women, and NB-AFAB individuals continue to face gaps of approximately 15.4, 15.7, and 24.0 percent, respectively, relative to cisgender men. In Column 3, after adding controls for occupation, TEER, and industry fixed effects, the earnings gaps narrow for all groups compared to Column 2: cisgender women\u0026rsquo;s gap falls to 12.2 percent, transgender men\u0026rsquo;s gap declines further to 7.7 percent, transgender women\u0026rsquo;s gap decreases to 13.1 percent, NB-AMAB individuals\u0026rsquo; gap narrows to 13.0 percent, and NB-AFAB individuals\u0026rsquo; gap decreases to 17.4 percent. The decline in wage gaps from Column 2 to Column 3 suggests that sorting across occupations and industries accounts for an important share of the observed disparities, particularly for transgender men and NB-AFAB individuals.\u003c/p\u003e\n\u003cp\u003eFor the robustness check, we restrict the sample to full-time workers\u0026mdash;defined as those employed at least 30 hours per week for at least 27 weeks in 2020\u0026mdash;and re-estimate the regressions. The results, reported in Table A2 of the Appendix, show a broadly similar pattern and indicate that wage disparities persist among full-time workers. Moreover, in the full specification with all controls, the earnings gaps for full-time transgender men and women relative to full-time cisgender men widen to approximately 14.6 percent and 20.3 percent, respectively, which are substantially larger than the corresponding estimates in Table 4. This suggests that full-time transgender workers experience even larger gaps compared to their cisgender male peers than do part-time workers. One concern is that wages in 2020 may be affected by COVID-19, so we also use 2019 annual wages, which are available in the data, as the dependent variable. The results are reported in Table A3 in the Appendix. For comparison, we also include results using 2020 annual wages. The patterns are largely similar, although transgender women and nonbinary individuals show slightly larger gaps in 2020, suggesting that these minority groups may have been more adversely affected by COVID-19. Carpenter, Feir, et al., (2025) use 2019 annual earnings as their main outcome but also show and acknowledge that their results remain unaffected when using 2020 data. Since weeks worked, hours, and occupation measures are all from 2020 or 2021, we use 2020 wages as our preferred measure.\u003c/p\u003e\n\u003cp\u003eWe also use alternative outcome variables\u0026mdash;including log total annual earnings in 2020, log weekly wages, log weekly earnings, weeks worked, and hours worked\u0026mdash;and re-estimate the regressions with the full set of covariates. The results, also reported in Table A3 of the Appendix, confirm the disadvantages faced by gender minorities in the labor market: transgender and nonbinary individuals work significantly fewer hours and weeks than cisgender men. The combination of reduced work time and lower hourly wages translates into even larger gaps in annual wages. The annual earnings variable we use in the Appendix is employment income in 2020, which includes self-employment income.\u003csup\u003e7\u003c/sup\u003e By using log annual earnings as the dependent variable, we incorporate the self-employed into the regression sample. The results using annual wages versus annual earnings are very similar, suggesting that the impact of excluding the self-employed and self-employment income is likely limited.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis: Heterogeneity by Occupation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo better understand wage gaps across gender identities, we estimate log hourly wage regressions separately for each occupation while controlling for demographic variables (the same controls as in Column 2 of Table 4). The coefficients on the gender identity dummies are reported in Table 5. The estimates highlight how wage gaps vary across occupations. Cisgender women face relatively smaller penalties in health (4.7 percent) and arts and recreation (8.4 percent), but substantially larger gaps in natural resources (29.8 percent). Transgender men exhibit small and statistically insignificant differences from cisgender men in health, trades, manufacturing, and natural resources, suggesting comparable wage outcomes in these fields; however, they experience significant penalties in legislative and management (64.4 percent) and arts and recreation (33.8 percent). Transgender women show relatively favorable outcomes in manufacturing, health, and natural sciences, but face disadvantages in education (22.7 percent) and arts and recreation (27.6 percent), with an especially large but imprecisely estimated penalty in legislative and management (54.8 percent). For nonbinary individuals, disparities are likewise occupation-specific: AMAB individuals exhibit relatively better outcomes in health and natural sciences but worse outcomes in legislative and management (65.5 percent) and sales (23.1 percent), whereas AFAB individuals perform better in arts but face significant penalties in legislative and management (40.6 percent) and natural resources (34.6 percent). Taken together, several patterns emerge. First, health occupations stand out as the sector where wage gaps across gender minority groups are consistently small (with the exception of NB-AFAB), suggesting a more equitable pay structure. In contrast, legislative and management positions exhibit the largest and most consistent disparities, particularly for transgender and nonbinary groups, though small sample sizes in some subgroups warrant cautious interpretation. Finally, in trades, manufacturing, and natural resources, transgender men experience relatively favorable wage outcomes, whereas other gender minority groups tend to face disadvantages. This pattern may reflect that these fields reward characteristics or norms traditionally associated with maleness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRegression Analysis: Heterogeneity by TEER\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next estimate separate regressions of log hourly wages on gender identity within each TEER category, using cisgender men as the reference group. Table 6 reports the results and reveals heterogeneity across occupational skill levels. Cisgender women earn between 9 and 17 percent less than cisgender men across TEER levels, with the largest penalties observed in TEER 2 (16.3 percent) and TEER 5 (14.3 percent). Transgender men earn roughly 29 percent less than cisgender men in TEER 0, though the gap narrows substantially at lower TEER levels, with insignificant differences in TEER 3 through TEER 5. Transgender women face persistent penalties across all TEER levels except TEER 5, ranging from approximately 22 percent in TEER 0 to 12 percent in TEER 2. Similarly, NB-AMAB individuals experience large penalties in TEER 0 (36 percent) and consistent disadvantages across other categories, except TEER 3. NB-AFAB individuals face significant penalties across all occupations, including a 31 percent gap in TEER 0 and a 29 percent gap in TEER 2. Overall, the TEER-specific regressions reveal distinct gradients in wage inequality. Cisgender women face modest but relatively consistent penalties across all skill levels. In contrast, wage gaps for gender minorities are larger and more variable, with the steepest penalties observed in higher-skill occupations (particularly TEER 0). At the lower end of the skill distribution, such as TEER 5, penalties are much smaller for transgender individuals, though nonbinary individuals continue to experience sizable gaps.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOaxaca\u0026ndash;Blinder Decompositions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further examine the extent to which wage gaps are attributable to compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination, we implement Oaxaca\u0026ndash;Blinder decompositions. Table 7 reports the decomposition of the log hourly wage gap between cisgender individuals (cisgender men and women combined) and non-cisgender individuals (nonbinary and transgender people combined) among full-time workers. We restrict the sample to full-time workers to eliminate the influence of differences in hours worked. The decomposition separates the mean gender wage gap into two main components: the endowment effects (explained portion), which reflect differences in the average values of predictors across genders, and the coefficient effects (unexplained portion), which arise from variations in the relationship between these predictors and earnings across different gender identities. Because the primary explanatory variables\u0026mdash;occupation and industry\u0026mdash;are categorical, the choice of a reference or omitted group complicates interpretation. For instance, it becomes difficult to distinguish the part of the unexplained wage gap that is truly attributable to a group from the part driven by differences in the coefficients of the omitted category. To address this issue, we adopt a modified approach that normalizes coefficients by transforming the categorical variables prior to estimation, following the methods proposed by Oaxaca \u0026amp; Ransom, (1999), Gardeazabal \u0026amp; Ugidos, (2004), and M. Yun, (2005; 2008). \u003c/p\u003e\n\u003cp\u003eThe last three rows of Table 7 indicate that approximately 48 percent of the total wage gap is explained by observable characteristics, while the remaining 52 percent is unexplained. Experience emerges as the most substantial factor, accounting for approximately 40 percent of the wage gap. Industry also plays an important role, explaining about 10.2 percent of the observed disparity. Other notable contributors include marital status (6.0 percent), occupation (5.9 percent), occupational skill level as indicated by TEER (2.5 percent), and parental status (2.1 percent). By contrast, some factors contribute negatively to the wage gap, suggesting areas where noncisgender individuals hold relative advantages compared to cisgender counterparts. Immigration status accounts for a negative 3.9 percent, while province and rurality each contribute around -3.5 percent, reflecting geographic differences in opportunities. Education also contributes negatively by about 1.8 percent, consistent with earlier evidence of higher educational attainment among nonbinary individuals. Overall, these decomposition results suggest that while experience and occupational sorting are major drivers of wage disparities, certain demographic characteristics partly offset these gaps.\u003c/p\u003eor market outcomes, as these provide additional insights into the disparities faced by gender minorities. