This article explores the effect of explicitly racial and inflammatory speech by political elites on mass citizens in a societal context where equality norms are widespread and generally heeded yet a subset of citizens nonetheless possesses deeply ingrained racial prejudices. The authors argue that such speech should have an ‘emboldening effect’ among the prejudiced, particularly where it is not clearly and strongly condemned by other elite political actors. To test this argument, the study focuses on the case of the Trump campaign for president in the United States, and utilizes a survey experiment embedded within an online panel study. The results demonstrate that in the absence of prejudiced elite speech, prejudiced citizens constrain the expression of their prejudice. However, in the presence of prejudiced elite speech – particularly when it is tacitly condoned by other elites – the study finds that the prejudiced are emboldened to both express and act upon their prejudices.

The scholarly literature is observing a slow but steady growth in research exploring the effects of subnational economic inequality on political attitudes and behavior. Germane to this work is the assumption that citizens are aware of the level of inequality in their local residential context. At present, however, the evidence in support of this assumption is mixed. This article attempts to offer the literature improved tests of citizens’ awareness of local inequality by addressing a key limitation in past work—the discordance between the geographic unit underlying measures of the independent and dependent variables. Analyzing two national surveys employing a measure of perceived inequality scaled to the local level, the results suggest that citizens are indeed aware of the level of income inequality in their local environment and that the link between objective and perceived local inequality is most pronounced among lower income citizens.

Social media data can provide new insights into political phenomena, but users do not always represent people, posts and accounts are not typically linked to demographic variables for use as statistical controls or in subgroup comparisons, and activities on social media can be difficult to interpret. For data scientists, adding demographic variables and comparisons to closed-ended survey responses have the potential to improve interpretations of inferences drawn from social media—for example, through comparisons of online expressions and survey responses, and by assessing associations with offline outcomes like voting. For survey methodologists, adding social media data to surveys allows for rich behavioral measurements, including comparisons of public expressions with attitudes elicited in a structured survey. Here, we evaluate two popular forms of linkages—administrative and survey—focusing on two questions: How does the method of creating a sample of Twitter users affect its behavioral and demographic profile? What are the relative advantages of each of these methods? Our analyses illustrate where and to what extent the sample based on administrative data diverges in demographic and partisan composition from surveyed Twitter users who report being registered to vote. Despite demographic differences, each linkage method results in behaviorally similar samples, especially in activity levels; however, conventionally sized surveys are likely to lack the statistical power to study subgroups and heterogeneity (e.g., comparing conversations of Democrats and Republicans) within even highly salient political topics. We conclude by developing general recommendations for researchers looking to study social media by linking accounts with external benchmark data sources.

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nities for people of color (Richeson 2015). More recent examples inlcude opposing police brutality and supporting the Black Lives Matter movement (Arora, Stout, and Kretschmer 2020). As Jennifer Richeson (2015) succinctly puts it, "it is when groups come together that real change becomes possible."Although a significant number of studies have considered the prospects for coalition building between African Americans and Latinos (Jones-Correa 2011; Kaufman 2003; Mc-Unpacking Identity: Opportunities and Constraints for Cross-Racial Collaboration m a neesh a ror a, sa r a sa dh Wa ni, a nd sono sh a hWe argue that two factors are important for cross-racial coalition building: policy convergence in key issue arenas and perceived interest alignment with other racial groups. Drawing on the 2016 National Asian American Survey, we examine two of the most salient issues Asian Americans consistently rate as among the most important: immigration and economic policy. Using principal component analysis, we plot mean scores by group to analyze national-origin clustering along these two dimensions. Next, we analyze national-origin differences in perceived interest alignment with Blacks and Latinos. Combining these two factors, we identify clusters of groups that have a strong potential for cross-racial coalition building and that face greater constraints. In sum, we propose a theoretical framework for understanding cross-racial coalition building that includes disaggregating Asian Americans by national origin, and then identify which national-origin groups have the greater opportunity to form such coalitions.