Mapping the Content of Asian Stereotypes in the United States: Intersections with Ethnicity,Gender,Income,and Birthplace

Abstract

How are people of Asian origin perceived in contemporary U.S. culture? While often depicted as a “model minority”—competent and hardworking but also quiet, unsociable, or cold—little work measures whether and how these stereotypes vary for Asians in different social locations. We use a large (n ≈ 4,700) quota sample of the United States, matched to key U.S. demographics, to map the content of Asian stereotypes across ethnicity, gender, income, and birthplace. We find that some stereotypes are largely consistent across subgroups—such as the perception that Asians lack sociability, but not warmth, relative to White Americans—while others vary substantially. Perceptions of dominance vary by income, while perceptions of competence are moderated by income and ethnicity in complex ways. Stereotypes have important consequences, ranging from everyday frustrations to depressive symptoms and employment discrimination. Our work provides a detailed picture of how stereotypes vary across social locations.

Keywords

group processes intergroup relations prejudice and discrimination race/ethnicity stereotypes

Asians are the fastest growing racial group in the United States, and the proportion of Asian immigrants has surpassed that of Hispanic immigrants since 2009 (Budiman et al. 2020). Partially because of Asians’ increased presence in the United States, researchers have called for more work on the complexity of the Asian experience (Lee and Kye 2016). Stereotypes are an important dimension of this experience. Asian-origin individuals’ experiences with stereotyping are associated with outcomes ranging from the everyday frustrations of being depersonalized (Siy and Cheryan 2013) to depressive symptoms (Wong et al. 2012) and hiring discrimination (Kang et al. 2016; Oreopoulos 2011). Responding to this call, we ask how people of Asian origin—across diverse social locations—are perceived in contemporary U.S. culture.

Cultural beliefs and stereotypes of Asians center around two linked ideas: a “model minority” stereotype that depicts Asians as competent, hardworking, and high-achieving and a “socially deficient” stereotype that depicts Asians as quiet and unsociable at best and cold and untrustworthy at worst (Chou and Feagin 2015; Fiske et al. 2002; Kim 2007; Lee 2006; Lee and Kye 2016; Li 2014).¹ As a result of these stereotypes, Asians are viewed as compliant, hardworking immigrants but also as an economic threat to other racial and ethnic ingroups (Cheryan and Monin 2005; Fiske et al. 2002; Ho and Jackson 2001; Kim 1999, 2007; Le Espiritu 2008; Ng, Lee, and Pak 2007; Wu 2016; Xu and Lee 2013).

While some dimensions of Asian stereotypes are well understood, important questions remain. Asian-origin individuals belong to diverse ethnic groups with different languages, cultures, and immigration histories (Kao 1995; Zeng and Xie 2004). These groups exhibit economic heterogeneity, earning relatively high wages on average (Kim and Mar 2007; Kim and Sakamoto 2010; Zeng and Xie 2004) but also exhibiting relatively high poverty rates (Budiman and Ruiz 2021). These economic differences extend to health, tracking differences in life expectancy (Baluran 2019).

Research on stereotypes of Asian-origin groups often does not reflect this diversity (Le Espiritu 2008). Many studies measure stereotypes of “Asians” as a unitary category (e.g., Berdahl and Min 2012; Fiske et al. 2002; Lai and Babcock 2013; Lin et al. 2005) without examining whether these stereotypes vary across identities. To address this, scholars emphasize an intersectional perspective (Lee 2006; Li 2014; Liu and Wong 2018), and some work examines intersections of Asian stereotypes with another dimension of inequality (Ghavami and Peplau 2013; Niemann et al. 1994). Yet, perhaps not surprisingly given the complexities of conducting intersectional research (McCall 2005), little work measures Asian stereotypes across multiple intersecting categories.

We use a relatively large (N > 4,700) quota sample that matches the demographics of the United States on gender, age, region, ethnicity, education, and income to systematically map Asian stereotypes in the United States by key intersecting identities: ethnicity, gender, income, and foreign- versus U.S.-born status.² We measure stereotypes of individuals of Chinese, Filipino, Indian, Korean, and Vietnamese descent. These are the five largest Asian-origin groups in the United States, comprising over 80% of all Asians in the United States, with distinct immigration histories from East, South, and Southeast Asia (Budiman and Ruiz 2021).

Specifically, we map Asian stereotypes on key dimensions of evaluation, including competence, warmth, sociability, and dominance, across ethnicity, gender, income, and place of birth (foreign vs. United States). We compare stereotypes across Asian individuals of East, South, and Southeast descent to perceptions of U.S.-born White Americans, the numerically and culturally dominant racial group in the United States (Blumer 1958; Chou and Feagin 2015). In making this comparison, we address debates around whether individuals of different Asian ethnicities are perceived as “honorary Whites” or if they face disadvantages relative to White Americans (Bonilla-Silva 2004; Lee and Kye 2016; Tinkler et al. 2019; Tuan 1998).

Theory

The Stereotype Content Model

Studies of Asian stereotypes stem from diverse theoretical and methodological perspectives. As an organizing framework, we draw on the stereotype content model (SCM; Fiske et al. 2002), which arranges stereotypes along two dimensions: warmth and competence.³ According to the SCM, structural relationships between groups shape perceptions of warmth and competence. Warmth derives from the perceived level of competition between the outgroup and the ingroup; outgroups that are perceived to be in competition with the ingroup are viewed as cold (low warmth). Competence derives from the stereotyped group's economic and cultural success; outgroups perceived as successful are viewed as competent (Fiske et al. 2002; Rudman and Glick 2001).

Stereotypes of Asian-Origin Individuals in the United States

Contemporary Asian stereotypes emphasize economic achievement and competition with other racial and ethnic groups, corresponding to “competent but cold” stereotype content under the SCM (Cheryan and Monin 2005; Fiske et al. 2002; Ho and Jackson 2001; Le Espiritu 2008; Ng et al. 2007). This model minority stereotype emerged from complex historical struggles. Efforts to shift the image of Asians in the United States from a “yellow peril” to a “model minority” emerged in WWII, driven by the perception that U.S. interests were harmed by the hypocrisy of promoting democracy abroad and racial oppression at home (Lee 2010; Wu 2016). This model minority imagery was later deployed to attack civil rights claims and foster anti-Black racism by suggesting that racial inequality stemmed from individual rather than structural factors (Wu 2016). The model minority rhetoric downplays Asian experiences with racism (Li 2014) and fuels perceptions of Asians as hyper-focused on education and achievement (Ng et al. 2007).