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents labor market outcomes for our working sample, including annual wages, weeks and hours worked, and the distribution across occupation groups, industry sectors, and TEER categories, separately for each gender identity. Cisgender men exhibit the most favorable labor market outcomes. On average, they work 41 hours per week and 44 weeks annually, earning the highest average annual wage of about \u003cspan\u003e$\u003c/span\u003e69,000. In contrast, cisgender women work fewer hours per week (36) and weeks annually (42), with an average annual wage of about \u003cspan\u003e$\u003c/span\u003e49,000, much lower than that of cisgender men, reflecting the traditional gender wage gap. Transgender individuals work slightly fewer weeks than cisgender women and earn less, with an average annual wage of approximately \u003cspan\u003e$\u003c/span\u003e44,000 to \u003cspan\u003e$\u003c/span\u003e45,000. Among nonbinary individuals, those AMAB have outcomes closer to cisgender women, working similar hours and earning about \u003cspan\u003e$\u003c/span\u003e50,000 annually. In contrast, nonbinary AFAB people earn the lowest average wage, approximately \u003cspan\u003e$\u003c/span\u003e36,000. These patterns reveal a clear hierarchy of labor market outcomes, with cisgender men at the top, followed by NB-AMAB individuals, cisgender women, transgender individuals, and NB-AFAB individuals.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics: individuals aged 18\u0026ndash;64, not in school, and employed in 2020\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCisgender\u003c/p\u003e\u003cp\u003emen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCisgender\u003c/p\u003e\u003cp\u003ewomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTransgender\u003c/p\u003e\u003cp\u003emen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTransgender\u003c/p\u003e\u003cp\u003ewomen\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNon-binary\u003c/p\u003e\u003cp\u003eAMAB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-binary\u003c/p\u003e\u003cp\u003eAFAB\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6,287,510\u003c/p\u003e\u003cp\u003e(53.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,461,620\u003c/p\u003e\u003cp\u003e(46.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,790\u003c/p\u003e\u003cp\u003e(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10,120\u003c/p\u003e\u003cp\u003e(0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,740\u003c/p\u003e\u003cp\u003e(0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9,730\u003c/p\u003e\u003cp\u003e(0.08)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Wages ($)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e69070.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49422.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44961.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e43817.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49975.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35616.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking Weeks\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e39.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e38.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eWorking Hours\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e40.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e37.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupational Group\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLegislative and senior management\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.04%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.04%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.51%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBusiness, finance and administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.71%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e28.74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.06%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e19.17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.03%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.47%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural and applied sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.76%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.08%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.02%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.27%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation, law and social\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.32%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.03%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.97%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArt, culture, recreation and sport\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.73%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.02%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.05%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSales and service\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.07%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e29.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.00%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.03%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrades, transport and equipment\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e30.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.18%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.68%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.97%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.49%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNatural resources, agriculture\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.21%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.86%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing and utilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.95%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.05%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.75%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIndustry sectors\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAgriculture, forestry, fishing and hunting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.09%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.82%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.28%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.05%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.03%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMining, quarrying, and oil and gas\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.27%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.41%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUtilities\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.57%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstruction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.31%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.59%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.34%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManufacturing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.24%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.22%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.91%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWholesale trade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.90%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.19%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.47%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.54%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRetail trade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.25%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.68%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.45%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e13.46%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransportation and warehousing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.44%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInformation and cultural industries\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.77%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.06%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.07%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.85%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.89%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinance and insurance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.96%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eReal estate and rental and leasing\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.37%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProfessional, scientific and technical services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.15%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.79%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.67%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e10.48%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManagement of companies and enterprises\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.27%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.34%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.46%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdministrative and support,\u003c/p\u003e\u003cp\u003ewaste management and remediation services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.98%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.55%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.21%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.53%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.88%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.92%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.70%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.31%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHealth care and social assistance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.32%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11.26%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.17%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.70%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArts, entertainment and recreation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.12%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.58%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.38%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4.93%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAccommodation and food services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.33%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.54%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.09%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther services\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.50%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3.62%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.01%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.06%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublic administration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.69%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.36%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.03%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.23%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.02%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOccupational Teer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.23%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.30%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.56%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.09%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.44%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.81%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.63%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25.81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.36%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.11%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e29.75%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.65%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27.48%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17.74%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23.78%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.87%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.42%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.76%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.91%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.16%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.81%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16.40%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.18%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.29%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.80%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.29%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.29%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e17.49%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.61%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.16%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11.94%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.72%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.93%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.39%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.33%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: Weighted descriptive statistics are based on individuals aged 18\u0026ndash;64 who were not enrolled in school and were employed in 2020, as reported in the 2021 Canadian long-form Census. We exclude self-employed individuals and restrict the sample to those with valid occupation and industry codes. Annual wages refer to gross wages and salaries for the calendar year 2020 (before deductions). Weeks worked are reported for 2020, including paid leave or training. Working hours are measured for the 2021 reference week (May 2\u0026ndash;8) across all jobs. Gender identity is derived by combining current gender and sex assigned at birth, classifying individuals as cisgender men and women, transgender men and women, and nonbinary individuals assigned male (AMAB) or female (AFAB) at birth. The number of observations is rounded to the nearest ten as required by Statistics Canada.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNext, we examine the distribution of occupations by gender identity, which reveals notable differences.\u003ca class=\"FNLink\" href=\"#Fn6\" id=\"#FNLinkFn6\"\u003e\u003c/a\u003e Compared to cisgender men, cisgender women are more likely to work in business, health, education-related, sales, and service occupations, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. The gap is especially pronounced in the trades: 30 percent of cisgender men are employed in this sector, compared to only 3 percent of cisgender women. Transgender individuals are more concentrated in sales and service occupations compared to cisgender individuals. Relative to cisgender men, they are also more likely to work in health, education, and arts, and less likely to be employed in trades, natural sciences and natural resources, and manufacturing. However, their representation in these latter occupations is higher than that of cisgender women. For nonbinary individuals, we observe patterns broadly similar to those of transgender individuals, with some notable differences. Nonbinary individuals are even more likely to work in education and arts related fields, particularly NB-AFAB, among whom more than 20 percent are employed in education, law and social occupations. Additionally, more than 20 percent of NB-AMAB are employed in natural and applied sciences, making them the largest group in relative terms. Nonbinary individuals\u0026rsquo; representation in trades, natural resources, and manufacturing is lower than that of transgender individuals but generally higher than that of cisgender women. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e also presents the distribution by industry sector. Several patterns are evident: transgender and nonbinary individuals are less likely than cisgender men to be employed in utilities, construction, and manufacturing, but more likely to work in retail trade, health care, and service sectors such as educational services. While industry refers to the type of business, occupation captures the specific tasks performed; we therefore view occupation categories as a more accurate reflection of the type of work undertaken. Accordingly, our main analysis focuses on occupation groups. Lastly, we examine the distribution of occupations by TEER category. Cisgender men are more likely to hold management positions (TEER 0). In contrast, professional occupations (TEER 1) are more common among cisgender women, transgender women, and nonbinary individuals. Occupations with lower qualification requirements (TEER 4 and 5) are also more prevalent among transgender and nonbinary individuals compared to cisgender individuals.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRegression Analysis: Employment Status\u003c/h3\u003e\n\u003cp\u003eWe first estimate the regression model (Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)) using employment status as the dependent variable. Since employment status is a binary variable equal to one for individuals working for pay in 2020 and zero for all others, we estimate a logistic model using our full sample (the same as in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This definition allows us to examine differences in formal labor market employment by gender identity. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the average marginal effects (AME) on the probability of employment for various gender identities, with cisgender men as the reference group, from the logit model after controlling for covariates including potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The AME for cisgender women is -0.063, which indicates that, on average, cisgender women are 6.3 percentage points less likely to be employed compared to cisgender men, holding other factors constant. Transgender men and women are, respectively, 9.5 and 14.1 percentage points less likely to be employed than cisgender men. NB-AMAB and NB-AFAB people are, respectively, 7.5 and 11.2 percentage points less likely to be employed than cisgender men. All estimates are statistically significant at the 1% level, indicating that individuals in these gender identity groups are significantly less likely to be employed compared to cisgender men. The disparity is largest for transgender women and NB-AFAB people, highlighting potential disparities in employment access for these groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAverage marginal effect of gender identities (relative to cisgender men) on employment status, results from logit model\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003edy/dx\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003estd. err.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ez\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;z\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[95% conf.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003einterval]\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-153.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-12.950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.110\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-21.440\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.154\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-7.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.095\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.055\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.112\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-15.570\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.098\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eWeighted N\u0026thinsp;=\u0026thinsp;19,546,000; Pseudo R Square\u0026thinsp;=\u0026thinsp;0.07\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: Average marginal effects are estimated from logistic regressions of employment status (1\u0026thinsp;=\u0026thinsp;employed for pay in 2020, 0\u0026thinsp;=\u0026thinsp;otherwise) on gender identity, using cisgender men as the reference category. The sample includes individuals aged 18\u0026ndash;64 who were not enrolled in school, as reported in the 2021 Canadian long-form Census. Estimation controls for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province. The number of observations is rounded to the nearest ten as required by Statistics Canada. AMAB\u0026thinsp;=\u0026thinsp;assigned male at birth; AFAB\u0026thinsp;=\u0026thinsp;assigned female at birth.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eRegression Analysis: Wages\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the regression results using log hourly wage as the dependent variable to examine wage gaps across gender identities relative to cisgender men. Column 1 includes no control variables. Column 2 adds controls for potential experience and its square, immigration status, race, marital status, parental status, education, area of residence, and province fixed effects. Column 3 further incorporates occupation, TEER, and industry fixed effects. The bottom panel of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reports the p-values from tests of coefficient equality across all possible pairs of gender identity groups.