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Abstract

Aggregate figures unequivocally depict an increase in anti-Asian sentiment in the United States and other Western countries since the start of the COVID-19 pandemic, but there is limited understanding of the contexts under which Asians encounter discrimination. The authors examine how coethnic concentration shapes Asians’ experiences of discrimination across U.S. counties during COVID-19 and also assess whether county-level context (e.g., COVID-19 infection rates, unemployment rates) could help explain this relationship. The authors analyze the Understanding Coronavirus in America tracking survey, a nationally representative panel of American households, along with county-level contextual data. The authors find an n-shaped relationship between coethnic concentration and Asians’ perceived discrimination. This relationship is explained largely by county-level COVID-19 infection rates. Together, the context of medium Asian concentration and high COVID-19 cases created a particularly hostile environment for Asians during COVID-19.

Keywords

discrimination anti-Asian sentiment COVID-19 Asian Americans coethnic concentration

Since the start of the coronavirus disease 2019 (COVID-19) pandemic in early 2020, there has been wide reporting of growing anti-Asian attacks across the United States and other Western countries (Tessler, Choi, and Kao 2020). From 2019 to 2020, anti-Asian hate crimes increased by 150 percent in major U.S. cities (Center for the Study of Hate and Extremism 2021). Since March 2020, Stop AAPI Hate has lodged more than 3,700 self-reports of anti-Asian incidents, with California, New York, and large cities reporting the highest number of incidents (Jeung et al. 2021). Reported incidents are not limited to big cities but are also common in small towns nationwide, which has only deepened the sense of fear among the Asian population (Jeung et al. 2021). Although reports of hate incidents capture only a fraction of the violence and harassment that Asians in the United States have endured, these aggregate figures unequivocally depict an increase in the overall scale of anti-Asian sentiment. Nonetheless, aggregate figures alone are limited for understanding the contexts under which Asians encounter discrimination. For instance, hate incidents are likely underreported (Zhang, Zhang, and Benton 2022) and not captured per capita, which may lead to a misleading portrayal that anti-Asian sentiment is concentrated in cities or states with large Asian populations. To understand where Asians experience greater discrimination, in this study we examine the contextual factors associated with perceived discrimination among Asians in the United States since the start of COVID-19. We explore whether and how perceived discrimination differs by local context, specifically by Asian concentration across United States counties.

We focus on Asians for several reasons. First, relative to Blacks and Hispanics, Asians have been understudied in the literature on coethnic concentration and perceived discrimination (Camacho, Allen, and Quinn 2019; Hunt et al. 2007). Second, when Asians have been a focus, the findings are mixed. For instance, Walton (2012) found that discrimination was lower in Asian neighborhoods, but Morey et al. (2020) found that it varied by nativity and length of residence. Third, since the onset of COVID-19, Asians have experienced heightened levels of discrimination and worsened mental health (Liu et al. 2020; Wu, Qian, and Wilkes 2021). Taken together, exploring how contextual factors shape anti-Asian racism not only enriches existing literature on coethnic concentration and perceived discrimination but is also crucial for aiding postpandemic recovery among Asian communities and broader racial relations in the United States.

U.S. Counties and Anti-Asian Sentiment during COVID-19

A common approach to assessing coethnic concentration is to measure coethnic populations in residential areas such as neighborhoods and metropolitan areas (Bygren and Szulkin 2010; Conger, Schwartz, and Stiefel 2011; Lee 2016). The assumption is that frequent encounters or interactions among coethnics or people of different racial/ethnic backgrounds are a primary mechanism shaping prejudice or discrimination (Pettigrew 1998). We follow a similar logic. To capture this context, we focus on whether the percentage of Asians, COVID-19 infection rate, and unemployment rate in U.S. counties affect perceived discrimination among Asians.

U.S. counties have been fundamental in shaping COVID-19 responses and individuals’ overall experiences during the pandemic. To illustrate, counties have been primarily responsible for implementing (or not) stay-at-home orders, mask mandates, and other public health measures, and regulating economic activities and essential workplaces (e.g., business and school closures). However, counties vary widely in the timing and content of public health orders and regulations (Lyu and Wehby 2020). For instance, in March 2020, Los Angeles County in California promulgated nine public health orders, whereas Orange County, a neighboring county, implemented only four (Goldhaber-Fiebert et al. 2021). Additionally, there is widespread county-level variation in the severity of COVID-19 infections and economic fallout (Aaronson and Alba 2020; Paul, Englert, and Varga 2021).

County-level differences in COVID-19 severity and pandemic policy responses, in turn, have implications for county-level differences in anti-Asian sentiment. Emergency declarations not only shape individual behaviors under COVID-19 (Weill et al. 2020) but may also influence individuals’ attitudes and prejudices, especially if individuals perceive that unwanted restrictions and their associated economic fallout stem from the responsibility of a racial/ethnic group. In light of the “Chinese virus” rhetoric surrounding the pandemic (Darling-Hammond et al. 2020), people living in counties with a high number of COVID-19 cases or high unemployment may inaccurately blame China, individuals of Chinese descent, or Asian Americans in general. This suggests that Asians living in U.S. counties with higher levels of COVID-19 infections and unemployment may experience greater hostility and discrimination.

The main goal of this study is to examine how coethnic concentration shapes Asians’ experiences of discrimination across U.S. counties during COVID-19. We also assess whether county-level context (e.g., COVID-19 infection rates and unemployment rates) could help explain this relationship (see Figure 1 as a conceptual diagram of this study). Although the relationship between coethnic concentration and perceived discrimination is not new (Morey et al. 2020), we explore how the relationship is sensitive to contextual factors, such as widespread public health crises and sudden changes in unemployment.

Figure 1.

Conceptual diagram.