Themes of achievement and threat have appeared in studies of stereotype content since at least the 1930s (Katz and Braly 1933). Asian-origin individuals are perceived as intelligent and hardworking but soft-spoken and shy (Ghavami and Peplau 2013; Niemann et al. 1994), self-disciplined but not sociable (Jackson et al. 1996), “too intelligent” and “too successful” (Ho and Jackson 2001), and “industrious, smart, and successful” (Lee 2015:141). This high-achieving image elicits perceptions of competition for academic honors, housing, and economic and political resources (Bobo and Hutchings 1996; Guthrie and Hutchinson 1995; Kim 2007; Lee 2015; Ng et al. 2007). These perceptions of threat are associated with negative attitudes and emotions toward Asians (Butz and Yogeeswaran 2011; Maddux et al. 2008).

Extensions of the Stereotype Content Model to Asian Stereotypes

The SCM identifies perceptions of competence and warmth as core dimensions of stereotypes. Researchers focusing on stereotypes of Asian-origin individuals have extended the SCM to include two additional traits, sociability and dominance. While the original SCM conceives of warmth in terms of positive or negative intentions (e.g., “trustworthy,”“good natured,”“tolerant”; Fiske et al. 2002), an extension conceives of warmth as sociability (e.g., time spent socializing, prioritizing one's social life, wanting to be the center of attention; Lin et al. 2005), finding that a lack of sociability is a key dimension of Asian stereotypes. While warmth and sociability are sometimes discussed interchangeably in stereotype content research, they are conceptually distinct: warmth is centered on intentions, and sociability focuses on being outgoing and socially skilled. Stereotypes based on these two dimensions may coincide for some groups (e.g., women are perceived to be warmer and more sociable than men). Nonetheless, they are distinct concepts; individuals can be perceived as good-natured but lacking interpersonal skills (“nice but quiet”). The SCM has also been extended to include dominance (Berdahl and Min 2012). Perceptions of dominance, such as assertiveness and forcefulness, are shaped by cultural images of East Asians as obedient or submissive (Berdahl and Min 2012).

Drawing on the SCM, we consider four dimensions of stereotype content: competence, warmth, sociability, and dominance. We explore whether these perceptions vary across intersecting identities: gender, race/ethnicity, income, and foreign- versus U.S.-born status.

Intersectionality

Asian-origin individuals are a diverse group, yet stereotyping research rarely examines this diversity (Le Espiritu 2008). Past work tends to focus on “Asian” as a social category (Fiske et al. 2002; Jackson et al. 1996; Lin et al. 2005) or examine individual subgroups (Lee 2015:201; Liu and Wong 2018). Because stereotypes occur at the intersections of disadvantage, collapsing across categories provides a limited picture (Crenshaw 1989:140).

Yet the fact that stereotypes can operate in intersectional ways does not mean that stereotypes at all intersections differ (Timberlake and Estes 2007). Stereotypes can homogenize, as when diverse Asian ethnic groups are assumed to be Chinese (Guthrie and Hutchinson 1995). Such perceptions of homogeneity may prevent distinct stereotypes from developing. A dearth of intersectional research makes it difficult to assess when stereotypes will differentiate versus homogenize. To evaluate these issues, we examine the content of Asian stereotypes across their intersection with ethnicity, focusing on the five largest Asian ethnic groups in the United States (Chinese, Filipino, Indian, Korean, and Vietnamese) as well as gender, income, and birthplace (U.S.- or foreign-born).

Ethnicity

Past work measuring stereotypes focuses on “Asian” (Fiske et al. 2002; Ho and Jackson 2001; Jackson et al. 1996; Lin et al. 2005) or East Asian categories (Berdahl and Min 2012; Liu and Wong 2018). Yet Asian ethnic groups vary in patterns of selection into the United States, driven by immigration and labor laws and by economic, colonial, and military relationships between sending countries and the United States (Le Espiritu 1996, 2008; Ngo and Lee 2007). Furthermore, perceptions of these groups are socially constructed by a range of actors, including politicians, media, and Asian American activists (Blumer 1958; Lee 2010; Ngo and Lee 2007; Wu 2016).

These differences in structural conditions, experiences, and popular narratives inform patterns of stereotyping. While early waves of East Asian immigrants were primarily laborers, more skilled workers immigrated from East Asia and India following the passage of the Immigration and Nationality Act in 1965 (Le Espiritu 2008). These patterns are reflected in income, with Asian Indian households earning the highest incomes, followed in descending order by households of Filipino, Chinese, Korean, and Vietnamese origin (Budiman and Ruiz 2021). Similarly, three quarters of Indian-origin individuals hold a college degree (75 percent), followed by more than half of Korean- and Chinese-origin (57 percent) individuals. In contrast, 48 percent of Filipino-origin individuals and about a third (32 percent) of Vietnamese-origin individuals possess a college degree (Budiman and Ruiz 2021).

These patterns reflect, in part, historical circumstances for individuals of Southeast Asian origin. The Philippines's history as a former U.S. colony created a unique set of immigration trajectories and experiences. Before 1965, Filipino immigrants were either laborers or U.S. Navy enlistees (Le Espiritu 1996). In the post-1965 era, Filipinos were more often in professional occupations, particularly women in nursing, although some educated men had difficulty finding skilled work (Le Espiritu 2008). Broadly speaking, U.S. colonial history in the Philippines is associated with greater English proficiency and less residential segregation compared to other groups (Ocampo 2014). Many Vietnamese immigrants came as refugees after the end of the Vietnam War in 1975 in waves that varied in socioeconomic status (Ngo and Lee 2007). Their arrival was followed by stories in media outlets promoting a “model minority” image of Vietnamese families (Ngo and Lee 2007).