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated log hourly wage across gender identities relative to cisgender men\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(1)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(3)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.110***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.155***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.130***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.252***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.123***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.080***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.024)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.023)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.198***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.171***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.140***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.216***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.167***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.139***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.032)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.031)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.030)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.351***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.275***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.191***\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.021)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePotential experience and its square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmigration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital Status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAreas of residence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvinces\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupational group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry group\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ex\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeighted N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11,611,050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11,611,050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11,611,050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.088\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.136\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003ep-value testing two coefficients being equal\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCis-women\u0026thinsp;=\u0026thinsp;Transmen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCis-women\u0026thinsp;=\u0026thinsp;Transwomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.452\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.654\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCis-women\u0026thinsp;=\u0026thinsp;NB-AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.696\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.773\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCis-women\u0026thinsp;=\u0026thinsp;NB-AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransmen\u0026thinsp;=\u0026thinsp;Transwomen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.134\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransmen\u0026thinsp;=\u0026thinsp;NB-AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.369\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransmen\u0026thinsp;=\u0026thinsp;NB-AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranswomen\u0026thinsp;=\u0026thinsp;NB-AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.903\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTranswomen\u0026thinsp;=\u0026thinsp;NB-AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.093\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNB-AMAB\u0026thinsp;=\u0026thinsp;NB-AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eNotes: Estimated coefficients are from linear regressions of log hourly wages on gender identity, with cisgender men as the reference group. Model (1) is unadjusted; Model (2) adds controls for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province; Model (3) further adjusts for occupation group, TEER category, and industry sector. Robust standard errors are reported in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. AMAB\u0026thinsp;=\u0026thinsp;assigned male at birth; AFAB\u0026thinsp;=\u0026thinsp;assigned female at birth.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eColumn 1, without any controls, reflects the raw average differences in hourly wages. The estimated coefficient for cisgender women is -0.110, implying they earn about 10.4 percent less (exp(-0.110) \u0026ndash; 1 = -0.104) than cisgender men on average. The gaps are larger for all gender-diverse groups: approximately 18.0 percent for transgender women, 19.4 percent for NB-AMAB individuals, 22.3 percent for transgender men, and 29.6 percent for NB-AFAB individuals. In Column 2, after adding demographic characteristics, the traditional men\u0026ndash;women wage gap widens to about 14.4 percent, primarily driven by women\u0026rsquo;s well-documented educational advantage in recent years (Blau \u0026amp; Kahn, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). For gender-diverse groups, the wage gaps narrow, most notably for transgender men, whose gap decreases by nearly half to 11.6 percent. NB-AMAB individuals, transgender women, and NB-AFAB individuals continue to face gaps of approximately 15.4, 15.7, and 24.0 percent, respectively, relative to cisgender men. In Column 3, after adding controls for occupation, TEER, and industry fixed effects, the earnings gaps narrow for all groups compared to Column 2: cisgender women\u0026rsquo;s gap falls to 12.2 percent, transgender men\u0026rsquo;s gap declines further to 7.7 percent, transgender women\u0026rsquo;s gap decreases to 13.1 percent, NB-AMAB individuals\u0026rsquo; gap narrows to 13.0 percent, and NB-AFAB individuals\u0026rsquo; gap decreases to 17.4 percent. The decline in wage gaps from Column 2 to Column 3 suggests that sorting across occupations and industries accounts for an important share of the observed disparities, particularly for transgender men and NB-AFAB individuals.\u003c/p\u003e\u003cp\u003eFor the robustness check, we restrict the sample to full-time workers\u0026mdash;defined as those employed at least 30 hours per week for at least 27 weeks in 2020\u0026mdash;and re-estimate the regressions. The results, reported in Table A2 of the Appendix, show a broadly similar pattern and indicate that wage disparities persist among full-time workers. Moreover, in the full specification with all controls, the earnings gaps for full-time transgender men and women relative to full-time cisgender men widen to approximately 14.6 percent and 20.3 percent, respectively, which are substantially larger than the corresponding estimates in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This suggests that full-time transgender workers experience even larger gaps compared to their cisgender male peers than do part-time workers. One concern is that wages in 2020 may be affected by COVID-19, so we also use 2019 annual wages, which are available in the data, as the dependent variable. The results are reported in Table A3 in the Appendix. For comparison, we also include results using 2020 annual wages. The patterns are largely similar, although transgender women and nonbinary individuals show slightly larger gaps in 2020, suggesting that these minority groups may have been more adversely affected by COVID-19. Carpenter, Feir, et al., (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) use 2019 annual earnings as their main outcome but also show and acknowledge that their results remain unaffected when using 2020 data. Since weeks worked, hours, and occupation measures are all from 2020 or 2021, we use 2020 wages as our preferred measure.\u003c/p\u003e\u003cp\u003eWe also use alternative outcome variables\u0026mdash;including log total annual earnings in 2020, log weekly wages, log weekly earnings, weeks worked, and hours worked\u0026mdash;and re-estimate the regressions with the full set of covariates. The results, also reported in Table A3 of the Appendix, confirm the disadvantages faced by gender minorities in the labor market: transgender and nonbinary individuals work significantly fewer hours and weeks than cisgender men. The combination of reduced work time and lower hourly wages translates into even larger gaps in annual wages. The annual earnings variable we use in the Appendix is employment income in 2020, which includes self-employment income.\u003ca class=\"FNLink\" href=\"#Fn7\" id=\"#FNLinkFn7\"\u003e\u003c/a\u003e By using log annual earnings as the dependent variable, we incorporate the self-employed into the regression sample. The results using annual wages versus annual earnings are very similar, suggesting that the impact of excluding the self-employed and self-employment income is likely limited.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRegression Analysis: Heterogeneity by Occupation\u003c/h3\u003e\n\u003cp\u003eTo better understand wage gaps across gender identities, we estimate log hourly wage regressions separately for each occupation while controlling for demographic variables (the same controls as in Column 2 of Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The coefficients on the gender identity dummies are reported in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The estimates highlight how wage gaps vary across occupations. Cisgender women face relatively smaller penalties in health (4.7 percent) and arts and recreation (8.4 percent), but substantially larger gaps in natural resources (29.8 percent). Transgender men exhibit small and statistically insignificant differences from cisgender men in health, trades, manufacturing, and natural resources, suggesting comparable wage outcomes in these fields; however, they experience significant penalties in legislative and management (64.4 percent) and arts and recreation (33.8 percent). Transgender women show relatively favorable outcomes in manufacturing, health, and natural sciences, but face disadvantages in education (22.7 percent) and arts and recreation (27.6 percent), with an especially large but imprecisely estimated penalty in legislative and management (54.8 percent). For nonbinary individuals, disparities are likewise occupation-specific: AMAB individuals exhibit relatively better outcomes in health and natural sciences but worse outcomes in legislative and management (65.5 percent) and sales (23.1 percent), whereas AFAB individuals perform better in arts but face significant penalties in legislative and management (40.6 percent) and natural resources (34.6 percent). Taken together, several patterns emerge. First, health occupations stand out as the sector where wage gaps across gender minority groups are consistently small (with the exception of NB-AFAB), suggesting a more equitable pay structure. In contrast, legislative and management positions exhibit the largest and most consistent disparities, particularly for transgender and nonbinary groups, though small sample sizes in some subgroups warrant cautious interpretation. Finally, in trades, manufacturing, and natural resources, transgender men experience relatively favorable wage outcomes, whereas other gender minority groups tend to face disadvantages. This pattern may reflect that these fields reward characteristics or norms traditionally associated with maleness.