Asian Americans and Residential Patterns

Anti-Asian discrimination has a long history in the United States, although the stereotypes and perceptions of Asian Americans have changed drastically over time. During the nineteenth century, the perception of Asian Americans as unassimilable foreigners was explicitly linked to stereotypes about disease, poor health, threats to safety and health, and untrustworthiness (Lee 2007). In the twenty-first century, prior to the pandemic, Asian Americans were often viewed problematically as a “model minority” because of their relatively high levels of socioeconomic success (Lee and Zhou 2015). The hyperselectivity of many Asian immigrant groups and the overall socioeconomic mobility of Asian Americans contribute to the perception of Asians as exceptionally competent, yet cold and calculating (Lee 2021; Lee and Fiske 2006). Despite Asian Americans’ successful integration into mainstream institutions in the contemporary era, they continue to be marginalized (Kibria 2000). For instance, Asians are often portrayed as unassimilable foreigners because of their physical and cultural differences from White Americans or individuals of European descent (Eichelberger 2007; Lee 2007; Lee and Kye 2016). The pandemic quickly reignited old tropes of Asians as foreign agents who carry disease (Tessler et al. 2020). Since the pandemic, there has been a sharp increase in the perception of Asians as more foreign and less American (Darling-Hammond et al. 2020). Although COVID-19 increased hostility toward Asian Americans, there were rising levels, albeit small, of anti-Asian hate crimes before the pandemic, suggesting some tensions that preceded the pandemic (Zhang et al. 2022).

Asians represent the fastest growing racial group in the United States (Budiman and Ruiz 2021a). In 2000, Asians represented less than 4 percent of the total population compared with approximately 6 percent in 2019. In part, the growth of the Asian population is driven by continuous migration from Asian countries with foreign-born persons comprising approximately 60 percent of the Asian population (Budiman and Ruiz 2021b). Although a large proportion of new Asian immigrants settle in immigrant gateways, such as California, New York, and Texas, there is also movement to other states that are emerging immigrant-receiving states, such as Georgia and Arizona (Portes and Rumbaut 2014).

On the whole, Asians in the United States show higher levels of socioeconomic status than the overall U.S. population, although there is wide variation across Asian subgroups (Budiman and Ruiz 2021b). Relative to the U.S. population, Asians show higher median household incomes ($85,000 vs. $61,800), lower poverty rates (10 percent vs. 13 percent), and a higher proportion of college education or more (54 percent vs. 33 percent). Given the link between socioeconomic status and residential mobility (Charles 2008; Massey and Denton 1985), Asians have been able to move in to more racially mixed residential areas that are comparable with middle-class White areas (Charles 2003; Lee and Kye 2016). In 2010, half of Asians lived in global residential areas where all four racial groups (Whites, Blacks, Hispanics, and Asians) were well represented (Logan and Zhang 2011). Although Asians show moderate levels of segregation from Whites, they still exhibit lower levels of residential segregation than both Hispanics and Blacks (Logan 2013; Logan and Zhang 2011). Nonetheless, Asian population growth, especially resulting from immigration, increases the likelihood that Asians will live with other coethnics (Iceland, Weinberg, and Hughes 2014). This is particularly the case for some of the largest Asian groups, such as Chinese, Koreans, and Indians (Iceland et al. 2014).

Coethnic concentration in residential areas may reflect large shares of first- or second-generation Asians with limited human capital and English proficiency (Logan 2013). Additionally, many Asian immigrant groups, especially Chinese, prefer to move directly into residential areas with more coethnics (Li 1998; Wen, Lauderdale, and Kandula 2009; Zhou 2009). Suburban coethnic residential areas, specifically, have flourished over the past quarter century and remain a viable alternative to majority White residences (Hall 2013; Lee and Kye 2016). In turn, if coethnic residential areas are preferred by some immigrant groups, this preference may inform immigrants’ decision making about which counties or metropolitan areas to reside.

We acknowledge that Asians are a diverse group with origins in more than 40 countries as well as different linguistic backgrounds, migration reasons, premigration human capital, and political affiliation (Morey et al. 2020; Tran et al. 2018). Among the Asian population in 2019, Chinese represent the largest group, constituting about 23 percent of the Asian population, followed by Asian Indians (20 percent) and Filipinos (18 percent) (Budiman and Ruiz 2021b). Despite the heterogeneity and diversity within the larger Asian panethnicity, Asians report a shared experience of racial bias and discrimination (Gee and Ponce 2010).

Coethnic Concentration and Perceived Discrimination

The relationship between coethnic concentration and discrimination is widely debated, specifically the direction, shape, and mechanisms linking the two. We review three distinct perspectives offering different hypothesized relationships between coethnic concentration and perceived discrimination: ethnic enclave perspective, group conflict perspective, and contested boundaries perspective. Using the three theoretical perspectives, we derive hypotheses about the direction and shape of the relationship between Asian concentration and perceived discrimination during COVID-19.

Ethnic Enclave Perspective

The ethnic enclave perspective posits a linear, negative relationship between coethnic concentration and discrimination. According to this perspective, living with coethnics provides a protective effect and thus, is associated with lower levels of discrimination (Camacho et al. 2019; Hunt et al. 2007). This perspective focuses on concentrations of individuals who share the same national origin or racial and ethnic background (Lim et al. 2017; Pong and Hao 2007). Coethnic concentration is often measured using the percentage or number of coethnics residing in a geographical area, such as census tracts or metropolitan areas (Bygren and Szulkin 2010; Conger et al. 2011; Lee 2016). Upon arrival, new immigrants typically live in areas with many coethnics, which were traditionally considered initial settlements that serve immigrants’ immediate needs (Logan, Zhang, and Alba 2002; Massey and Denton 1985). Areas with large coethnic concentrations provide immigrants with linguistic and cultural familiarity, as well as opportunities for accruing social and economic capital (Zhou 2009). Additionally, coethnic concentration may help shield immigrants from discrimination in the primary labor market as a result of the host population’s prejudice or immigrants’ poor language skills (Boyd 1996; Wilson 2003). Thus, from the ethnic enclave perspective, residential areas with higher levels of coethnic concentration are supportive environments that can lower stress and discrimination experienced by minority residents, especially immigrants (Gee 2002; Morey et al. 2020; Mossakowski and Zhang 2014).