Groups’ structural positions influence stereotypes (Fiske et al. 2002) because observers infer causal relationships between social positions and traits (Ridgeway 1991) and socially construct stereotypes (Blumer 1958). For example, one study found that Chinese American high school students experienced a model minority stereotype, whereas Filipino American students were perceived as gang members (Teranishi 2002). Vietnamese youth experience both “valedictorian” and “delinquent” stereotypes (Ngo and Lee 2007). An SCM study of immigrant stereotypes finds that East Asian, Chinese, Korean, and Japanese immigrants cluster into a high-competence, low-warmth category; Indian immigrants cluster into a high-competence, moderate-warmth category; and Vietnamese immigrants cluster into a moderate-competence, low-warmth cluster (Filipino immigrants were not included; Lee and Fiske 2006).

These stereotypes may be driven by perceived socioeconomic status. We hold income constant by experimental design, allowing us to examine whether these stereotypes are driven by income versus ethnic stereotypes per se. In addition, differences may be muted by homogenizing stereotypes that treat Asians as identical (Guthrie and Hutchinson 1995; Le Espiritu 2008).

Gender

While much work measuring Asian stereotypes does not distinguish between men and women, research that does make this distinction finds differences and similarities in the stereotypes they experience (Timberlake and Estes 2007). Compared to non-Asian men, Asian men are often perceived as sexually unattractive and feminine (Chou 2012; Liu and Wong 2018). In contrast, Asian women tend to be stereotyped in terms of either “exotic sexuality” or “docile femininity” (Li 2014). Despite these differences, Asian men and women are both stereotyped as intelligent, hardworking, soft-spoken, and shy, whereas stereotypes of White men and women diverge more substantially (Ghavami and Peplau 2013; Niemann et al. 1994). Because Asian men are often stereotyped as feminine, people may perceive fewer differences in Asian men and women compared to some other racial groups (Johnson, Freeman, and Pauker 2012).

Income

Income shapes stereotypes (Johannesen-Schmidt and Eagly 2002). Low-income groups are perceived to be less competent and warm than higher-income groups (Fiske et al. 2002), and individuals from high-income backgrounds are viewed as better fits for elite jobs (Rivera 2012). Although Asians are economically diverse, little work compares stereotypes of high- and low-income Whites and Asians. Wage disparities between Asian and non-Hispanic White individuals have narrowed substantially, and some studies report no wage differential between U.S.-born Asian men and non-Hispanic White men among highly educated professionals (e.g., Kim and Mar 2007; Kim and Sakamoto 2010; Zeng and Xie 2004). The poverty rate varies by ethnicity, with comparatively lower poverty rates for Asian Indians and Filipinos relative to Chinese-, Vietnamese-, and Korean-origin individuals (Budiman and Ruiz 2021). This economic variation may explain some of the variation in stereotypes that have previously been attributed to ethnicity. Experimentally controlling for income allows us to assess the relative contributions of income versus ethnicity in shaping stereotypes.

Birthplace

We examine stereotypes of U.S.-born and immigrant Asians relative to U.S.-born White Americans. Past research has used the SCM to examine stereotypes of Asian immigrants (Lee and Fiske 2006, described earlier) but has not compared these stereotypes to those of U.S.-born Asian Americans. Understanding the relationship between birthplace and racial stereotypes of Asians is important, in part because more than 70 percent of the Asian population in the United States is foreign-born (Pew Research Center 2012). In comparison, less than 8 percent of the White population in the United States is foreign-born (U.S. Census Bureau 2010).⁴

Stereotypes of immigrants may vary with the structural positions of immigrant groups (Fiske 2018). Alternatively, research on the “perpetual foreigner” stereotype argues that many Americans do not distinguish between foreign-born and U.S.-born Asians, often assuming U.S.-born Asians are foreign-born (Li 2014; Ng et al. 2007). Compared to White and Black Americans, Asian Americans are viewed as more likely to be foreign-born (Cheryan and Monin 2005) and less patriotic (Zou and Cheryan 2017).

Methods

Design

Drawing on the SCM, we test the hypotheses that compared to U.S.-born White Americans, U.S.- and foreign-born Asian-origin individuals will be seen as at least equal in competence (Hypothesis 1), less warm (Hypothesis 2), less sociable (Hypothesis 3), and less dominant (Hypothesis 4). Scholars have encouraged caution in the choice of reference categories to avoid reifying particular (usually dominant) groups as the standard (Johfre and Freese 2021). Our comparison of Asians and Asian Americans to White Americans is driven by our theoretical interest in whether Asian-origin individuals experience race-based disparities. Because White Americans are the numerically and culturally dominant group in the United States, using White Americans as the reference category allows us to speak to debates about whether Asian-origin individuals have reached societal and labor market parity with White Americans (Chou and Feagin 2015; Hewlett et al. 2011; Hurh and Kim 1989; Kim and Mar 2007; Takei and Sakamoto 2008; Woo 2000; Xie and Goyette 2000). In our figures, we depict means and confidence intervals by condition, which does not treat any category as the default.

We do not disaggregate these hypotheses by all intersections. In many cases, theory and evidence is ambiguous on the conditions under which stereotypes have distinguishing versus homogenizing effects. Instead, we take a descriptive approach and focus on mapping intersections of stereotypes by ethnicity, gender, income, and U.S.- versus foreign-born status.

To do this, we conducted a survey experiment drawn from a relatively large (analytical sample N = 4,766) quota sample that matched U.S. demographics on gender, race/ethnicity, age, education level, income, and region. The survey experiment randomly assigned participants to evaluate one group in a multifactor study: 5 ethnicity (Chinese, Filipino, Indian, Korean, or Vietnamese) × 2 gender (men or women) × 2 income (high or low) × 2 birthplace (U.S. born or foreign born) + 4 (high- or low-income White men or women). For each ethnicity, we compare high- and low-income men and women who are U.S. or foreign born to high- and low-income U.S.-born White men and women.