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated difference in log hourly wage across gender identities relative to cisgender men in each occupation group\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLegislative and\u003c/p\u003e\u003cp\u003esenior management\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBusiness, finance\u003c/p\u003e\u003cp\u003eand administration\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNatural and\u003c/p\u003e\u003cp\u003eapplied sciences\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEducation, law\u003c/p\u003e\u003cp\u003eand social\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eArt, culture,\u003c/p\u003e\u003cp\u003erecreation\u003c/p\u003e\u003cp\u003eand sport\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSales and\u003c/p\u003e\u003cp\u003eservice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eTrades, transport\u003c/p\u003e\u003cp\u003eand equipment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNatural resources,\u003c/p\u003e\u003cp\u003eagriculture\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eManufacturing\u003c/p\u003e\u003cp\u003eand utilities\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.161***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.158***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.117***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.048***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.238***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.088***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.170***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.097***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.354***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.230***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.010)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0.005)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.032***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.157***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.118**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.251***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.413**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.092**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.389)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.060)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.073)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.171)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.051)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.228)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0.081)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.794\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.166***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.257***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.323**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.159***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.156**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.163\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.045\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.526)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.045)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.075)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.061)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.160)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.043)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.210)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0.114)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1.063*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.248***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.150**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.263***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.158**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.158\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.215\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.552)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.070)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.066)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.125)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.072)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.146)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.418)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.521**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.232***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.242***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.237**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.248***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-0.296***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-0.197\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.425***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-0.246**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.258)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.057)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.094)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.041)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.076)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e(0.039)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e(0.156)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e(0.154)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e(0.112)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeighted N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e185,410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,275,100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1,181,400\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e968,950\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,558,320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e283,680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2,271,460\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2,044,820\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e246,970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e594,940\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.086\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.111\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eNotes: Estimates are from separate linear regressions of log hourly wages on gender identity for each major occupational group, using cisgender men as the reference category. All models control for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province of residence. Robust standard errors are shown in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. AMAB\u0026thinsp;=\u0026thinsp;assigned male at birth; AFAB\u0026thinsp;=\u0026thinsp;assigned female at birth.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eRegression Analysis: Heterogeneity by TEER\u003c/h3\u003e\n\u003cp\u003eWe next estimate separate regressions of log hourly wages on gender identity within each TEER category, using cisgender men as the reference group. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e reports the results and reveals heterogeneity across occupational skill levels. Cisgender women earn between 9 and 17 percent less than cisgender men across TEER levels, with the largest penalties observed in TEER 2 (16.3 percent) and TEER 5 (14.3 percent). Transgender men earn roughly 29 percent less than cisgender men in TEER 0, though the gap narrows substantially at lower TEER levels, with insignificant differences in TEER 3 through TEER 5. Transgender women face persistent penalties across all TEER levels except TEER 5, ranging from approximately 22 percent in TEER 0 to 12 percent in TEER 2. Similarly, NB-AMAB individuals experience large penalties in TEER 0 (36 percent) and consistent disadvantages across other categories, except TEER 3. NB-AFAB individuals face significant penalties across all occupations, including a 31 percent gap in TEER 0 and a 29 percent gap in TEER 2. Overall, the TEER-specific regressions reveal distinct gradients in wage inequality. Cisgender women face modest but relatively consistent penalties across all skill levels. In contrast, wage gaps for gender minorities are larger and more variable, with the steepest penalties observed in higher-skill occupations (particularly TEER 0). At the lower end of the skill distribution, such as TEER 5, penalties are much smaller for transgender individuals, though nonbinary individuals continue to experience sizable gaps.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimated difference in log hourly wage across gender identities relative to cisgender men in each TEER\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTeer 0\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTeer 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTeer 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTeer 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTeer 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTeer 5\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCisgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.137***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.109***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.178***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.097***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.136***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.154***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.002)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.003)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender men\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.342***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.164***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.095**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.091)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.042)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.059)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.051)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTransgender women\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.247***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.194***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.126***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.156***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.166***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.083)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.048)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.050)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.064)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AMAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.448***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.093*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.123**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.203**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.130*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.152)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.056)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.078)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.086)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.068)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-binary AFAB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.374***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.203***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.337***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.163***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.247***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.133**\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e(0.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(0.