Nonetheless, it is unclear how widely the ethnic enclave perspective applies to various racial/ethnic groups and contexts. The ethnic enclave perspective initially focused on immigrants and shows the greatest support among immigrants but receives mixed evidence among nonimmigrants (Gee 2002; Morey et al. 2020; Vega et al. 2011). Additionally, it is unclear whether the effect of coethnic concentration is sensitive to social or economic conditions. For instance, during widespread economic hardship, such as recessions, social and institutional trust may change and individuals may rely more heavily on coethnic networks (Ervasti, Kouvo, and Venetoklis 2019; Zhu, Liu, and Painter 2013). We assess whether coethnic concentration protects Asians against discrimination during times of crises. In short, the ethnic enclave perspective posits that in areas with greater coethnic concentration, Asians will report lower perceived discrimination.

Group Conflict Perspective

The group conflict perspective posits a linear, positive relationship between minority concentration and discrimination because greater minority concentration creates conflict and competition (real or perceived) across groups (Abascal and Baldassarri 2015; Blalock 1957; Blumer 1958; Legewie and Schaeffer 2016). Conflict may arise from competition over scarce resources, economic interests, and access to nonmaterial issues, such as political representation or way of life (Bobo and Hutchings 1996; Legewie and Schaeffer 2016). Additionally, the size of the outgroup is an important mechanism shaping the ingroup’s sense of threat (Blalock 1957).

When the group conflict perspective is applied to residential contexts, the findings indicate that Whites feel a greater sense of threat and become more racially hostile with increasing minority concentrations (Quillian 1996; Rosenstein 2008; Taylor 1998; Taylor and Mateyka 2011). For instance, Whites show lower levels of trust and are more likely to relocate with increasing shares of minorities in their neighborhoods or surrounding areas (Crowder and South 2008; Hou and Wu 2009). Additionally, increasing Black residential concentration is associated with Whites’ greater racial prejudice and opposition to affirmative action policies (Quillian 1996; Taylor 1998; Taylor and Mateyka 2011). In addition to minority group size, economic and political conditions shape racial attitudes (Oliver and Mendelberg 2000). In particular, racial animosity or conflict is heightened during economic stress or macroeconomic contraction (Olzak 1992). Thus, from the group conflict perspective, hostile racial attitudes are motivated by threat, increasing minority population size, and deteriorating economic conditions (Bobo and Zubrinsky 1996; Rosenstein 2008).

Much of the attention, though, focuses on Whites’ prejudicial attitudes or behaviors toward Blacks or toward minorities more generally (Crowder and South 2008; Hou and Wu 2009). Yet the relationship between minority residential concentration and threat differs depending on the minority group, given that Whites express different racial attitudes for each minority group (Blalock 1957; Taylor 1998). For instance, Link and Oldendick (1996) found that Whites showed greatest hostility toward Blacks, followed by Latinos and Asians. Additionally, Taylor (1998) found that greater concentrations of Asians and Latinos in the metropolitan area had no effect on Whites’ race-related attitudes. Nevertheless, the perception of Asians as a threat may be heightened during the COVID-19 pandemic when the economy has suffered and racial attitudes toward Asians have been particularly negative (Dhanani and Franz 2020), a possibility we explore in the current study. In short, from the group conflict perspective, Asians living in areas with higher coethnic concentration will report higher levels of discrimination.

Contested Boundaries Perspective

Third, the contested boundaries perspective posits an n-shaped or curvilinear relationship between coethnic concentration and perceived discrimination. From this perspective, discrimination is greatest in areas where there is a moderate coethnic concentration (Legewie and Schaeffer 2016). Although a range of residential areas exist, the contested boundaries perspective highlights three types with particular significance: areas with low coethnic concentration, moderate coethnic concentration, and high coethnic concentration. Whereas boundaries between groups are clearly demarcated in areas with low and high coethnic concentrations, boundaries are blurred or even contested in areas with a moderate minority concentration, leading to greater conflict between groups (Desmond and Valdez 2012; Grimshaw 1960; Legewie and Schaeffer 2016). There is reduced social cohesion among residents in these areas because of increased conflict and fighting over boundaries. Where there is ambiguity about group rank, the perceived threat from outgroup members may increase (Legewie 2018).

One instance of this is predominantly White residential areas with moderate minority populations (Crowder 2000; Legewie and Schaeffer 2016). In contrast to residential areas that have small or large minority populations, residential areas with moderate minority populations may instead increase Whites’ perceived threat. One reason why a small or large minority area versus a moderate minority area may have different effects on perceived discrimination is because of “tipping points” or Whites’ thresholds toward minority neighbors (Schelling 1971). Whereas most Whites tolerate small concentrations of minority neighbors, minority concentrations beyond the tipping threshold may increase Whites’ perceived threat (Clark 1991). Therefore, from this perspective, Asians living in areas with moderate coethnic concentrations will report higher levels of discrimination than those living in areas with low or high coethnic concentrations.

Methods

Data

Our study relies on data from four sources. The main source is the Understanding Coronavirus in America tracking survey, conducted by the University of Southern California’s Center for Economic and Social Research. Respondents of the survey are members of the Understanding America Study (UAS), which is a nationally representative Internet panel of American households including approximately 8,500 U.S. adults 18 years and older. The 1st wave was fielded from March 20 to April 1, 2020, and subsequent longitudinal waves were repeated every two weeks. At the time of this study, the 29th wave, conducted from June 9 to July 21, 2021, was the most recent data. Questionnaires, codebooks, and a majority of the data are publicly available through the UAS Web site (https://uasdata.usc.edu/index.php).

Although the Understanding Coronavirus in America tracking survey is publicly available, we analyze a nonpublic version that links survey respondents with the characteristics of their counties of residence (e.g., percentage of Asians, COVID-19 case rate, unemployment rate). Currently, nonpublic UAS data are accessible per approval and include blinded location indicators when linking county-level data with the survey data from respondents. In other words, even in nonpublic UAS data, actual location codes (e.g., ZIP codes and county codes) are not made available to researchers. Although it would be ideal to have detailed and time-varying contextual information about the counties where respondents live, we do not have this information in our nonpublic version of UAS data. Despite this limitation, the UAS provides the most comprehensive data for examining Asians’ geographic concentration and perceived discrimination during COVID-19.