Respondents who had missing data on demographic variables (race/ethnicity, age, gender, nativity, education, region of the United States, number of past studies, or type of community [city, suburbs]), less than 2 percent of the data, were removed from the sample. In the main text, we present data for those who passed all of the manipulation checks (≈70 percent of respondents—a typical failure rate of these kinds of studies; Downs et al. 2010; Weinberg, Freese, and McElhattan 2014). The results are similar when including all respondents (including those who failed manipulation checks, as shown in Appendix A). After removing participants who had missing data and/or failed manipulation checks, there were between 82 and 140 respondents per condition.⁵

Participants

Participants were recruited from a Qualtrics online panel between January 31 and March 26, 2018. Participants were invited to participate in a 7- to 10-minute study, described as a survey of how society perceives different individuals. Participants were compensated with approximately $4 in “e-rewards” for participation (the standard Qualtrics compensation, which can be redeemed for gift cards, airline miles, or other rewards). Using stratified sampling techniques, the demographics of the Qualtrics sample reflect the current makeup of the U.S. population on dimensions of gender, race/ethnicity, age, education level, income, and region (see Table 1).

Table 1.

Descriptive Statistics (N = 4,766)

	Mean/Proportion	SD
Gender
Man	.47
Woman	.53
Race
Asian	.03
Black	.09
Native/American Indian	.01
White	.80
Other or multiracial	.06
Latinx/Hispanic	.12
Born in United States?	.93
Respondent age	48.24	17.03
Education
High school diploma or less	.36
Some university or associate’s	.32
Bachelor’s	.20
Graduate education (MA/MD/JD/PhD)	.12
Household income
< $25,000	.16
$25,000–$50,000	.23
$50,000–$75,000	.20
$75,000–$100,000	.15
$100,000–$150,000	.15
$150,000–$200,000	.06
≥$200,000	.05
Region of the United States
Northeast	.21
Midwest	.21
West	.30
South	.28
Community/locale type
City/metro	.21
Suburban	.54
Rural	.25
Number of past surveys
None	.54
1–10	.34
>10	.12

Note: Some respondents had missing data; these individuals were included in calculation of means but removed for any regression-based analyses. This includes 20 people missing on household income and 63 people missing on age. Because there are so few nonbinary people in the sample (n = 27), we omitted them in regression analyses.

Procedure

Upon completing the informed consent, participants were randomly assigned to one of 44 possible conditions in a between-subjects design. To avoid potential social desirability bias, in which respondents provide responses to depict themselves in a positive light, we asked how “most people” view a certain demographic group. This method, known as “most people projective questioning,” is widely used in research on stereotypes (e.g., Fiske et al. 2002) and proves effective for measuring third-order inferences (i.e., what individuals believe most people think). Stereotypes that are third-order inferences, even if not shared by the person reporting them, have been found to effectively predict individuals’ behavior (Carlsson, Daruvala, and Jaldell 2010; Cho and Knowles 2013; Correll et al. 2017; Epley and Dunning 2000; Fisher 1993).

Specifically, respondents were asked:

In this study, we are interested in learning more about how different groups of people are viewed in American society. We are not interested in your personal beliefs, but in how you think members of these groups are viewed by others. Please respond by saying how you think members of the following group are viewed by others:

Men who:

• Were born in the United States to Chinese parents

• Have an income of about $95,000 per year

• Live and work in the United States

Indicate how much you think others would agree that the following statements apply to members of this group.

Bold text was edited by experimental condition (see next section). Upon evaluating the randomly assigned demographic group, participants were asked an attention check question, several open-ended questions not analyzed here, and manipulation check questions to ascertain whether they could identify the race/ethnicity, income, gender, and U.S./foreign-born status of the group they evaluated. Participants then received payment.

Independent Variables

Our key independent variables include five categories of ethnicity (Chinese, Filipino, Indian, Korean, and Vietnamese) crossed with birthplace (U.S.- or foreign-born). We included one additional condition for U.S.-born White Americans. Ethnicity was manipulated by stating the race/ethnicity of the demographic group member's parents. Birthplace was manipulated by stating where the demographic group member was born. These 11 categories (5 [ethnic groups] × 2 [U.S.- vs. foreign-born]) + [U.S.-Born White Americans]) were fully crossed with the remaining variables: gender (men/women) and income ($15,000/$95,000).

Dependent Variables

Our key dependent variables included scales for competence (five items), warmth (4 items, both competence and warmth scales from Fiske et al. 2002), sociability (12 items, Lin et al. 2005), and dominance (7 items, Berdahl and Min 2012; Wiggins, Trapnell, and Phillips 1988). We adopt the wording used by Fiske et al. (2002) for their competence and warmth measures: “As viewed by society, how [item] are members of this group?” We adapt the sociability and dominance items to use this wording. All items used 6-point Likert-type scales, anchored on not at all = 1 and extremely = 6. For all scale items and measures of fit, see Table 2.⁶

Table 2.

Dependent Scale Measures

Latent measure	Cronbach’s α	Mean	SD	Range	Items	Total items	Source
Competence	.85	4.08	1.03	1–6	Competent, confident, independent, competitive, intelligent Example: As viewed by society, how competent are members of this group?	5	Fiske et al. (2002)
Warmth	.84	3.73	.99	1–6	Tolerant, warm, good-natured, sincere Example: As viewed by society, how tolerant are members of this group?	4	Fiske et al. (2002)
Sociability	.91	3.43	.90	1–6	Commit less time to socializing than others, dislike being the center of attention at gatherings, do not put high priority on their social lives, are not very vocal, do not interact smoothly in social situations, do not spend a lot of time at social gatherings, are not as social as other groups of people, rarely initiate social events or gatherings, tend to be shy and quiet, have less fun compared to other social groups, do not function well in social situations, and do not know how to have fun and relax Example: As viewed by society, members of this group commit less time to socializing than others do.	12	Lin et al. (2005)
Dominance	.89	3.56	.99	1–6	Self-assured, assertive, persistent, firm, dominant, forceful, and domineering Example: As viewed by society, how self-assured are members of this group?	7	Berdahl and Min (2012); Wiggins, Trapnell, and Phillips (1988)

Note: All scales were anchored on not at all (1) and extremely (6). The sociability items are reverse-coded so that greater values indicate greater sociability.

To examine if the items for each scale appeared to measure one latent variable, we used exploratory factor analysis. In an unrotated exploratory factor analysis, the items for each scale all load onto a single factor (with an eigenvalue of >1). To retain the natural units, scales were generated by taking the means of the items. As long as participants responded to one of the scale items, they were included in the final sample.⁷ Tests of measurement invariance⁸ suggest the scales operate similarly across respondent race and ethnicity (Collins and Lanza 2009).

