Using a probability-based sample from 39 U.S. states from a general health survey, the author evaluates popular claims of a transgender tipping point by estimating probabilities of identifying as transgender and gender nonconforming among cohorts of respondents born between 1935 and 2001. Respondents born after 1984 are significantly more likely to identify as transgender or gender nonconforming than respondents in earlier cohorts. However, cohort changes in identification as transgender and gender nonconforming vary along lines of sex assigned at birth, race/ethnicity, and college attendance. Within different cohorts, these factors have different associations with higher or lower odds of identifying as transgender or gender nonconforming, sometimes contrasting with popular narratives and media representation patterns. Analyzed in context, these findings provide empirical evidence that several distinct population-level biographical availability patterns, including convergences, reversals, and persistence of demographic associations, have shaped the prevalence and composition of U.S. transgender and gender nonconforming populations over time.

In 2018, the General Social Survey (GSS) asked some respondents for their sex assigned at birth and current gender identity, in addition to the ongoing practice of having survey interviewers code respondent sex. Between 0.44% and 0.93% of the respondents who were surveyed identified as transgender, identified with a gender that does not conventionally correspond to the sex they were assigned at birth, or identified the sex they were assigned at birth inconsistently with the interviewer's assessment of respondent sex. These results corroborate previous estimates of the transgender population size in the United States. Furthermore, the implementation of these new questions mirrors the successful inclusion of other small populations represented in the GSS, such as lesbian, gay, and bisexual people, as well as Muslims, Buddhists, and Hindus. Data on transgender and gender-nonconforming populations can be pooled together over time to assess these populations' attitudes, beliefs, behaviors, and social inequality patterns. We identified inconsistencies between interviewer-coded sex, self-reported sex, and gender identity. As with the coding of race in the GSS, interviewer-coded assessments can mismatch respondents' self-reported identification. Our findings underscore the importance of continuing to ask respondents to self-report gender identity separately from sex assigned at birth in the GSS and other surveys.