This study focuses on how Asians perceive discrimination during the COVID-19 pandemic. We restrict our sample to respondents who identify as Asian, including single-race and mixed-race individuals. We exclude data from waves 7 and 9 because perceived discrimination was not surveyed. After listwise deleting observations with missing values for the variables used, we obtain a total of 10,766 person-wave observations across 27 waves. A total of 569 Asians participated, but the exact number of Asians varied from wave to wave.

We attach five county-level indicators from external sources to survey respondents in the UAS. We include county-level percentage of Asians, social inequality (Gini coefficient), and median household income. These three indicators were obtained from the Geography of Social Capital in America Project of the U.S. Congress Joint Economic Committee (2018) and captured before the pandemic, from 2012 to 2018, which are currently the most up-to-date data available for these indicators. We also include cumulative COVID-19 cases per 1,000 population for each county, as of July 25, 2021, obtained from Johns Hopkins University’s COVID-19 Data Repository (Dong, Du, and Gardner 2020). Our last county-level indicator is 2020 unemployment rates from the Bureau of Labor Statistics (2021).

Measures

Our dependent variable is perceived discrimination, which we derive from four questions in the Understanding Coronavirus in America tracking survey. Respondents were asked whether, during the past two weeks, they were treated with less courtesy and respect, received poorer service, were threatened or harassed, and were the subject of other people’s fear (Williams et al. 1997). All four questions were answered on a 3-point scale (0 = no, 1 = unsure, 2 = yes). We combine the four items to create a scale ranging from 0 to 8, with higher scores indicating higher levels of perceived discrimination (Wu et al. 2021). We examine perceived discrimination by Asians because it captures everyday forms of discrimination and affects individual well-being, stress, and mental health (Small and Pager 2020; Wu et al. 2021).

Our main predictor is coethnic (Asian) concentration at the county level, as measured by the percentage of Asians in each county. The percentage ranges from 0.1 percent to 41.9 percent. We notice that counties in California and Hawaii have the highest levels of Asian concentration, with many containing more than 30 percent of Asians (see Figure A1 in Appendix A). In addition, the majority of Asian respondents (75 percent) resided in counties within the state of California. Therefore, in our regression models, we include two dummy variables indicating whether respondents resided in Hawaii or California relative to other states. In supplementary analysis, we experimented with including dummy variables for all states of residence but it did not improve the model fit (p value of the likelihood ratio test = .61). We therefore opted for the more parsimonious models with indicators for Hawaii and California only.

We consider two key county-level variables that may shape intergroup conflicts and anti-Asian racism and therefore help explain the relationship between coethnic concentration in the county and Asians’ perceived discrimination. One is the 2020 unemployment rate by county and the other is the severity of the COVID-19 pandemic situation measured by confirmed cases of COVID-19 per 1,000 population as of July 25, 2021. We use county-level cumulative COVID-19 cases, and Figure A2 in Appendix A shows that they are highly correlated with the corresponding COVID-19 case numbers from the previous year (i.e., cumulative cases as of July 25, 2020).

We also include a series of sociodemographic controls: gender, age, education, household income, marital status, job change, and immigration status. Gender is measured through a dummy variable, with 1 indicating women and 0 indicating men. Age is measured as a continuous variable in years. Education level ranges from 2 (up to fourth grade) to 16 (doctoral degree) in our sample. Household income level ranges from 1 (<$5,000) to 16 (≥$150,000). Marital status contains three categories: married (reference category), never married, and previously married (including separated, divorced, or widowed). Job change is a dichotomous measure of whether the respondent has experienced job change since the pandemic started or not (reference category). Immigration status distinguishes between foreign-born and U.S.-born respondents (reference category). We also include two continuous county-level controls: Gini coefficient and median household income. Table 1 reports the descriptive statistics for our variables.

Table 1.

Descriptive Statistics for Key Variables In Analysis.

	Mean or %	SD	Minimum	Maximum
Perceived discrimination	.61	1.54	0	8
Coethnic concentration
% Asians in the county	13.81	8.46	.10	41.90
COVID-19 cases per 1,000 population	106.70	29.73	14.29	166.67
2020 unemployment rate	10.54	2.64	4.00	18.00
Individual controls
Female	56.74		0	1
Education	12.56	2.15	2	16
Age	44.34	15.40	18	97
Household income	11.79	4.37	1	16
Job change	1.67		0	1
Married	52.81		0	1
Never married	36.27		0	1
Separated/divorced/widowed	10.92		0	1
Foreign born	56.21		0	1
Residing in California	75.35		0	1
Residing in Hawaii	1.21		0	1
County-level controls
Gini coefficient	.48	.01	.44	.51
Median household income (×$10,000)	6.54	1.45	3	11
Total observations
Individual	569
Individual wave	10,766

Note: We report percentages for dichotomous variables. COVID-19 = coronavirus disease 2019.

Analytic Strategy

Our analysis involves three steps. First, we descriptively examine the association between the percentage of Asians in the county and perceived discrimination among Asians. This analysis provides a general picture about how Asians’ perceived discrimination is correlated with coethnic concentration. Second, we use multilevel negative binomial models to estimate the contextual effects of coethnic concentration on perceived discrimination among Asians. Multilevel modeling is used given the data structure (individual-wave observations nested within individuals). We treat the composite scale of perceived discrimination as a count variable. Because the dependent variable is over-dispersed (variance [2.39] > mean [0.61]), we use the negative binomial specification for statistical estimations to account for overdispersion (Long 1997). Figure 2 presents the observed proportions along with the Poisson and negative binomial probabilities for perceived discrimination. Indeed, we find that the negative binomial probability curve fits the data better than the Poisson probability curve. As robustness checks, supplementary analysis shows that our results are substantively the same if we use multilevel Poisson models (see Table A1 in Appendix A).

Figure 2.

Comparing the fit of negative binomial versus Poisson models.

Finally, in our regression models, we add county-level COVID-19 cases per 1,000 population and unemployment rates to investigate whether they help explain the relationship between coethnic concentration and perceived discrimination among Asians.