Analytic Strategy

In the following, we summarize findings for four dependent variables (competence, warmth, sociability, and dominance) across 44 conditions. Mapping stereotypes by multiple intersecting categories creates data reduction challenges (McCall 2005). To present the results succinctly, for most analyses we present figures rather than tables.⁹

We first present overall patterns for competence, warmth, sociability, and dominance using an ordinary least squares (OLS) regression in which all Asian American and Asian ethnicities are combined and compared to White, U.S.-born Americans. In each of the four models, one for each dependent variable, we regress the dependent variable on indicator variables for each experimental condition: U.S.-born Asian American, foreign-born Asian (reference category: U.S.-born White Americans), gender (reference category: men), and high income (reference category: low income). To assess whether the effects of race and birthplace vary by gender, we include two interaction effects in each model: the U.S.-Born Asian American × Gender interaction and the Foreign-Born Asian × Gender interaction. To assess whether the effects of race and birthplace differ by income, we include two further interaction effects in each model: the U.S.-Born Asian American × Income interaction and the Foreign-Born Asian × Income interaction.¹⁰ Because the goal of the regression analysis is to summarize the effect of Asian/Asian American status, we do not examine all possible interactions (e.g., Income × Gender interaction). In the subgroup analyses, however, we present the means by all conditions, equivalent to a fully interactive model.

We then present subgroup analyses for each dependent variable within ethnic groups. To generate estimates and tests of significance, we use OLS regression. Each regression was estimated separately within gender and income category and compares White men and women (omitted category) to men and women of each Asian ethnicity (Chinese, Korean, Indian, Vietnamese, Filipino), both U.S.- and foreign-born. To account for nonnormality, we use bootstrapped standard errors (with 400 repetitions; Efron 1979, 1985). We illustrate these differences with figures that plot the predicted means and 95 percent confidence intervals for each dependent variable.¹¹

The SCM conceptualizes group stereotypes as distributed across a two-dimensional space defined by warmth and competence (Fiske et al. 2002; Lee and Fiske 2006). We adopt this approach to illustrate how ethnic groups are evaluated relative to one another. We present two alternate visualizations that map all groups onto a two-dimensional space defined by competence and sociability (Version 1) or competence and warmth (Version 2).¹²

Results

Competence

Combined Asian ethnicities analysis

We predicted that overall, Asian men and women would be viewed as being at least as competent as White men and women (Table 3, Column 1). On average, low-income Asian and Asian American men are viewed as more competent than low-income White American men (p < .001, see Table 3, Rows 1 and 2), by about a half point on a 6-point scale. We find no evidence that perceived levels of competence differ by gender: neither the main effect of gender (Row 3) nor the ethnicity-gender interaction effects (Rows 5 and 6) are significant. High-income White individuals are viewed as more competent than low-income White individuals (p < .001, Row 4), by 1.46 scale points. There is a significant and negative interaction between income and race/place of birth (bs = –.69, –.64; Rows 7 and 8) that is large enough to entirely offset the main effects of Asian and Asian American (bs = .49, .54).

Table 3.

Ordinary Least Square Regression Models for the Effects of Asian and Asian American Status (Pooled across Ethnic Groups), Gender, and Income, on Perceived Competence, Warmth, Sociability, and Dominance

	(1)	(2)	(3)	(4)
	Competence	Warmth	Sociability	Dominance
Ethnicity (reference: White, U.S.-born)
Asian	.49***	.21*	−.63***	−.12
	(.09)	(.09)	(.08)	(.09)
Asian American	.54***	.32***	−.41***	−.16
	(.09)	(.09)	(.08)	(.08)
Woman (reference: man)	.01	.24**	.12	−.27**
	(.09)	(.09)	(.08)	(.09)
High income ($95,000; reference: low income, $15,000)	1.46***	−.08	.39***	1.13***
High income ($95,000; reference: low income, $15,000)	(.09)	(.09)	(.08)	(.09)
Interactions
Asian ×	−.14	−.14	−.04	.11
Woman	(.10)	(.10)	(.09)	(.10)
Asian American ×	−.07	−.10	−.11	.20*
Woman	(.10)	(.10)	(.09)	(.09)
Asian ×	−.69***	.10	−.29**	−.55***
High income ($95,000)	(.10)	(.10)	(.09)	(.10)
Asian American ×	−.64***	.06	−.33***	−.53***
High inc ($95,000)	(.10)	(.10)	(.09)	(.09)
Constant	3.21***	3.39***	3.82***	3.37***
	(.08)	(.08)	(.07)	(.08)
Observations	4,766	4,766	4,766	4,766

Note: Standard errors are in parentheses. X indicates an interaction effect.

p < .05. **p < .01. ***p < .001.

Subgroup analysis

Low income

For low-income groups, we find support for Hypothesis 1.¹³ As presented in Figure 1, many low-income Asian and Asian American men (Chinese, Chinese American, Korean, Korean American, Indian, Indian American, and Vietnamese American) are viewed as more competent than low-income White men. The exceptions are Filipino, Filipino American, and Vietnamese men, who did not statistically significantly differ from low-income White men in perceived competence. Similarly, except Filipino, Filipino American, and Indian women, low-income Asian and Asian American women are viewed as significantly more competent than low-income White women. As predicted, low-income Asian-origin individuals are perceived as either significantly more competent or not significantly different from low-income White individuals.

High income

For high-income groups, we find support for Hypothesis 1, with some exceptions (Figure 1). Most high-income Asian men, including Chinese, Chinese American, Korean, Korean American, Indian, Indian American, and Vietnamese American men, do not significantly differ in perceived competence compared to high-income White men. Similarly, most high-income Asian women, including Chinese, Chinese American, Korean American, Indian, Indian American, and Vietnamese American women, do not significantly differ in perceived competence compared to high-income White women. Contrary to predictions, high-income Filipino, Filipino American, and Vietnamese men are viewed as significantly less competent than high-income White men. High-income Filipino, Filipino American, Korean, and Vietnamese women are viewed as significantly less competent than high-income White women. Overall, high-income White individuals are never viewed as significantly less competent than high-income Asian-origin individuals but are viewed as significantly more competent than some groups.