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e(0.062)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e(0.040)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e(0.058)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeighted N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1,550,850\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,536,560\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,664,710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,909,920\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,675,630\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1,273,380\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eR Square\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.049\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.041\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: Estimates are derived from separate linear regressions of log hourly wages on gender identity within each TEER (Training, Education, Experience, and Responsibilities) category, using cisgender men as the reference group. All regressions control for potential experience and its square, immigrant status, race, marital status, parental status, education, area of residence, and province of residence. Robust standard errors are reported in parentheses. The number of observations is rounded to the nearest ten as required by Statistics Canada. Significance levels: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. AMAB\u0026thinsp;=\u0026thinsp;assigned male at birth; AFAB\u0026thinsp;=\u0026thinsp;assigned female at birth.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eOaxaca\u0026ndash;Blinder Decompositions\u003c/h2\u003e\u003cp\u003eTo further examine the extent to which wage gaps are attributable to compositional factors (e.g., occupation, industry) versus unexplained components that may reflect discrimination, we implement Oaxaca\u0026ndash;Blinder decompositions. Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reports the decomposition of the log hourly wage gap between cisgender individuals (cisgender men and women combined) and non-cisgender individuals (nonbinary and transgender people combined) among full-time workers. We restrict the sample to full-time workers to eliminate the influence of differences in hours worked. The decomposition separates the mean gender wage gap into two main components: the endowment effects (explained portion), which reflect differences in the average values of predictors across genders, and the coefficient effects (unexplained portion), which arise from variations in the relationship between these predictors and earnings across different gender identities. Because the primary explanatory variables\u0026mdash;occupation and industry\u0026mdash;are categorical, the choice of a reference or omitted group complicates interpretation. For instance, it becomes difficult to distinguish the part of the unexplained wage gap that is truly attributable to a group from the part driven by differences in the coefficients of the omitted category. To address this issue, we adopt a modified approach that normalizes coefficients by transforming the categorical variables prior to estimation, following the methods proposed by Oaxaca \u0026amp; Ransom, (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), Gardeazabal \u0026amp; Ugidos, (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and M. Yun, (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDecomposition of wage gap between cisgender (combined cisgender men and women) and non-cisgender (combined nonbinary and transgender people) full-time workers\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoefficient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;t\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e[95% conf.\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003einterval]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperience\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e40.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarriage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmigration\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParent\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.1%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProvince\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRurality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-3.4%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-1.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOccupation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.9%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTeer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.5%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal explained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.104\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.094\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e51.8%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal unexplained\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.2%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal wage gap\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.181\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e100.0%\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eWeighted N\u0026thinsp;=\u0026thinsp;11,201,270\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: This table presents Oaxaca\u0026ndash;Blinder decompositions of the mean log hourly wage gap between cisgender (cisgender men and women combined) and non-cisgender (transgender and non-binary) full-time workers. The decomposition separates the total gap into an \u003cem\u003eexplained\u003c/em\u003e component\u0026mdash;attributable to group differences in characteristics such as experience, marital status, immigration, race, parental status, province, rurality, education, occupation, TEER category, and industry\u0026mdash;and an \u003cem\u003eunexplained\u003c/em\u003e component, reflecting differences in returns to these characteristics. The percentage column shows each factor\u0026rsquo;s contribution to the total explained portion of the wage gap. The number of observations is rounded to the nearest ten as required by Statistics Canada. AMAB\u0026thinsp;=\u0026thinsp;assigned male at birth; AFAB\u0026thinsp;=\u0026thinsp;assigned female at birth.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe last three rows of Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e indicate that approximately 48 percent of the total wage gap is explained by observable characteristics, while the remaining 52 percent is unexplained. Experience emerges as the most substantial factor, accounting for approximately 40 percent of the wage gap. Industry also plays an important role, explaining about 10.2 percent of the observed disparity. Other notable contributors include marital status (6.0 percent), occupation (5.9 percent), occupational skill level as indicated by TEER (2.5 percent), and parental status (2.1 percent). By contrast, some factors contribute negatively to the wage gap, suggesting areas where noncisgender individuals hold relative advantages compared to cisgender counterparts. Immigration status accounts for a negative 3.9 percent, while province and rurality each contribute around \u0026minus;\u0026thinsp;3.5 percent, reflecting geographic differences in opportunities. Education also contributes negatively by about 1.8 percent, consistent with earlier evidence of higher educational attainment among nonbinary individuals. Overall, these decomposition results suggest that while experience and occupational sorting are major drivers of wage disparities, certain demographic characteristics partly offset these gaps.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eDiscussion and Conclusion\u003c/h2\u003e\u003cp\u003eIn this paper, we use the 2021 Canadian Census to examine the labor market outcomes of transgender and nonbinary individuals. Our findings highlight persistent and sizable earnings disparities for transgender and nonbinary individuals in the Canadian labor market. Transgender women and nonbinary individuals earn roughly 15 percent less per hour than comparable cisgender men, after adjusting for demographic, occupational, and industrial factors. Transgender men fare somewhat better: although their raw wage gaps are sizable, about two-thirds of the difference is explained by demographic characteristics and sorting across jobs and sectors, and in the full specification their earnings penalties shrink to less than 10 percent.\u003c/p\u003e\u003cp\u003eCombining the distribution by occupation group in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e with the subgroup analyses in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e reveals several important patterns. In legislative and senior management occupations, transgender and nonbinary individuals are underrepresented relative to cisgender men, and this category also shows the largest wage gaps across all occupations, indicating both barriers to entry and disadvantageous treatment within the field. In health-related occupations, by contrast, transgender and nonbinary individuals are more represented than cisgender men, and wage gaps within the occupation are much smaller (except for NB-AFAB), suggesting fewer barriers to entry and a more equitable pay structure. Transgender and nonbinary individuals are also overrepresented in sales and service compared to cisgender individuals, but wage gaps in this sector remain sizable and significant. In arts and sports, nonbinary individuals are heavily represented and experience relatively favorable wage outcomes. In traditionally male-dominated occupations such as trades, natural resources, and manufacturing, transgender men are more represented than other gender minority groups, and their wages are not significantly different from those of cisgender men. Taken together, these patterns highlight substantial heterogeneity across occupations\u0026mdash;variation that has not been systematically examined in the existing literature.\u003c/p\u003e\u003cp\u003eWe also decompose the wage gap between cisgender and non-cisgender individuals and find that roughly half can be explained by observable characteristics, while the remaining half is unexplained. Compared to the literature showing that most of the traditional male\u0026ndash;female wage gap is accounted for by observable factors (Blau \u0026amp; Kahn, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), our results suggest a different pattern for transgender and nonbinary individuals. While the unexplained component cannot be fully attributed to discrimination, since it may also reflect unobserved productivity-related characteristics, the relatively larger unexplained share in the gap indicates that discrimination may play a more prominent role than in the male\u0026ndash;female gap.