Results

To begin, we consider the descriptive association between coethnic concentration of Asians and their perceived discrimination. Figure 3 shows the scatterplot between the percentage of Asians and perceived discrimination among Asians at the state level (Figure 3A) and county level (Figure 3B). Overall, we see a curvilinear association between coethnic concentration and perceived discrimination among Asians. As the level of coethnic concentration increases, it produces varying impacts on Asians’ perceived discrimination. Initially, Asian concentration appears positively associated with perceived discrimination. However, there is a turning point as the level of Asian concentration reaches about 15 percent to 20 percent. The increase in Asian concentration beyond 20 percent is associated with lower perceived discrimination among Asians. This curvilinear pattern exists across Figures 3A and 3B. Figure 3A shows a more positive association at the state level, possibly because all the U.S. states in the graph have lower than 15 percent of Asians. In Figure 3B, the county-level pattern represents a fuller picture of the association between Asian concentration and perceived discrimination, with Asian concentration showing a wide range. Our regression analysis focuses on estimating the relationship between county-level coethnic concentration and perceived discrimination among Asians.

Figure 3.

Scatterplot between coethnic concentration and perceived discrimination among Asians.

Next, in Table 2, we use multilevel negative binomial models to understand how county-level coethnic concentration is related to Asians’ perceived discrimination. Note that the significant dispersion parameter ln(α) across all four models (p < .001) lends support to the use of negative binomial models over Poisson regression models.¹ To capture the curvilinear association, all models include coethnic concentration (percentage Asians) and its squared term. Model 1 includes only our major predictors and shows that both the percentage of Asians and its squared term are significant (p < .001). According to model 1, Asians’ perceived discrimination among Asians initially increases as the percentage of Asians in the county increases from 0 percent to 18.7 percent (0.112/[0.003 × 2]); as the percentage of Asians continues to increase, perceived discrimination among Asians starts to decrease. In model 2, we add individual and county-level controls, and the n-shaped relationship between the percentage of Asians and perceived discrimination among Asians remains because the squared term of the percentage of Asians is still significant (p < .05).

Table 2.

Multilevel Negative Binomial Models Estimating the Effect of Coethnic Concentration on Perceived Discrimination among Asians.

	Model 1	Model 2	Model 3	Model 4
Coethnic concentration
% Asians in the county	.112*** (.032)	.090⁺ (.049)	.073 (.049)	.064 (.053)
% Asians in the county × % Asians in the county	–.003*** (.001)	–.002* (.001)	–.002 (.001)	–.002 (.001)
COVID-19 cases per 1,000 population			.011* (.005)	.010⁺ (.005)
2020 unemployment rate				.028 (.062)
Individual controls
Female (0 = no, 1 = yes)		–.152 (.173)	–.149 (.172)	–.150 (.172)
Education (2–16)		–.053 (.039)	–.055 (.038)	–.054 (.038)
Age (in years)		–.014* (.007)	–.013⁺ (.007)	–.013⁺ (.007)
Household income (1–16)		–.054*** (.016)	–.056*** (.016)	–.056*** (.016)
Job change (0 = no, 1 = yes)		.276 (.187)	.280 (.187)	.280 (.187)
Never married (reference: married)		.352⁺ (.206)	.317 (.206)	.314 (.206)
Separated/divorced/widowed (reference: married)		.997*** (.266)	.958*** (.265)	.964*** (.265)
Foreign born (0 = no, 1 = yes)		.126 (.181)	.086 (.181)	.086 (.180)
Residing in California		–.147 (.390)	–.147 (.390)	–.147 (.390)
Residing in Hawaii		.804 (1.035)	.804 (1.035)	.804 (1.035)
Wave of the survey (1–29)		–.039*** (.003)	–.039*** (.003)	–.039*** (.003)
County-level controls
Gini coefficient		–.147 (.390)	–.219 (.389)	–.236 (.391)
Median household income		.804 (1.035)	1.083 (1.033)	.853 (1.152)
Constant	–2.347*** (.262)	–4.274 (5.564)	–3.911 (5.536)	–3.472 (5.617)
Dispersion parameter
ln(α)	1.042*** (.046)	.982*** (.046)	.981*** (.046)	.981*** (.046)
Random effects
Var(individual)	3.223*** (.297)	3.162*** (.293)	3.119*** (.289)	3.117*** (.289)
n (individual wave)	10,766	10,766	10,766	10,766
n (individual)	569	569	569	569

Note: Values in parentheses are standard errors. To be more precise, the coefficient for “% Asians in the county × % Asians in the county” is –0.00306 in model 1 (p = .000), –0.00241 in model 2 (p = .036), –0.00174 in model 3 (p = .138), and –0.00155 in model 4 (p = .212). COVID-19 = coronavirus disease 2019.

p < .10. *p < .05. ***p < .001.

In models 3 and 4, we consider whether county-level COVID-19 infection rates and unemployment rates help explain the significant curvilinear association between Asian concentration and perceived discrimination among Asians. In model 3, we include COVID-19 cases per 1,000 population, which shows two findings. First, COVID-19 infection rates at the county level have a significant and positive impact on Asians’ perceived discrimination (b = 0.011, p < .05). This suggests that higher rates of COVID-19 cases are associated with higher levels of anti-Asian prejudice. Second, when county-level COVID-19 cases per 1,000 population are included in model 3, the coefficients for the percentage of Asians and its squared term are no longer significant. This finding suggests that the county-level severity of the COVID-19 infection helps explain the association between coethnic concentration and Asians’ perceived discrimination. Therefore, the higher levels of perceived discrimination observed in counties with moderate levels of Asian concentration are driven largely by COVID-19 infection rates (see Figure A3 in Appendix A). Finally, in model 4, we add county-level unemployment rates in 2020 to assess whether the economic situation during COVID-19 helps explain the relationship between the percentage of Asians and perceived discrimination. We find no significant effect of county-level unemployment rates on perceived discrimination among Asians (b = 0.028, p > .05). Likewise, controlling for unemployment rates does not change the significance of the coefficients for the percentage of Asians or its squared term.