Warmth

Combined Asian ethnicities analysis

We predicted that Asian-origin individuals would be viewed as less warm than their White counterparts (Hypothesis 2; Table 3, Column 2). In contrast, low-income Asian (p < .05) and Asian American (p < .001) men are viewed as significantly warmer than low-income White men, by about .2 to .3 scale points (Table 3, Rows 1 and 2). As might be expected (Eckes 2002; Fiske et al. 2002; Ghavami and Peplau 2013), low-income White women are viewed as warmer than low income White men by a similar margin (p < .01, Table 3, Row 3). There are no significant differences in perceived warmth by income. Gender and income did not significantly moderate perceptions of warmth by ethnicity.

Subgroup analysis

Low income

For low-income groups, we do not find support for Hypothesis 2 (Figure 2). Low-income Vietnamese American men are evaluated as significantly warmer than low-income White men. Other low-income Asian-origin men do not statistically significantly differ from low-income White men, although they are rated as warmer substantively. Korean American and Filipina women are evaluated as significantly warmer than low-income White women, and all other low-income Asian-origin women are rated as similarly warm as low-income White women.

High income

For high-income groups, we do not find support for Hypothesis 2 (Figure 2). High-income Chinese American, Filipino, Filipino American, Korean, Korean American, and Vietnamese American men are viewed as significantly warmer than high-income White men, while other high-income Asian-origin men do not significantly differ. High-income Vietnamese American, Filipino, and Filipino American women are viewed as significantly warmer than high-income White women, while other high-income Asian-origin women do not significantly differ.

Sociability

Combined Asian ethnicities analysis

Consistent with Hypothesis 3, we find sociability penalties for Asian and Asian American individuals (Table 3, Column 3). On average, low-income Asian and Asian American men are viewed as less sociable than low-income White men (p < .001, Table 3, Rows 1 and 2), a difference of roughly .4 to .6 scale points for low-income groups. High-income White individuals are viewed as significantly more sociable than low-income White individuals (p < .001, Table 3, Row 4). There are no statistically significant differences for gender. There is an interaction between income and ethnicity such that the sociability advantage of White individuals is greater at higher levels of income (p < .01/p < .001, Table 3, Rows 7 and 8).

Subgroup analysis

Low Income

For low-income groups, we find that all Asian ethnic groups are viewed as significantly less sociable than their White counterparts, except for Filipino Americans (who are rated nonsignificantly less sociable). These patterns are similar for men and women (Figure 3).

High income

For high-income groups, we find that for men, nearly all Asian ethnic groups are viewed as significantly less sociable than their White counterparts. The exception is again Filipino American men, who are rated lower, but the difference is nonsignificant. For high-income women, all Asian ethnic groups are viewed as significantly less sociable than their White counterparts (Figure 3).

Dominance

Combined Asian ethnicities analysis

We predicted that Asian individuals would be viewed as less dominant than their White counterparts (Table 3, Column 4). Contrary to predictions, ethnicity does not predict dominance for low-income men (p = n.s., Table 3, Rows 1 and 2). Instead, income and ethnicity interact such that White men are perceived as more dominant than Asian and Asian American men at higher incomes (p < .01/p < .001, Table 3, Rows 7 and 8). On average, White women are viewed as about .27 scale points less dominant than White men (p < .01, Table 3, Row 3). There is a positive and significant Asian American × Woman interaction (p < .05, Table 3, Row 6), suggesting that the effect of gender on dominance perceptions is smaller for Asian American women. The Asian × Woman interaction is not statistically significant. High-income White individuals are viewed as about 1.13 scale points more dominant than low-income White individuals (p < .001, Table 3, Row 4).

Subgroup analysis

Low income

Among low-income groups, there are few ethnic differences. The only significant difference is that low-income Filipino men are viewed as less dominant than low-income White men. There are no statistically significant differences among low-income women (Figure 4).

High income

Among high-income groups, all high-income Asian men are viewed as less dominant than White men. Similarly, all high-income Asian women are viewed as less dominant than high-income White women (Figure 4).

Alternate Visualization: Competence × Sociability/Warmth

To illustrate how each group is evaluated in relation to the others, we present scatterplots mapping each group onto axes for competence and sociability (Figure 5a) or warmth (Figure 5b), borrowing from SCM research (Fiske et al. 2002; Lee and Fiske 2006). These two graphs, one using warmth and one using sociability, illustrate differences in these two ways of conceptualizing the interpersonal dimension of stereotypes.

Figure 5a shows the relationship between competence and sociability by gender, place of birth, ethnicity, and income. High-income White men and White women are the only group evaluated as high in competence and sociability. This upper right quadrant typically contains groups that are admired in society (Fiske et al. 2002). Most high-income Asian and Asian American groups cluster in the bottom right quadrant, indicating high perceived competence and (compared to White Americans) lower perceived sociability. This lower right quadrant typically contains groups that are envied in society (Fiske et al. 2002).¹⁴ High-income Filipino-origin individuals fall roughly between high-income White Americans and other Asian-ethnic groups.

Figure 5a illustrates the importance of income in shaping stereotype content, with every low-income group rated lower in competence than every high-income group (small and large shapes, respectively). Low-income Asian-origin individuals are perceived as moderately competent but low in sociability, while low-income White Americans are viewed as moderately sociable but low in competence. Ratings of Filipino-origin individuals fall between White Americans and other Asian ethnic groups.

These differences are compressed when looking at the intentions-based measure of warmth (Figure 5b). As discussed, White Americans are perceived as more sociable—but not warmer—than Asians and Asian Americans. Our findings suggest the two measures capture different concepts.

Discussion

We examine Asian stereotypes in the United States across ethnicity, gender, income, and birthplace. We map these categories onto fundamental bases of social categorization, including competence, warmth, sociability, and dominance. Our results support past findings of a model minority stereotype while uncovering substantial variation.

Perceptions of competence varied across ethnic and income groups. Low-income East and South Asians were viewed as more competent than low-income White Americans, perhaps reflecting a model minority stereotype and/or a negative stereotype of poor White individuals (Kunstman, Plant, and Deska 2016). In contrast, high-income White Americans were viewed as similarly or more competent than Asian individuals. There were intraethnic differences, with individuals of Southeast Asian origin perceived to be less competent relative to White Americans.

