Abstract
Despite recent media and scholarly attention describing the “disappearance” of traditionally gay neighborhoods, urban scholars have yet to quantify the segregation of same-sex partners and determine whether declining segregation from different-sex partners is a wide-spread trend. Focusing on the 100 most populous places in the United States, I use data from the 2000 and 2010 Decennial Census to examine the segregation of same-sex partners over time and its place-level correlates. I estimate linear regression models to examine the role of four place characteristics in particular: average levels of education, aggregate trends in the family life cycle of same-sex partners, violence and social hostility motivated by sexual orientation bias, and representation of same-sex partners in the overall population. On average, same-sex partners were less segregated from different-sex partners in 2010 than in 2000, and the vast majority of same-sex partners lived in environments of declining segregation. Segregation was lower and declined more rapidly in places that had a greater percentage of graduate degree holders. In addition, segregation of female partners was lower in places that had a greater share of female partner households with children. These findings suggest that sexual orientation should be considered alongside economic status, race, and ethnicity as an important factor that contributes to neighborhood differentiation and urban spatial inequality.
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Notes
Census-designated places are the statistical counterparts of incorporated places. They are settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located.
The adjustment procedure involved four steps: First, I developed estimates of the state error rate (the percent of different-sex, husband-wife and unmarried partner households that miscoded the sex of a spouse or partner) using data from the Census Bureau (available from http://www.census.gov/hhes/samesex/). Second, I multiplied the state error rate for husband–wife households by the original number of husband–wife households (separately for male-headed and female-headed households), and then did the same for different-sex unmarried partner households, in order to obtain the estimated number of miscodes by sex and marital status. Third, I subtracted the estimated number of miscodes from the original number of comparable same-sex couples in each tract to obtain the adjusted number of male and female same-sex couples. This involved subtracting miscoded different-sex couples with a male householder from the same-sex male counts and vice versa for females. Census 2000 did not tabulate the sex of the householder in husband-wife households, so counts of male- and female-headed husband–wife households were estimated using the ratio of male- and female-headed husband-wife households in the tract in 2010. Fourth, I added the estimated number of miscodes to the original counts of husband–wife and different-sex unmarried partner households to obtain adjusted estimates. Take census tract 205 in San Francisco, California as an example. The California state error rate was estimated to be 0.4 % for husband–wife households and 0.5 % for different-sex unmarried partners in 2010. The 2010 original data reported that census tract 205 contained 212 male same-sex partner households, 32 female same-sex partner households, 163 male-headed husband-wife households, 24 male-headed different-sex unmarried partner households, 72 female-headed husband–wife households, and 33 female-headed different-sex unmarried partner households. The 2010 adjusted number of male same-sex partners is 212 − (0.004 × 163 + 0.005 × 24), the adjusted number of female same-sex partners is 32 − (0.004 × 72 + 0.005 × 33), the adjusted number of husband-wife households is (163 + 72) − (0.004 × 163 + 0.004 × 72), and the adjusted number of different-sex unmarried partners is (24 + 33) − (0.005 × 24 + 0.005 × 33). In 2000, the California state error rate was estimated to be 0.4 % for husband–wife households and 0.5 % for different-sex unmarried partners. The 2000 original data reported that census tract 205 contained 207 male same-sex partner households, 23 female same-sex partner households, 200 husband–wife households (male- and female-headed not reported separately), 30 male-headed different-sex unmarried partner households, and 24 female-headed different-sex unmarried partner households. Using the 2010 ratio of husband–wife households that were male-headed (163/235 = 0.69) and female-headed (72/235 = 0.31), the number of male-headed husband–wife households in 2000 is estimated to be 138 (0.69 × 200) and the number of female-headed husband–wife households is estimated to be 62 (0.31 × 200). The 2000 adjusted number of male same-sex partners is 207 − (0.004 × 138 + 0.005 × 30), the adjusted number of female same-sex partners is 23 − (0.004 × 62 + 0.005 × 24), the adjusted number of husband–wife households is 200 − (0.004 × 200), and the adjusted number of different-sex unmarried partners is (30 + 24) − (0.005 × 30 + 0.005 × 24).
The dependent variable in the cross-sectional female model did exhibit a non-normal distribution which was somewhat skewed to the right. As a result, the model disturbances also exhibited heteroscedasticity. An appropriate solution would be to transform the dependent variable so that it approximates a normal distribution, and logging achieved this goal. However, the substantive results of the transformed model were identical to the un-transformed model, so for simplicity, I chose to report the latter.
I conducted diagnostics tests for normality of the dependent variable, endogeneity, heteroscedasticity, multicollinearity, spatial autocorrelation, and nonlinear relationships between the dependent variables and each of the independent variables. Based on these diagnostics, I am confident that the assumptions of OLS are met, apart from the discussion of the female cross-sectional model in the previous footnote.
Census tract boundaries did not perfectly align with place boundaries, and occasionally straddled more than one place. Using ArcGIS software, tracts were assigned to places if the majority of their land area fell within the place’s boundaries.
Seventeen places in the sample were a concern, having fewer than 100 male–male partner households in 2000 or 2010. The seventeen places are Augusta, GA; Chesapeake, VA; Chula Vista, CA; Colorado Springs, CO; Corpus Christi, TX; Fremont, CA; Garland, TX; Glendale, AZ; Hialeah, FL; Lincoln, NE; Lubbock, TX; Modesto, CA; Montgomery, AL; North Las Vegas, NV; Plano, TX; San Bernardino, CA; and Virginia Beach, VA. I conducted a supplementary analysis excluding those seventeen places. Because there are only a few concerning places and the original results were weighted by the size of the male partnered population, results of the supplementary analysis were almost identical to the original analysis, and therefore are not reported. No places in the sample had fewer than 100 female same-sex partner households in 2000 or 2010.
The seven places that did not submit any crime reports from 2005 to 2009 were Birmingham, AL; Augusta-Richmond County, GA; Honolulu, HI; Baton Rouge, LA; Paradise, NV; Toledo, OH; and Arlington, VA. Imputed values of total hate crimes from 2005 to 2009 were estimated from a multiple imputation model using the negative binomial method for overdispersed count variables. Predictor variables were the outcome variable, the total number of crime reports submitted from 2005 to 2009, logged population, population density, percent of the population age 25+ with a graduate degree, percent of the population in poverty, median household income, percent of the population that was a racial/ethnic minority, percent of housing units built in the previous 10 years, and region.
Using places as the units of analysis resulted in lower estimates of the index of dissimilarity compared to using metropolitan areas as the units of analysis. I chose to use places because the estimates were more conservative, and because place boundaries align with the jurisdictional boundaries used in the FBI Uniform Crime Report data.
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Acknowledgments
The author acknowledges Stewart Tolnay and four anonymous reviewers for their helpful comments. Partial support for this research came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development training grant, T32 HD007543, to the Center for Studies in Demography & Ecology at the University of Washington.
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Spring, A.L. Declining Segregation of Same-Sex Partners: Evidence from Census 2000 and 2010. Popul Res Policy Rev 32, 687–716 (2013). https://doi.org/10.1007/s11113-013-9280-y
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DOI: https://doi.org/10.1007/s11113-013-9280-y