To facilitate the interpretation of the results in nonlinear models (such as negative binomial models in our case), we follow Mize, Doan, and Long’s (2019) advice to present the predicted values of perceived discrimination in Figure 4 by Asian concentration. Figures 4A to 4D are created, respectively, on the basis of models 1 through 4 in Table 2. Figure 4A is the baseline model without any covariates and shows that as the percentage of Asians in the county increases from 0 percent to about 18 percent, Asians’ perceived discrimination increases nearly threefold, from 0.48 to 1.32. However, as the percentage of Asians in the county increases from 18 percent to about 40 percent, Asians’ perceived discrimination decreases from 1.32 to 0.31. Clearly, the relationship between the percentage of Asians and perceived discrimination is curvilinear or n shaped.

Figure 4.

Predicted values of Asians’ perceived discrimination by the percentage of Asians in the county.

Figure 4B shows that after including individual controls, county-level Gini coefficient, and median household income, the n-shaped curvilinear relationship between the percentage of Asians and perceived discrimination remains evident. In Figure 4C, after we include COVID-19 cases per 1,000 population, the dotted line becomes flatter, indicating that COVID-19 infection rates help explain the relationship between the percentage of Asians at the county level and perceived discrimination. Last, in Figure 4D, we further control for county-level unemployment rates in 2020, and the predicted values of perceived discrimination by county-level Asian concentration remain very similar to those in Figure 4C.

Taken together, the results from Figure 4 suggest that the n-shaped association between coethnic concentration and perceived discrimination among Asians is in part explained by the severity of the COVID-19 infection rate in the county whereas county-level unemployment rates explain little (if any) of the association.

Discussion and Conclusion

There are two major findings of our study. First, we find that across U.S. counties, there is an n-shaped association between coethnic concentration and perceived discrimination among Asians since the start of the COVID-19 pandemic. Asians perceive the lowest level of discrimination when coethnic concentration in their county is low or high. In contrast, Asians perceive the highest discrimination in counties with a medium concentration of Asians (about 18 percent). The major contribution of our study is to show that the relationship between coethnic concentration and perceived discrimination during COVID-19 does not work in a linear fashion but rather exhibits a curvilinear pattern.

Our findings show evidence of the contested boundaries hypothesis that areas with a medium level of Asian concentration are associated with greater perceived discrimination. Our results are consistent with Crowder (2000) and Legewie and Schaeffer (2016), who also found nonlinear effects of minority residential concentration. Although it is beyond the scope of this study to test the causal mechanisms underlying this relationship, our findings are consistent with Schelling (1971) on tipping points. Whites may perceive greater threat in counties with moderate Asian concentrations, which in turn could heighten discrimination toward Asians. Perceptions, whether real or distorted, have consequences for attitudes toward immigrants and minorities (Alba, Rumbaut, and Marotz 2005). As Abascal (2020) found, when Whites experience a threat to their group status, they are more likely to harden their boundaries and redefine White membership to be more exclusive.

Additionally, it is possible that the COVID-19 crisis may have made racial boundaries even more contested and this could be exacerbated in these counties. During the pandemic, Asian Americans as a whole, regardless of nativity, were racialized and perceived as foreigners (Tessler et al. 2020). Many non-Asians in the United States viewed the virus as foreign and Asians as agents spreading the virus (Ellerbeck 2020). Counties with moderate Asian concentrations may be perceived by non-Asians as areas with large foreign-born Asian populations, which may have heightened the visibility of Asians in these counties and increased their experiences of racialization, conflict, and perceived discrimination (Goto, Gee, and Takeuchi 2002; Morey et al. 2020; Viruell-Fuentes et al. 2013). Our findings suggest that everyday forms of discrimination were the most salient among Asians living in counties with moderate coethnic concentration.

Future research may assess whether areas with moderate Asian concentrations have also experienced rapid increases in their minority populations prior to COVID-19. It is possible that such demographic changes have led to greater levels of perceived threat or lower trust among Whites, which may in turn lead to greater prejudice toward non-Whites, including Asians. Although we observe individuals over a 16-month period, our data do not provide information on changes in residential patterns over long periods of time. In our sample, we find that respondents residing in counties with a medium level of Asians are mainly from California, Illinois, New York, New Jersey, Oregon, Texas, Washington, and Virginia. The 2020 census revealed that in the past decade, numerous counties within these states have experienced White population loss and minority population increases, which lends support to the argument that changing minority populations over time could be driving this effect (Frey 2020). More comprehensive and detailed data on county-level racial and ethnic composition could help confirm this.

Our findings also show that once the percentage of Asians reaches beyond the medium level, further increases in coethnic concentration are associated with decreases in perceived discrimination. This finding is partially consistent with the ethnic enclave hypothesis. Whereas the ethnic enclave hypothesis posits a linear, negative relationship between coethnic concentration and discrimination, our findings extend this by showing a threshold effect and that the coethnic community is protective only after coethnic concentration is moderately high. Furthermore, our results show that large concentrations of coethnics may offer a protective factor against discrimination even during periods of intense racial conflict and economic recessions. Although the effects of ethnic enclave hypothesis have focused primarily on how living with coethnics can provide solutions to discrimination in the primary labor market (Boyd 1996; Wilson 2003), our findings show that during periods of heightened racial unrest, living with a high percentage of coethnics continues to provide a protective effect against perceived discrimination.

Future research may examine the mechanisms that contribute to the protective effect of the coethnic community. For example, it is possible that in counties with high levels of coethnic concentration, Asians have fewer encounters or interactions with people from other racial and ethnic groups, or alternatively, the large presence of Asians may change the dominant racial discourses and how Asians are treated in local areas. In addition, future research may extend our work by considering other racial and ethnic groups during times of crisis. It remains an open question of whether the coethnic community may still act as a refuge against discrimination for groups that did not face the same scrutiny or public visibility. We also note that in our sample, counties with high Asian concentrations are mainly located in California and Hawaii. If data become available, future research should examine coethnic concentration and Asians’ perceived discrimination in more disaggregated geographic areas that may contain greater variations in Asian concentration (e.g., by ZIP code or census tract).

