In this research, we test the central hypothesis that perceptions of Asian Americans as a high-status “model minority” lead to overestimates of the extent of wealth equality between Asian and White Americans. We test this hypothesis across three studies that manipulate the salience of high- or low-status Asian American exemplars before soliciting estimates of Asian-White wealth equality. A meta-analysis of the results revealed that participants significantly overestimated Asian-White wealth equality and that making low- versus high-status Asian American exemplars salient decreased this tendency. These data suggest that activation of high-status Asian American exemplars elicits greater overestimates of Asian-White wealth equality, obscuring existing wealth disparities relative to White Americans and significantly downplaying the economic inequality that burdens a subset of Asian Americans from less prototypical ethnic backgrounds. The findings echo recent calls by sociologists and political scientists for a more nuanced understanding of the diversity and economic inequality among Asian American communities.
America's racial sands are quickly shifting, with parallel growth in theories to explain how varied groups respond, politically, to demographic changes. This Element develops a unified framework to predict when, why, and how racial groups react defensively toward others. America's racial groups can be arrayed along two dimensions: how American and how superior are they considered? This Element claims that location along these axes motivates political reactions to outgroups. Using original survey data and experiments, this Element reveals the acute sensitivity that people of color have to their social station and how it animates political responses to racial diversity.
In this research, we test the central hypothesis that perceptions of Asian Americans as a high-status “model minority” lead to overestimates of the extent of wealth equality between Asian and White Americans. We test this hypothesis across three studies that manipulate the salience of high- or low-status Asian American exemplars before soliciting estimates of Asian-White wealth equality. A meta-analysis of the results revealed that participants significantly overestimated Asian-White wealth equality, and that making low- versus high-status Asian American exemplars salient decreased this tendency. These data suggest that activation of high-status Asian American exemplars elicits greater overestimates of Asian-White wealth equality, obscuring existing wealth disparities relative to White Americans and significantly downplaying the economic inequality that burdens a subset of Asian Americans from less-prototypical ethnic backgrounds. The findings echo recent calls by sociologists and political scientists for a more nuanced understanding of the diversity and economic inequality among Asian American communities.
What politicizes White identity? We consider here a racialized partisan hypothesis. Although Whites numerically prevail within each party, the variance around this central tendency varies sharply between them: Republicans are tightly organized around Whites, yet Democrats are structured around Whites who share membership with people of color. This configuration puts White Democrats in a more precarious position and can sometimes motivate them to jockey for intraparty prominence. We support this claim with survey and experimental evidence. First, we show that White identity is more strongly associated with opposition to immigration among White Democrats than White Republicans (n = 6,126). This pattern is absent on a placebo (opposition to federal spending on science). Second, we demonstrate, experimentally, that White identity (but not partisan identity) mediates the impact of racial threat on racially coded policies among White Democrats (n = 400). This pattern does not emerge among White Republicans (n = 400) and is absent on another placebo (support for infrastructure spending).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.