Abstract
This paper uses census tract data from the 2000 and 2010 U.S. Censuses and the 2005–2009 American Community Survey to examine the locations of gay male and lesbian partnerships in 38 large U.S. cities. Gay men and lesbians are less segregated than African Americans and lesbians are less spatially concentrated than gay men. There is little evidence to support the common assertion that gays concentrate in more racially and ethnically diverse neighborhoods. We find evidence supporting the popular notion that concentrations of gay men lead to more rapid development of central city neighborhoods. Census tracts that start the decade with more gay men experience significantly greater growth in household incomes (and, therefore, presumably housing prices) and greater population growth over the next decade than those census tracts with fewer gay men. Census tracts with more lesbians at the start of the decade see no difference in population or income growth.
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Notes
- 1.
There is an extensive literature on sexuality and space that explores the variety of reasons that gays and lesbians do or do not concentrate spatially within cities (for example, Brown 2013; Lauria and Knopp 1985; Harry 1974). We do not address those questions here. Rather, we analyze the kinds of neighborhoods that attract concentrations of gays and lesbians and the effects of those concentrations on subsequent development of the neighborhoods.
- 2.
We use census tract data that is from the short form (SF1) answered by all households in the census. We cannot use these data to measure differences within tracts, however, because the boundaries of the census tracts changed between 2000 and 2010.
- 3.
For most of these time periods and locations, same-sex marriage was not legally recognized. The Census assumed in most cases of one-sex partnerships listing a spouse relationship that the spouse identification was incorrect but the gender identification was correct. The Census changed the coding of partnerships in these cases to same-sex partners. This approach to recoding was likely to have incorrectly classified some two-sex couples who incorrectly identified the gender of one partner to same-sex partnerships. Because the pool of same-sex partners accounts for less than five percent of all partnerships, any procedure that incorrectly allocates even a very small percentage of two-sex partnerships to same-sex ones leads to substantially greater bias in estimates for same-sex partners than for two-sex ones.
- 4.
See Madden (2014) for a study of changes in the intra-metropolitan area spatial distribution of residents by race and income using the same measurement approach.
- 5.
The segregation index, the Duncan Index of Dissimilarity, is calculated:
$$ 1/2{\displaystyle \sum i}\left|{P}_i\kern0.5em -\kern0.5em n{P}_i\right| $$where Pi is the proportion of the city’s gay male (lesbian) households in census tract i and nPi is the proportion of the city’s non-gay households in census tract i.
The index takes on values between 0 and 1, where 0 indicates no segregation (partnerships of different sexual compositions are sorted identically across neighborhoods) and 1 indicates perfect segregation (gay partnership households and heterosexual partnership households live in completely different neighborhoods).
- 6.
Madden (2014) reports African American segregation indices of 0.46 for 2000 and 0.44 for 2009 for these same Western metro areas, 0.55 and 0.51 respectively for the South, 0.71 and 0.67 for the Midwest, 0.65 and 0.63 for the Northeast.
- 7.
Moran’s I is calculated as:
\( I\kern0.5em =\kern0.5em \frac{N}{{\displaystyle {\sum}_i}{\displaystyle {\sum}_j}{w}_{ij}}\left(\frac{{\displaystyle {\sum}_i}{\displaystyle {\sum}_j}{\operatorname{w}}_{ij}\left( Xi,-,\overline{X}\right)\left(Xj,-,\overline{X}\right)}{{\displaystyle {\sum}_i}{\left( Xi,-,\overline{X}\right)}^2}\right)\kern1.75em E(I)\kern0.5em =\kern0.75em \frac{-1}{N-1} \)
where X i is the proportion of tract i′s households that are of a given type, X j is the proportion of tract j (≠i)′s households that are of this same type, \( \overline{X} \) is the mean proportion of this household type over all tracts, w ij is a matrix denoting the spatial relationship between all tracts i and j, and N is the total number of tracts.
- 8.
Moran’s I varies between −1 and 1, with values increasing toward 1 indicative of higher levels of positive spatial autocorrelation and values decreasing toward −1 indicative of higher levels of negative spatial autocorrelation. A Moran’s I equal to its expected value, which is approximately 0 in large samples, suggests that there is no spatial autocorrelation in the data.
- 9.
We use the 2005–2009 ACS data, rather than the 2010 short form Census data, because these analyses require common boundaries across the time periods, and because many of the relevant explanatory variables are not included on the Census short form.
- 10.
Because they have been adjusted and therefore include greater uncertainty, the effects of the 2000 locations of gay male and lesbian households are measured less precisely than those of other characteristics of census tracts.
- 11.
These are the census tract’s population in the household or other demographic category divided by the city population in the same category.
- 12.
When only one of the robust LM statistics was significant, that type of model was estimated. When both of the robust LM statistics were significant, the model with the larger test statistic was chosen.
- 13.
The LM statistic tests for all household shares strongly suggested the presence of a spatial error process. The LM statistic tests for gay male and lesbian household shares appeared weaker. The spatial lag model was the most consistently “preferred”, although gay male and lesbian household share in the Midwest and gay male household share nationally showed no evidence of a spatial process. We estimated a spatial lag model for all regions nevertheless, to allow for easier comparisons between regions. The estimation of a spatial lag model in cases where there is no underlying spatial process should not unduly bias the results. We also tested a spatial error specification for gay and lesbian household share, and the results from these estimations were not substantively different than those from those shown. The LM statistics from each of the OLS estimations are shown in Table 19.6 in Appendix.
- 14.
In this case, the W matrix is block diagonal, with the main diagonal blocks equal to the queen contiguity matrix within each city. The off-diagonal blocks are composed of zero matrices.
- 15.
- 16.
In general, the coefficients from a spatial autoregressive model cannot be directly compared to the coefficients from a spatial error regression, as the lagged dependent variable in the autoregressive model introduces feedback and indirect impacts. While the coefficient for an explanatory variable (X) in a spatial error model is interpreted as the average impact of X on the outcome (Y), the average impact of X on Y in the spatial autoregressive model is calculated based on the coefficient, the spatial weight matrix, and the spatial autoregressive term [ρ in Eq. (19.2)] (LeSage and Pace 2009). In the results presented here, the feedback and indirect impacts in the autoregressive models are quite small, and do not make a substantive difference in the conclusions reached. As such, only the coefficients themselves are presented in the results tables.
- 17.
As in Table 19.3, OLS estimation is performed to test for spatial dependence in the residuals and to choose the appropriate spatial model specification. For the population growth equation a spatial error model is estimated. For the income growth equation a spatial autoregressive model is used. The LM statistics from the OLS estimations are displayed in Table 19.6 in Appendix.
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Acknowledgement
We thank Marcus Dillender, Gabrielle Fack, Gary Gates, Stephen Sheppard, and an anonymous reviewer for helpful comments on an earlier version of this paper.
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Madden, J.F., Ruther, M. (2015). Gayborhoods: Economic Development and the Concentration of Same-Sex Couples in Neighborhoods Within Large American Cities. In: Nijkamp, P., Rose, A., Kourtit, K. (eds) Regional Science Matters. Springer, Cham. https://doi.org/10.1007/978-3-319-07305-7_19
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