\u003c/p\u003e\u003cp\u003eOur findings are broadly consistent with prior evidence from the United States and other OECD countries, which has documented persistent wage and employment disadvantages for transgender and nonbinary individuals (Aksoy et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Badgett et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Carpenter et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Carpenter, Goodman, et al., 2024). Using BRFSS data, Carpenter et al., (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that transgender adults in the United States were 11 percentage points less likely to be employed and earned 16\u0026ndash;20 percent less than cisgender men, even after adjusting for demographics. Drawing on linked U.S. Social Security and IRS administrative records, Carpenter, Goodman, et al., (2024) estimated a 6\u0026ndash;13 log-point wage penalty for transgender workers, with larger gaps among transgender women. Experimental evidence from Aksoy et al., (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) revealed that transgender applicants in Europe were 22\u0026ndash;25 percent less likely to receive callbacks than comparable cisgender applicants, while Shannon, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) found that individuals assigned female at birth who identify as transgender or nonbinary earn 10\u0026ndash;15 percent less than their assigned-male peers, underscoring a persistent \u0026ldquo;maleness premium.\u0026rdquo; In a Latin American context, Nettuno, (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) reported that transgender and nonbinary workers in Chile face an average 18 percent earnings penalty relative to cisgender men, with nonbinary individuals experiencing the largest gaps. Together with Badgett et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), who show global LGBTQ\u0026thinsp;+\u0026thinsp;wage gaps ranging from 5 to 30 percent, these studies suggest pervasive, cross-national inequities. Our Canadian results fall toward the upper end of this range, with wage penalties of 15\u0026ndash;20 percent for transgender women and nonbinary individuals after covariate adjustment. A key advantage of our study lies in the unprecedented sample size of the 2021 Canadian Census, which enables detailed subgroup analysis across occupational and skill sectors\u0026mdash;something unattainable in prior survey-based studies. This granularity reveals that wage gaps are not uniform but concentrated in specific occupational contexts, providing new evidence on the structural sources of gender-diverse wage inequality.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. Although the 2021 Census provides an unprecedented opportunity to examine labor market outcomes by gender identity, the Census does not collect information on sexual orientation, workplace climate, discrimination experiences, or gender transition history, all of which could shed light on the mechanisms underlying observed wage disparities. Future research could integrate Census microdata with administrative earnings records or specialized survey data to capture these dimensions and explore how they interact with labor market processes. As more countries incorporate gender identity measures into national censuses, cross-national comparative studies will be essential for understanding how institutional and policy environments shape the economic inclusion of gender-diverse populations.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eIn this paper, we use the 2021 Canadian Census to examine the labor market outcomes of transgender and nonbinary individuals. Our findings highlight persistent and sizable earnings disparities for transgender and nonbinary individuals in the Canadian labor market. Transgender women and nonbinary individuals earn roughly 15 percent less per hour than comparable cisgender men, after adjusting for demographic, occupational, and industrial factors. Transgender men fare somewhat better: although their raw wage gaps are sizable, about two-thirds of the difference is explained by demographic characteristics and sorting across jobs and sectors, and in the full specification their earnings penalties shrink to less than 10 percent.\u003c/p\u003e\n\u003cp\u003eCombining the distribution by occupation group in Table 2 with the subgroup analyses in Table 5 reveals several important patterns. In legislative and senior management occupations, transgender and nonbinary individuals are underrepresented relative to cisgender men, and this category also shows the largest wage gaps across all occupations, indicating both barriers to entry and disadvantageous treatment within the field. In health-related occupations, by contrast, transgender and nonbinary individuals are more represented than cisgender men, and wage gaps within the occupation are much smaller (except for NB-AFAB), suggesting fewer barriers to entry and a more equitable pay structure. Transgender and nonbinary individuals are also overrepresented in sales and service compared to cisgender individuals, but wage gaps in this sector remain sizable and significant. In arts and sports, nonbinary individuals are heavily represented and experience relatively favorable wage outcomes. In traditionally male-dominated occupations such as trades, natural resources, and manufacturing, transgender men are more represented than other gender minority groups, and their wages are not significantly different from those of cisgender men. Taken together, these patterns highlight substantial heterogeneity across occupations\u0026mdash;variation that has not been systematically examined in the existing literature.\u003c/p\u003e\n\u003cp\u003eWe also decompose the wage gap between cisgender and non-cisgender individuals and find that roughly half can be explained by observable characteristics, while the remaining half is unexplained. Compared to the literature showing that most of the traditional male\u0026ndash;female wage gap is accounted for by observable factors (Blau \u0026amp; Kahn, 2017), our results suggest a different pattern for transgender and nonbinary individuals. While the unexplained component cannot be fully attributed to discrimination, since it may also reflect unobserved productivity-related characteristics, the relatively larger unexplained share in the gap indicates that discrimination may play a more prominent role than in the male\u0026ndash;female gap.\u003c/p\u003e\n\u003cp\u003eOur findings are broadly consistent with prior evidence from the United States and other OECD countries, which has documented persistent wage and employment disadvantages for transgender and nonbinary individuals (Aksoy et al., 2025; Badgett et al., 2024; Carpenter et al., 2020; Carpenter, Goodman, et al., 2024). Using BRFSS data, Carpenter et al., (2020) reported that transgender adults in the United States were 11 percentage points less likely to be employed and earned 16\u0026ndash;20 percent less than cisgender men, even after adjusting for demographics. Drawing on linked U.S. Social Security and IRS administrative records, Carpenter, Goodman, et al., (2024) estimated a 6\u0026ndash;13 log-point wage penalty for transgender workers, with larger gaps among transgender women. Experimental evidence from Aksoy et al., (2025) revealed that transgender applicants in Europe were 22\u0026ndash;25 percent less likely to receive callbacks than comparable cisgender applicants, while Shannon, (2022) found that individuals assigned female at birth who identify as transgender or nonbinary earn 10\u0026ndash;15 percent less than their assigned-male peers, underscoring a persistent \u0026ldquo;maleness premium.\u0026rdquo; In a Latin American context, Nettuno, (2024) reported that transgender and nonbinary workers in Chile face an average 18 percent earnings penalty relative to cisgender men, with nonbinary individuals experiencing the largest gaps. Together with Badgett et al., (2024), who show global LGBTQ+ wage gaps ranging from 5 to 30 percent, these studies suggest pervasive, cross-national inequities. Our Canadian results fall toward the upper end of this range, with wage penalties of 15\u0026ndash;20 percent for transgender women and nonbinary individuals after covariate adjustment. A key advantage of our study lies in the unprecedented sample size of the 2021 Canadian Census, which enables detailed subgroup analysis across occupational and skill sectors\u0026mdash;something unattainable in prior survey-based studies. This granularity reveals that wage gaps are not uniform but concentrated in specific occupational contexts, providing new evidence on the structural sources of gender-diverse wage inequality.\u003c/p\u003e\n\u003cp\u003eSeveral limitations should be acknowledged. Although the 2021 Census provides an unprecedented opportunity to examine labor market outcomes by gender identity, the Census does not collect information on sexual orientation, workplace climate, discrimination experiences, or gender transition history, all of which could shed light on the mechanisms underlying observed wage disparities. Future research could integrate Census microdata with administrative earnings records or specialized survey data to capture these dimensions and explore how they interact with labor market processes. As more countries incorporate gender identity measures into national censuses, cross-national comparative studies will be essential for understanding how institutional and policy environments shape the economic inclusion of gender-diverse populations.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of Interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThe study is supported by Antony Chum through the Canada Research Chair program (CRC-2021-00269). The project was also partially funded by the Social Sciences and Humanities Research (SSHRC) Project Grant, File no: 435-2023-1102, and the Canadian Institutes of Health Research (CIHR) Project Grant, File no: 497334. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY.B., A.C., and Q.L. wrote the main manuscript text, and Y.B. prepared the tables and figures. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThis study is based on confidential microdata accessed through the Statistics Canada Research Data Centre (RDC). Researchers can apply for access to these data through Statistics Canada\u0026rsquo;s Research Data Centres Program at [](https:/www.statcan.gc.ca/en/microdata/data-centres/access) [https://www.statcan.gc.ca/en/microdata/data-centres/access](https:/www.statcan.gc.ca/en/microdata/data-centres/access) .\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAksoy, B., Carpenter, C. S., \u0026amp; Sansone, D. (2025). Understanding labor market discrimination against transgender people: Evidence from a double list experiment and a survey. \u003cem\u003eManagement Science\u003c/em\u003e, \u003cem\u003e71\u003c/em\u003e(1), 659\u0026ndash;677. \u003c/li\u003e\n\u003cli\u003eBadgett, M. L., Carpenter, C. S., Lee, M. J., \u0026amp; Sansone, D. (2024). A review of the economics of sexual orientation and gender identity. \u003cem\u003eJournal of Economic Literature\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e(3), 948\u0026ndash;994. \u003c/li\u003e\n\u003cli\u003eBlau, F. D., \u0026amp; Kahn, L. M. (2017). The gender wage gap: Extent, trends, and explanations. \u003cem\u003eJournal of Economic Literature\u003c/em\u003e, \u003cem\u003e55\u003c/em\u003e(3), 789\u0026ndash;865. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Eppink, S. T., \u0026amp; Gonzales, G. (2020). Transgender status, gender identity, and socioeconomic outcomes in the United States. \u003cem\u003eILR Review\u003c/em\u003e, \u003cem\u003e73\u003c/em\u003e(3), 573\u0026ndash;599. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Feir, D. L., Pendakur, K., \u0026amp; Warman, C. (2024). \u003cem\u003eNonbinary Gender Identities and Earnings: Evidence from a National Census\u003c/em\u003e. National Bureau of Economic Research. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Feir, D., Pendakur, K., \u0026amp; Warman, C. (2025). Nonbinary and Transgender Identities and Earnings: Evidence from a National Census. \u003cem\u003eAmerican Economic Review: Insights\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Goodman, L., \u0026amp; Lee, M. J. (2024). \u003cem\u003eTransgender earnings gaps in the United States: Evidence from administrative data\u003c/em\u003e. National Bureau of Economic Research. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Kirkpatrick, L., Lee, M. J., \u0026amp; Plum, A. (2025). Economic outcomes of gender diverse people: New evidence from linked administrative data in New Zealand. \u003cem\u003eEconomics Letters\u003c/em\u003e, \u003cem\u003e247\u003c/em\u003e, 112155. \u003c/li\u003e\n\u003cli\u003eCarpenter, C. S., Lee, M. J., \u0026amp; Nettuno, L. (2022). Economic outcomes for transgender people and other gender minorities in the United States: First estimates from a nationally representative sample. \u003cem\u003eSouthern Economic Journal\u003c/em\u003e, \u003cem\u003e89\u003c/em\u003e(2), 280\u0026ndash;304. \u003c/li\u003e\n\u003cli\u003eCiprikis, K., Cassells, D., \u0026amp; Berrill, J. (2020). Transgender labour market outcomes: Evidence from the United States. \u003cem\u003eGender, Work \u0026amp; Organization\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(6), 1378\u0026ndash;1401. \u003c/li\u003e\n\u003cli\u003eCoffman, K., Coffman, L., \u0026amp; Ericson, K. M. (2024). \u003cem\u003eNon-binary gender economics\u003c/em\u003e. \u003c/li\u003e\n\u003cli\u003eDrydakis, N. (2022). Sexual orientation and earnings: A meta-analysis 2012\u0026ndash;2020. \u003cem\u003eJournal of Population Economics\u003c/em\u003e, \u003cem\u003e35\u003c/em\u003e(2), 409\u0026ndash;440. \u003c/li\u003e\n\u003cli\u003eGardeazabal, J., \u0026amp; Ugidos, A. (2004). More on identification in detailed wage decompositions. \u003cem\u003eReview of Economics and Statistics\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e(4), 1034\u0026ndash;1036. \u003c/li\u003e\n\u003cli\u003eGeijtenbeek, L., \u0026amp; Plug, E. (2018). Is there a penalty for registered women? Is there a premium for registered men? Evidence from a sample of transsexual workers. \u003cem\u003eEuropean Economic Review\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e, 334\u0026ndash;347. \u003c/li\u003e\n\u003cli\u003eNettuno, L. (2024). Gender identity, labor market outcomes, and socioeconomic status: Evidence from Chile. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e, 102487. \u003c/li\u003e\n\u003cli\u003eOaxaca, R. L., \u0026amp; Ransom, M. R. (1999). Identification in detailed wage decompositions. \u003cem\u003eReview of Economics and Statistics\u003c/em\u003e, \u003cem\u003e81\u003c/em\u003e(1), 154\u0026ndash;157. \u003c/li\u003e\n\u003cli\u003eSchilt, K., \u0026amp; Wiswall, M. (2008). Before and after: Gender transitions, human capital, and workplace experiences. \u003cem\u003eThe BE Journal of Economic Analysis \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e8\u003c/em\u003e(1). \u003c/li\u003e\n\u003cli\u003eShannon, M. (2022). The labour market outcomes of transgender individuals. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e, 102006. \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (n.d.). \u003cem\u003eGuide to the Census of Population, 2021, Chapter 12 \u0026ndash; Sampling and weighting for the long form\u003c/em\u003e. Retrieved October 15, 2025, from https://www12.statcan.gc.ca/census-recensement/2021/ref/98-304/2021001/chap12-eng.cfm \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2018, August 17). \u003cem\u003eNorth American Industry Classification System (NAICS) Canada 2017 Version 3.0\u003c/em\u003e. https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD\u0026amp;TVD=1181553 \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2021a, September 15). \u003cem\u003eNational Occupational Classification (NOC) 2021 Version 1.0\u003c/em\u003e. https://www23.statcan.gc.ca/imdb/p3VD.pl?Function=getVD\u0026amp;TVD=1322554 \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2021b, November 17). \u003cem\u003e2021 Census of Population collection response rates\u003c/em\u003e. https://www12.statcan.gc.ca/census-recensement/2021/ref/response-rates-eng.cfm \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2022a, April 6). \u003cem\u003eFilling the gaps: Information on gender in the 2021 Census\u003c/em\u003e. https://www12.statcan.gc.ca/census-recensement/2021/ref/98-20-0001/982000012021001-eng.cfm \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2022b, April 27). \u003cem\u003eThe Daily\u0026mdash;Canada is the first country to provide census data on transgender and non-binary people\u003c/em\u003e. https://www150.statcan.gc.ca/n1/daily-quotidien/220427/dq220427b-eng.htm \u003c/li\u003e\n\u003cli\u003eStatistics Canada. (2024, October 23). \u003cem\u003eCoverage Technical Report, Census of Population, 2021\u003c/em\u003e. https://www12.statcan.gc.ca/census-recensement/2021/ref/98-303/index-eng.cfm \u003c/li\u003e\n\u003cli\u003eStatistics Canada of Canada. (2022, April 27). \u003cem\u003eThe Daily\u0026mdash;Canada is the first country to provide census data on transgender and non-binary people\u003c/em\u003e. https://www150.statcan.gc.ca/n1/daily-quotidien/220427/dq220427b-eng.htm \u003c/li\u003e\n\u003cli\u003eWaite, S., Ecker, J., \u0026amp; Ross, L. E. (2019). A systematic review and thematic synthesis of Canada\u0026rsquo;s LGBTQ2S+ employment, labour market and earnings literature. \u003cem\u003ePloS One\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(10), e0223372. \u003c/li\u003e\n\u003cli\u003eYun, M. (2005). A simple solution to the identification problem in detailed wage decompositions. \u003cem\u003eEconomic Inquiry\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(4), 766\u0026ndash;772. \u003c/li\u003e\n\u003cli\u003eYun, M.-S. (2008). Identification problem and detailed Oaxaca decomposition: A general solution and inference. \u003cem\u003eJournal of Economic and Social Measurement\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(1), 27\u0026ndash;38. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Footnotes","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e See Badgett et al., (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) for a recent review. There is also a growing literature on labor market outcomes of LGBT\u0026thinsp;+\u0026thinsp;population; see Drydakis, (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for a meta-analysis.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e A smaller literature has examined the effects of gender transition on individual earnings trajectories (Geijtenbeek \u0026amp; Plug, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Schilt \u0026amp; Wiswall, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e We use a variable on school attendance that indicates whether a person attended, either full-time or part-time, any accredited educational institution or program at any point during the nine-month period from September 2020 to May 11, 2021. Individuals who answered \u0026ldquo;yes\u0026rdquo; are classified as students and are excluded from our sample.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The number of observations is rounded to the nearest ten, as required by Statistics Canada.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e The number of weeks worked includes paid vacation, sick leave, and training, and is set to 52 weeks for individuals paid year-round.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports column percentages (i.e., the values within each column sum to 100 percent). Row percentages are provided separately in Table A1 in the Appendix.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003e According to the data codebook, employment income refers to all income received as wages, salaries, and commissions from paid employment, as well as net self-employment income from farm or non-farm unincorporated businesses and/or professional practice during 2020.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"review-of-economics-of-the-household","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reho","sideBox":"Learn more about [Review of Economics of the Household](http://link.springer.com/journal/11150)","snPcode":"11150","submissionUrl":"https://submission.nature.com/new-submission/11150/3","title":"Review of Economics of the Household","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"wage gap, labor market outcomes, gender identity, transgender, non-binary people, Canadian Census","lastPublishedDoi":"10.21203/rs.3.rs-8080057/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8080057/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL code:\u003c/strong\u003e J0\u003c/p\u003e","manuscriptTitle":"Beyond the Gender Binary: Wage Inequality and Occupational Segregation among Transgender and Nonbinary Workers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-24 06:55:12","doi":"10.21203/rs.3.rs-8080057/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-06T05:18:31+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"293840042492961287193311712508680956728","date":"2025-12-17T15:14:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-09T13:04:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5212052084260777243854250097905718244","date":"2025-11-12T08:18:06+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-12T05:08:36+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-12T05:04:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-12T03:56:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Review of Economics of the Household","date":"2025-11-10T18:58:27+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"review-of-economics-of-the-household","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"reho","sideBox":"Learn more about [Review of Economics of the Household](http://link.springer.com/journal/11150)","snPcode":"11150","submissionUrl":"https://submission.nature.com/new-submission/11150/3","title":"Review of Economics of the Household","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8d52977d-2f5c-4264-8e85-69dc2b8a0e42","owner":[],"postedDate":"November 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-17T05:24:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-24 06:55:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8080057","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8080057","identity":"rs-8080057","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}