Second, we find that the severity of the COVID-19 pandemic situation helps explain the relationship between coethnic concentration and perceived discrimination among Asians. This finding does not show strong support for the group conflict perspective which posits heightened prejudice or conflict arising from greater perceived competition during economic stress (Oliver and Mendelberg 2000; Olzak 1992). Rather, our results show evidence that racial tensions during COVID-19 were exacerbated for Asians living in U.S. counties with high COVID-19 infection rates. One potential reason why COVID-19 infection rates help explain the relationship between Asian concentration and perceived discrimination is related to the public perception of the Chinese government’s role in COVID-19 originating out of Wuhan. In June 2020, nearly half of Americans believed that China should be held accountable for its role in the pandemic (Silver, Devlin, and Huang 2020). Chinese are the largest Asian group in the United States, and Chinese ethnicity tends to be conflated with the Asian panethnic group and vice versa (Lee 2021). Thus, anti-China and anti-Chinese sentiment may be projected more broadly to anti-Asian sentiment during a public health crisis that first appeared in China.

This research is not without limitations. Our study faces well-known methodological challenges in estimating the effects of residential coethnic concentration. Selection bias is an issue as it is possible that Asians who perceive greater levels of discrimination are forced into particular residential areas (Iceland and Wilkes 2006). Thus, perceived discrimination may reflect selection into certain counties rather than a result of the coethnic concentration in the county. Related, it is also possible that Whites or non-Asians living in areas with high levels of Asians are selective of individuals who are more tolerant of non-White residential areas (Bobo and Zubrinsky 1996). Another limitation is that it is unclear how generalizable our findings are to a context outside of the pandemic. For instance, prior to COVID-19, Asians may have experienced some protection from a model minority stereotype and low levels of harassment (Ramakrishnan et al. 2017), though there is evidence of minor increases in anti-Asian discrimination before the pandemic (Zhang et al. 2022). Research with comparable data from both before and during the pandemic would better identify the role of the COVID-19 pandemic in shaping anti-Asian discrimination and its linkages to county-level contextual factors.

Overall, this study shows that the context of medium Asian concentration together with high COVID-19 infection rates created a particularly hostile environment for Asians during the pandemic. To better understand the future of racial relations in postpandemic America, more research is needed to assess contextual factors and perceived discrimination among Asians in the evolving social, economic, and political landscapes.

Research Data

sj-docx-1-srd-10.1177_23780231221124580 – Research Data for Coethnic Concentration and Asians’ Perceived Discrimination across U.S. Counties during COVID-19

Research Data, sj-docx-1-srd-10.1177_23780231221124580 for Coethnic Concentration and Asians’ Perceived Discrimination across U.S. Counties during COVID-19 by Rennie Lee, Yue Qian and Cary Wu in Socius

This article is distributed under the terms of the Creative Commons Attribution 4.0 License (http://www.creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Footnotes

Appendix A

Table A1.

Multilevel Poisson Models Estimating the Effect of Coethnic Concentration on Perceived Discrimination among Asians.

	Model 1	Model 2	Model 3	Model 4
Coethnic concentration
% Asians in the county	.108*** (.031)	.079⁺ (.046)	.061 (.046)	.051 (.050)
% Asians in the county × % Asians in the county	–.003*** (.001)	–.002* (.001)	–.001 (.001)	–.001 (.001)
COVID-19 cases per 1,000 population			.011* (.004)	.010* (.005)
2020 unemployment rate				.029 (.057)
Individual controls	No	Yes	Yes	Yes
County-level controls	No	Yes	Yes	Yes
Constant	–2.422*** (.249)	–4.879 (5.207)	–4.544 (5.179)	–4.068 (5.257)
Random effects
Var(individual)	3.454*** (.299)	3.240*** (.283)	3.194*** (.279)	3.191*** (.279)
n (individual wave)	10,766	10,766	10,766	10,766
N(individual)	569	569	569	569

Note: Values in parentheses are standard errors. Individual and county-level controls are the same as those in Table 2. COVID-19 = coronavirus disease 2019.

p < .10. *p < .05. ***p < .001.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article:Rennie Lee received support from the Australia Research Council Centre of Excellence for Children and Families over the Life Course (CE200100025). Yue Qian and Cary Wu received support from the Canadian Institutes of Health Research (funding number: OV7-170372 for Qian; FRN-170368 for Wu). Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the funders.

ORCID iDs

Rennie Lee

Yue Qian

Cary Wu

Notes

Author Biographies

Rennie Lee is a senior research fellow at the Institute for Social Science Research and ARC Life Course Centre at the University of Queensland. Her main areas of interest include immigrant integration and racial and ethnic stratification in different national contexts. Her current research examines immigrant and ethnic groups in North America, Europe, and Australia, including a focus on anti-Asian sentiment since COVID-19.

Yue Qian is an associate professor of sociology at the University of British Columbia. Her research interests focus on family, gender, and demography. Her current research examines how gender intersects with family and population processes (e.g., assortative mating, divisions of labor, parenthood) to shape individual well-being and social inequality in East Asia and North America. Most recently, she has been researching the social and mental health impacts of COVID-19. Her research has appeared in the American Sociological Review, Social Forces, and the Journal of Marriage and Family, among others.

Cary Wu is an assistant professor of sociology at York University in Canada.

References

Aaronson

Stephanie

Alba

Francisca

. 2020. “The Unemployment Impacts of COVID-19: Lessons from the Great Recession.” Washington DC: Brookings Institution. Retrieved September 6, 2022. https://www.brookings.edu/blog/up-front/2020/04/15/the-unemployment-impacts-of-covid-19-lessons-from-the-great-recession/.

Abascal

Maria

. 2020. “Contraction as a Response to Group Threat: Demographic Decline and Whites’ Classification of People Who Are Ambiguously White.” American Sociological Review 85(2):298–322.

Abascal

Maria

Baldassarri

Delia

. 2015. “Love Thy Neighbor? Ethnoracial Diversity and Trust Reexamined.” American Journal of Sociology 121(3):622–782.