Differences in perceptions of warmth by race were often small, but where they existed, they tended to favor Asians. This may come as a surprise given past descriptions of “socially deficient” Asian stereotypes (Fiske et al. 2002). We speculate that when comparing Asian and White Americans with little distinguishing information, people implicitly compare prototypical high-income Asian Americans with middle-class White Americans. With more information—comparing high-income White and Asian Americans—these differences may shrink or reverse.

Most Asian groups were perceived as significantly less sociable than White Americans, except U.S.-born Filipino/a Americans. This may reflect Filipino-origin individuals’ heritage of colonization, which is associated with greater English proficiency and less residential segregation than other Asian groups (Ocampo 2014) but also stereotypes of lower academic ability (Teranishi 2002). Furthermore, it may reflect the racial incongruity that Filipinos experience. While most Filipinos classify themselves as Asian, they are less likely to be viewed as Asian by White Americans relative to East Asians (Lee and Ramakrishnan 2020).

The perception of Asians as lacking in sociability but not warmth reflects how stereotypes have changed over time. Earlier “yellow peril” stereotypes portrayed Asians as overtly hostile and untrustworthy (Le Espiritu 2008). With the development of the model minority stereotype, the cultural image of Asians in the United States has shifted to one that is docile, polite, and focused on educational and economic achievement (Nguyen, Carter, and Carter 2019).

Income moderated perceptions of dominance such that high- but not low-income White Americans were viewed as more dominant than their Asian-origin counterparts. This is consistent with stereotypes of Asians as deferential (Berdahl and Min 2012) and lacking leadership ability (Chavez 2021; Chin 2020). The similar perceived dominance for low-income groups could stem from an expectation for lower-income people to be deferential (Calarco 2014).

Broader Implications

Scholars emphasize the importance of understanding not just the outcomes experienced by Asian-origin individuals in the United States but also the processes that produce those outcomes (Drouhot and Garip 2021; Lee and Kye 2016). Stereotypes are cognitive constructs that shape perceptions, emotions, and behavior, and as a result, they are an important input into these processes (Berdahl and Min 2012; Greenwald and Krieger 2006; Jackson et al. 1996; Kang et al. 2016). By mapping these stereotypes, our work has implications for intergroup relations, stratification, and the theoretical understanding of intersectional stereotypes.

Scholars have debated whether Asians are now perceived as “honorary Whites” (Bonilla-Silva 2004; Lee and Kye 2016; Tinkler et al. 2019; Tuan 1998). Our work paints a complex picture. While Asians are sometimes evaluated similarly to White Americans, these perceptions often vary by ethnicity and income. Mapping this patchwork identifies the social locations in which Asian-origin individuals may experience discrimination. For example, our results suggest that past findings that Asian American professionals are perceived to lack social and leadership skills may be shaped more by stereotypes of sociability and dominance than warmth (Chavez 2021; Chin 2020). Furthermore, our work highlights social class as a main effect and a moderator of racial and ethnic stereotypes.

Limitations and Future Directions

Our work has several limitations and areas for extension. First, we use a quota sample matched to the U.S. population on key demographics rather than a true probability sample. Survey experiments conducted on nonprobability samples often yield similar results to those conducted on probability samples (Weinberg et al. 2014) and lab experiments (Mullinix et al. 2015). Samples from the Qualtrics platform perform close to the General Social Survey and better than MTurk samples (Zack, Kennedy, and Long 2019).

Second, as a map of stereotypes—a cognitive construct—our work cannot speak to the extent to which these stereotypes translate into behavior or to the factors that give rise to them. While many studies have examined how Asians experience discrimination—a behavioral consequence of stereotyping—future work can examine how discriminatory behavior varies at the intersection of different stereotyped categories. In addition, our work does not measure how these stereotypes arise or factors such as interpersonal contact or media consumption that may shape stereotypes.

Third, partially due to concerns about social desirability bias, we measure third-order—rather than first-order—inferences. Third-order inferences are an individual's perception of what most people think. Because societal-level stereotypes affect individuals’ attitudes and behavior (Correll et al. 2017), measures of third-order inference are important; they should not, however, be interpreted as representing individuals’ beliefs.

Fourth, while we examine key aspects of interpersonal and intergroup evaluation, we do not examine all of them. Some—such as perceived foreignness—are well documented. Others, such as interpersonal trust, are less so (Chin 2020). This work could also be expanded to include additional ethnicities, incomes, skin tone, or other independent variables. Key among these is education, given perceptions of Asian-origin individuals as a model minority competing for academic honors and credentials (Lee 2006, 2015). Manipulating education introduces some complexities because the returns to education differ for Asian-origin individuals educated in the United States versus abroad (Zeng and Xie 2004).

Conclusion

Our work maps Asian-origin stereotypes across a diverse range of social categories. This work is important because stereotyping is costly. The subjective experience of discrimination is associated with outcomes such as self-reported health, registering to vote, and perceiving commonality with other racial and ethnic groups (Drouhot and Garip 2021; Huang 2021). Being positively stereotyped is also an aversive experience because it involves being seen in terms of one's racial category rather than as an individual (Siy and Cheryan 2013). Our work speaks to the diversity of the Asian experience in the United States and has implications for a range of outcomes shaping the well-being of Asian-origin individuals in the United States.

Supplemental Material

sj-docx-1-spq-10.1177_01902725221126188 – Supplemental material for Mapping the Content of Asian Stereotypes in the United States: Intersections with Ethnicity, Gender, Income, and Birthplace

Supplemental material, sj-docx-1-spq-10.1177_01902725221126188 for Mapping the Content of Asian Stereotypes in the United States: Intersections with Ethnicity, Gender, Income, and Birthplace by Stephen Benard, Bianca Manago, Anna Acosta Russian and Youngjoo Cha in Social Psychology Quarterly

Supplemental Material

sj-pptx-2-spq-10.1177_01902725221126188 – Supplemental material for Mapping the Content of Asian Stereotypes in the United States: Intersections with Ethnicity, Gender, Income, and Birthplace

Supplemental material, sj-pptx-2-spq-10.1177_01902725221126188 for Mapping the Content of Asian Stereotypes in the United States: Intersections with Ethnicity, Gender, Income, and Birthplace by Stephen Benard, Bianca Manago, Anna Acosta Russian and Youngjoo Cha in Social Psychology Quarterly

Footnotes

Acknowledgements

The authors thank Muna Adem, Jennifer C. Lee, Shelley Rao, and members of the Indiana University Sociology Lab for helpful discussion and feedback.

