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Group Cohesion without Group Mobilization: The Case of Lesbians, Gays and Bisexuals

Published online by Cambridge University Press:  07 February 2012


Group identities that are chosen, rather than inherited, are often associated with cohesive political attitudes and behaviours. Conventional wisdom holds that this distinctiveness is generated by mobilization through processes such as intra-group contact and acculturation. This article identifies another mechanism that can explain cohesiveness: selection. The characteristics that predict whether an individual selects a group identity may themselves determine political attitudes, and thus may account substantially for the political cohesion of those who share the identity. This mechanism is illustrated with analyses of the causes and consequences of the acquisition of lesbian, gay or bisexual identity. Seldom shared by parents and offspring, gay identity provides a rare opportunity to cleanly identify the selection process and its implications for political cohesion.

Research Article
Copyright © Cambridge University Press 2012

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3 In this article, I use the terms ‘lesbian, gay and bisexual’ and ‘gay’ interchangeably.

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22 Sherrill, ‘The Political Power of Gays, Lesbians, and Bisexuals’, p. 472Google Scholar. For demographic data on residential concentration of LGBs, see Gates, Gary J. and Ost, Jason, The Gay and Lesbian Atlas (Washington, D.C.: The Urban Institute Press, 2004)Google Scholar.

23 Schaffner and Senic, ‘Rights or Benefits?’

24 Lewis, Rogers, and Sherrill, ‘Lesbian, Gay, and Bisexual Voters in the 2000 Election’.

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26 See, for example, Gelman, Andrew and Hill, Jennifer, Data Analysis Using Regression and Multilevel/ Hierarchical Models (New York: Cambridge University Press, 2006)CrossRefGoogle Scholar, pp. 188–190.

27 Goodness of fit statistics reported in Table 3 (expected percentage correctly predicted and expected proportional reduction in error) are calculated as proposed in Herron, Michael C., ‘Postestimation Uncertainty in Limited Dependent Variable Models’, Political Analysis, 8 (1999), 8398CrossRefGoogle Scholar, and implemented by the epcp routine in Stata (Christopher N. Lawrence, ‘epcp: Display Classification Accuracy for Nonmetric Dependent Variable Models’, 2009, available at

28 The null hypothesis that these two predictions are equal is rejected at p = 0.03 (two-tailed test).

29 See for example Ho, Daniel E., Imai, KosukeKing, Gary and Stuart, Elizabeth A., ‘Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference’, Political Analysis, 15 (2007), 199236CrossRefGoogle Scholar; Imbens, Guido and Wooldridge, Jeffrey M., ‘Recent Developments in the Econometrics of Program Evaluation’, Journal of Economic Literature, 47 (2009), 586CrossRefGoogle Scholar.

30 In the present context, the average treatment effect, or ATE, is the proper quantity of interest as we are simply interested in the average differences in political views remaining between gays and straights after accounting for the fact that they come from dissimilar backgrounds. In the literature on program evaluation, the ATE has at times been criticized as an estimand, because it compares those treated with the entire population – including those individuals who would never be eligible for the ‘treatment’ of participation in the program. In the present context, however, there is no reason to rule out any identifiable subset of the population. Those eligible for treatment are people with the ascriptive trait of same-sex attraction – a trait we assume is not predictable with the background characteristics in Table 4.

31 Imbens and Wooldridge, ‘Recent Developments in the Econometrics of Program Evaluation’, pp. 40–41Google Scholar.

32 Because LGB respondents in the GSS are vastly outnumbered by the non-LGB respondents, in practice this means that some LGB respondents are repeatedly matched to multiple non-LGBs. Compared to matching without replacement this technique generally lowers the bias of estimates while increasing their variance. See Abadie, Alberto and Imbens, Guido W., ‘Large Sample Properties of Matching Estimators for Average Treatment Effects’, Econometrica, 74 (2006), 235267CrossRefGoogle Scholar. Software used for these estimates: Abadie, Alberto, Herr, Jane LeberImbens, Guido W. and Drukker, David M., ‘NNMATCH: Stata Module to Compute Nearest-Neighbor Bias-Corrected Estimators’, 2004, available at Scholar.

33 Diamond, Alexis and Sekhon, Jasjeet, ‘Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies’ (unpublished, University of California, Berkeley, 2010)Google Scholar. Software used for these estimates: Sekhon, Jasjeet S., ‘Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R’, Journal of Statistical Software, 42 (2011:7), 152CrossRefGoogle Scholar.

34 Statistical significance is assessed using one-tailed difference-of-means hypothesis tests, which reflect the strong theoretical expectation that gay–straight differences should be diminished after accounting for background characteristics. I refrain from making explicit comparisons regarding the relative size of selection effects among the dependent variables as the sampling distributions of these ratios are unknown without making additional assumptions.

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37 There are approximately 1,100 American adults per voting precinct, according to a ratio constructed with the following figures. In 1990, the national voting-age population was 185.5 million people (Statistical Abstract of the United States: 2007 (Washington D.C.: U.S. Census Bureau, 2006), p. 12). In that same year (the most recent for which data are available), there were about 170,000 election precincts nationwide ( King, Gary and Palmquist, Bradley, ‘The Record of American Democracy, 1984–1990’, PS: Political Science and Politics, 30 (1997), 746747Google Scholar).

38 Unfortunately, the data and specifications used here make the matching analyses in Table 5 infeasible.

39 Giddens, Anthony, Modernity and Self Identity: Self and Society in the Late Modern Age (Stanford, Calif.: Stanford University Press, 1991)Google Scholar.

40 Another important explanation for how group cohesion develops comes from social identity theory, which has found that in-group favouritism can arise under highly artificial and minimal conditions ( Henri Tajfel, Human Groups and Social Categories (Cambridge: Cambridge University Press, 1981Google Scholar); Turner, John C., Rediscovering the Social Group: A Self-Categorization Theory (Oxford: Blackwell, 1987)Google Scholar). However, like the work on mobilization, this line of research has yet to explore in detail the identity selection process discussed here (see Huddy, ‘From Social to Political Identity’).

41 Frymer, Paul, Uneasy Alliances: Race and Party Competition in America (Princeton, N.J.: Princeton University Press, 1999)Google Scholar; Frymer, Paul and Skrentny, John David, ‘Coalition Building and the Politics of Electoral Capture during the Nixon Administration: African Americans, Labor, Latinos’, Studies in American Political Development, 12 (1998), 13131361CrossRefGoogle Scholar; Smith, Charles Anthony, ‘The Electoral Capture of Gay and Lesbian Americans: Evidence and Implications from the 2004 Election’, Studies in Law, Politics and Society, 40 (2007), 103121CrossRefGoogle Scholar.

42 Lax, Jeffrey R. and Phillips, Justin H., ‘Gay Rights in the States: Public Opinion and Policy Responsiveness’, American Political Science Review, 103 (2009), 367386CrossRefGoogle Scholar; Mucciaroni, Gary, Same Sex, Different Politics: Success and Failure in the Struggles over Gay Rights (Chicago: University of Chicago Press, 2008)CrossRefGoogle Scholar.

43 Cook, Timothy E., ‘The Empirical Study of Lesbian, Gay and Bisexual Politics: Assessing the First Wave of Research’, American Political Science Review, 93 (1999), 679692CrossRefGoogle Scholar, p. 691.

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