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Fickle Prosociality: How Violence against LGBTQ+ People Motivates Prosocial Mass Attitudes toward LGBTQ+ Group Members

Published online by Cambridge University Press:  27 December 2024

MARCEL F. ROMAN*
Affiliation:
Harvard University, United States
JACK THOMPSON*
Affiliation:
University of Leeds, United Kingdom
*
Corresponding author: Marcel F. Roman, Assistant Professor, Department of Government, Harvard University, United States, mroman@fas.harvard.edu.
Jack Thompson, Postdoctoral Research Fellow, Business School, University of Leeds, United Kingdom, J.Thompson@leeds.ac.uk.
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Abstract

We present a Fickle Prosocial Violence Response Model to explain how indirect exposure to civilian-perpetrated violence against marginalized minority groups motivates prosocial attitudes toward victimized groups. Although the mass public may not sympathize with marginalized groups, they may adopt prosocial attitudes toward marginalized groups subject to civilian-perpetrated violence if the violence is salient and perceptibly illegitimate. However, the adoption of prosocial attitudes may be fickle. We find evidence consistent with the model. Studies 1–3 show that high-profile violence against LGBTQ+ people increases support for LGBTQ+ rights and reduces negative attitudes toward LGBTQ+ group members. But, the adoption of prosocial attitudes is short-term. Study 4 shows that less salient violence against LGBTQ+ people may not engender prosocial attitudes at the outset. Our findings suggest that violent events must be sufficiently salient to initially motivate prosocial beliefs. Nevertheless, salient civilian-perpetrated violence against marginalized groups may not sustainably motivate prosocial beliefs toward targeted groups.

Information

Type
Research Article
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of American Political Science Association
Figure 0

Figure 1. Stylized Expectations Concerning the Effect of Violence against Marginalized Groups on Prosocial AttitudesNote: Horizontal lines denote prosocial attitudes toward marginalized groups (y-axis) over time (x-axis). The dotted line characterizes violence against a marginalized group.

Figure 1

Figure 2. Fickle Prosocial Violence Response Model

Figure 2

Figure 3. SSM Support Increases Post-PulseNote: Panel (a) displays respondent covariate balance pre- and post-Pulse. Panel (b) characterizes the post-Pulse effect on SSM support with and without covariate adjustment. Panel (c) displays falsification tests characterizing the unadjusted post-Pulse effect on LGBTQ+-irrelevant outcomes. Black coefficients are statistically significant, and gray otherwise. Estimates use population weights. All covariates scaled between 0 and 1. 95% percent CIs displayed from HC2 robust SEs. See DSM Tables 91–93 for regression tables characterizing the coefficients.

Figure 3

Figure 4. The Influence of Pulse on SSM Support Attenuates over TimeNote: The x-axis is days cut from moment of Pulse massacre after the massacre (with days after intact). The y-axis is the post-Pulse coefficient. 95% CIs from robust SEs. See DSM Table 94 for regression table characterizing reported coefficients in this figure. See DSM Section 2.10.5 for control covariate coefficients.

Figure 4

Figure 5. Influence of post-Pulse on Anti-Gay AttitudesNote: The x-axis is the sample bandwidth. The y-axis is the post-Pulse coefficient. All covariates rescaled between 0 and 1. 95% CIs displayed from robust SEs. See DSM Sections 3.5.2 and 3.5.3 for corresponding regression tables.

Figure 5

Figure 6. The Influence of Post-Pulse on Reducing Anti-Gay Attitudes Attenuates over TimeNote: The x-axis is days cut from moment of Pulse massacre after the massacre (with 15 days after kept intact). The y-axis is the post-Pulse coefficient. All estimates from models adjusting for controls. 95% CIs from robust SEs. See DSM Tables 123–130 for tables characterizing the displayed coefficients. See DSM Sections 3.5.8 and 3.5.9 for control coefficients.

Figure 6

Figure 7. Media Coverage of Gay-Related Content in 1998Note: Panels (a,b) display the number of NYT/Washington Post gay-related articles (y-axis) by month (x-axis). Panel (c) displays the number of gay-related articles related to Shepard or anti-gay violence by month. Dashed vertical line denotes the period Shepard is murdered.

Figure 7

Figure 8. Respondents Interviewed Post-Shepard Were Less Likely to Believe Homosexuality Is Morally WrongNote: Panel (a) characterizes balance between respondents interviewed pre- and post-Shepard’s murder. Black coefficients are significant, gray not. Panel (b) characterizes (1) the influence of being interviewed on June 1998 relative to April 1997 on the belief homosexuality is morally wrong (temporal placebo) and (2) the influence of being interviewed post-Shepard on moral wrong. Panel (c) characterizes falsification tests assessing the influence of post-Shepard on non-LGBTQ+ group attitudes. 95% CIs displayed from robust SEs. See DSM Tables 176–179 for regression tables on balance tests, the temporal placebo, the post-Shepard coefficient estimates, and falsification tests.

Figure 8

Figure 9. Belief in Moral Wrong Is Stable between 1978 and 2004 with the Exception of the Moment Shepard Was MurderedNote: Reference study is the 1994 CNN poll. Color denotes the inclusion/exclusion of controls (age, education, gender, partisanship, and race). Shaded estimate denotes Shepard’s murder (October 1998). All estimates use survey weights. All covariates scaled between 0 and 1. See DSM Table 183 for a corresponding regression table. 95% CIs displayed derived from robust SEs.

Figure 9

Figure 10. Influence of Post-Club Q on Anti-Gay, Anti-Trans AttitudesNote: The x-axis is the bandwidth sample (1–40 days). The y-axis is the post-Club Q coefficient. Panels characterize different outcomes. The top/bottom three panels characterize estimates from the 2022 PI S-IAT/T-IAT data. Black coefficients are from models adjusting for controls, gray otherwise. 95% CIs displayed from HC2 robust SEs. See DSM Tables 185 and 186 for regression tables characterizing these estimates.

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