1

We do not use the term “Asian-American” to refer to foreign-born individuals of Asian descent to avoid imposing an identity that they have not necessarily have adopted. Instead, we use the terms “Asian-origin individuals,”“U.S.- and foreign-born Asians,” or “Asian American” to refer to U.S.-born individuals of Asian origin.

2

While quota samples are not equivalent to probability samples (Smith and Dawber 2019), our sample may better reflect the diversity of American perceptions of Asian-origin individuals compared to past work on undergraduate samples (Butz and Yogeeswaran 2011; Ghavami and Peplau 2013; Ho and Jackson 2001; Maddux et al. 2008) or specific communities (Bobo and Hutchings 1996; Guthrie and Hutchinson 1995; Lee 2006; ).

3

Stereotypes of Asian Americans can be organized in other ways, most often with racial triangulation models (Kim 1999; Xu and Lee 2013; ). Like the stereotype content model, these approaches include a superior/inferior dimension analogous to perceived competence but substitute a perceived foreignness or insider/outsider dimension for warmth. Our approach does not conflict with these models but examines a partially overlapping set of stereotypes.

4

We do not include a condition for the 8 percent of the White population in the United States that is foreign-born. These stereotypes appear to differ by country of origin; immigrants from Eastern Europe, Russia, the Middle East, and several other countries are viewed as less competent than and equally warm as Asian, Chinese, and Japanese immigrants. Irish and Italian immigrants are viewed as less competent but warmer ().

5

For details on exclusion of respondents, see Appendix A, available in the online appendix. For details on missing data, see , available in the online appendix. The sample provided by Qualtrics was restricted to survey recruits who met several criteria, including being at least 18 years old, a U.S. citizen, agreeing to read carefully, and passing an attention check. Qualtrics also screened out respondents who straightlined or nearly straightlined the entire survey (e.g, responding to every item with a “5”) or completed the survey in less than one-third the median time. The attention check question read, “For the purpose of our study, it is important that we know participants are paying close attention to our questions. To make sure you're paying attention, please select Completely Agree,” followed by a set six responses ranging from completely agree to completely disagree.

6

For all four scales (competence, warmth, sociability, and dominance), we adopt the wording used by : “As viewed by society, how [item] are members of this group?” The warmth and competence scales items are identical to those used in Fiske et al. (2002). The sociability items are taken from Lin et al.’s (2005) “(un)sociability” scale and reverse-coded so that greater values indicated greater sociability. We omitted one item (“street smart”) from the original scale that we felt captured a different concept. For dominance, we used Wiggins, Trapnell, and Phillips's (1988)“assured-dominance” scale. On this scale, we excluded one item (“confidence”) because it is already included in the competence scale. The confidence measure was a worse fit with the other dominance measures. Specifically, confidence is the only item that loaded on the main dominance factor below .6 and had an interitem correlation below .7. The scales we adapt are all Likert-style and varied in whether they originally used five, six, or eight response categories; we used six response categories across all scales. We use identical anchors as Fiske et al. (2002): not at all = 1 and extremely = 6. We also measured additional items that are either exploratory or address different questions, intended for future work.

7

For more details on missing data, see , available online.

8

Reported in , available online.

9

Tables with means and standard errors are presented in Appendix C, available online. Because we make multiple comparisons for each mean, we use p = .01 as a more conservative cutoff for subscripting differences in the .

10

To illustrate, the regression equation for competence is: Ycompetence = 3.21 + .49 × Asian + .54 × AsianAmerican + .01 × Woman + 1.46 × HighIncome +–.14 × Asian × Woman +–.07 × AsianAmerican × Woman +–.69 × Asian × HighIncome +–.64 × AsianAmerican × HighIncome. The direct effect of the Asian variable indicates the difference in predicted means when comparing low-income White and Asian men.

11

The means and bootstrapped standard errors are available in (online).

12

For graphs of dominance and sociability or dominance and warmth, see , available online.

13

For subgroup analyses, see full tables in , available online.

14

In conceptualization, groups low in both warmth and competence are scorned, and those high in warmth and low in competence are pitied.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We thank the National Science Foundation (Award ID No. 1658168), the Center for Research on Race and Ethnicity in Society, and the Office of the Vice Provost for Research at Indiana University for financial support of this research and the Institute for Advanced Study at Indiana University for a residential fellowship to the first author. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or other sponsors.

ORCID iDs

Stephen Benard

Bianca Manago

Supplemental Material

Supplemental material for this article is available online. Data, code, and survey instruments are available at .

Bios

Stephen Benard is an associate professor in the Department of Sociology at Indiana University. His research focuses on intergroup relations, particularly issues around status, conflict, identity, and inequality. His current projects include National Science Foundation-funded studies of how intergroup revenge and forgiveness shape social status in groups and the content of Asian stereotypes in the United States and their implications for inequality.

Bianca Manago is an assistant professor in the Department of Sociology at Vanderbilt University. Her research examines topics related to social psychology, inequality, and research methods. Her current projects examine how labels affect stigma and status processes and best practices for data preparation.

Anna Acosta Russian is a PhD candidate in the Department of Sociology at Indiana University, Bloomington. Her research is at the intersection of critical inquiry into gender, race, and social interaction. She uses a social psychological lens to examine the interplay between cultural ideologies, social interaction, and identity construction.

Youngjoo Cha is an associate professor of sociology at Indiana University, Bloomington and Yonsei University, Seoul, South Korea. Her research interests are in gender, work, family, occupations, and organizations. Her current research investigates how the trend toward long work hours reinforces gender inequality and what organizational and institutional conditions help to change this trend. Her other research focuses on the effect of marriage and parenthood on the gender pay gap and the role of Asian stereotypes on the labor market outcomes of Asian women and men in the United States.

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