Alba

Richard

Rumbaut

Rubén G.

Marotz

Karen

. 2005. “A Distorted Nation: Perceptions of Racial/Ethnic Group Sizes and Attitudes Toward Immigrants and Other Minorities.” Social Forces 84(2):901–19.

Blalock

H. M.

Jr.

1957. “Per Cent Non-White and Discrimination in the South.” American Sociological Review 22(6):677–82.

Blumer

Herbert

. 1958. “Race Prejudice as a Sense of Group Position.” Pacific Sociological Review 1(1):3–7.

Bobo

Lawrence

Hutchings

Vincent L.

1996. “Perceptions of Racial Group Competition : Extending Blumer’s Theory of Group Position to a Multiracial Social Context.” American Sociological Review 61(6):951–72.

Bobo

Lawrence.

Zubrinsky

Camille L.

1996. “Attitudes on Residential Integration: Perceived Status Differences, Mere In-Group Preference, or Racial Prejudice?” Social Forces 74(3):883–909.

Boyd

Robert L.

1996. “The Great Migration to the North and the Rise of Ethnic Niches for African American Women in Beauty Culture and Hairdressing.” Sociological Focus 29(1):33–45.

10.

Budiman

Abby

Ruiz

Neil G.

2021a. “Asian Americans Are the Fastest-Growing Racial or Ethnic Group in the U.S.” Washington, DC: Pew Research Center. Retrieved September 6, 2022.https://www.pewresearch.org/fact-tank/2021/04/09/asian-americans-are-the-fastest-growing-racial-or-ethnic-group-in-the-u-s/.

11.

Budiman

Abby

Ruiz

Neil G.

2021b. “Key Facts about Asian Americans, a Diverse and Growing Population.” Washington, DC: Pew Research Center. Retrieved September 6, 2022. https://www.pewresearch.org/fact-tank/2021/04/29/key-facts-about-asian-americans/.

12.

Bureau of Labor Statistics. 2021. Labor force data by county, 2020 annual averages. Accessed July 25, 2021 https://www.bls.gov/lau/#data

13.

Bygren

Magnus

Szulkin

Ryszard

. 2010. “Ethnic Environment during Childhood and the Educational Attainment of Immigrant Children in Sweden.” Social Forces 88(3):1–25.

14.

Camacho

Gabriel

Aerielle

M. Allen

Quinn

Diane M.

2019. “Neighborhood Ethnic Composition and Perceived Discrimination Among Young Adult Latina/os: The Mediating Role of Ethnic Centrality.” Hispanic Journal of Behavioral Sciences 41(3):331–43.

15.

Center for the Study of Hate and Extremism. 2021. “Fact Sheet: Anti-Asian Prejudice March 2021.” Retrieved September 6, 2022. https://www.csusb.edu/sites/default/files/FACT%20SHEET-%20Anti-Asian%20Hate%202020%20rev%203.21.21.pdf.

16.

Charles

Camille Zubrinsky

. 2003. “The Dynamics of Racial Residential Segregation.” Annual Review of Sociology 29:167–207.

17.

Charles

Camille Zubrinsky

. 2008. “Comfort Zones: Immigration, Acculturation, and the Neighborhood Racial-Composition Preferences of Latinos and Asians.” Du Bois Review 4(1):41–77.

18.

Clark

W.A.V.

1991. “Residential Preferences and Neighborhood Racial Segregation: A Test of the Schelling Segregation Model.” Demography 28(1):1–19.

19.

Conger

Dylan

Schwartz

Amy E.

Stiefel

Leanna

. 2011. “The Effect of Immigrant Communities on Foreign-Born Student Achievement.” International Migration Review 45(3):675–701.

20.

Crowder

Kyle

. 2000. “The Racial Context of White Mobility: An Individual-Level Assessment of the White Flight Hypothesis.” Social Science Research 29:223–257.

21.

Crowder

Kyle

South

Scott J.

2008. “Spatial Dynamics of White Flight: The Effects of Local and Extralocal Racial Conditions on Neighborhood Out-Migration.” American Sociological Review 73(5):792–812.

22.

Darling-Hammond

Sean

Michaels

Eli K.

Amani

M. Allen

Chae

David H.

Thomas

Marilyn D.

Nguyen

Thu T.

Mujahid

Mahasin M.

, et al. 2020. “After ‘the China Virus’ Went Viral: Racially Charged Coronavirus Coverage and Trends in Bias against Asian Americans.” Health Education and Behavior 47(6):870–79.

23.

Desmond

Matthew

Valdez

Nicol

. 2012. “Unpolicing the Urban Poor: Consequences of Third-Party Policing for Inner-City Women.” American Sociological Review 78(1):117–41.

24.

Dhanani

Lindsay Y.

Franz

Berkeley

. 2020. “Unexpected Public Health Consequences of the COVID-19 Pandemic: A National Survey Examining Anti-Asian Attitudes in the USA.” International Journal of Public Health 65(6):747–54.

25.

Dong

Ensheng

Hongru

Gardner

Lauren

. 2020. “An Interactive Web-Based Dashboard to Track COVID-19 in Real Time.” Lancet Infectious Diseases 20(5):533–34.

26.

Eichelberger

Laura

. 2007. “SARS and New York’s Chinatown: The Politics of Risk and Blame during an Epidemic of Fear.” Social Science and Medicine 65(6):1284–95.

27.

Ellerbeck

2020. Over 30 percet of Americans have witnessed COVID-19 bias against Asians, poll syas. NBC News. Accessed 1 September 2022. https://www.nbcnews.com/news/asian-america/over-30-americans-have-witnessed-covid-19-bias-against-asians-n1193901.

28.

Ervasti

Heikki

Kouvo

Antti

Venetoklis

Takis

. 2019. “Social and Institutional Trust in Times of Crisis: Greece, 2002–2011.” Social Indicators Research 141(3):1207–31.

29.

Frey

William H.

2020. “The Nation Is Diversifying Even Faster Than Predicted, According to New Census Data.” Retrieved September 6, 2022. https://www.brookings.edu/research/new-census-data-shows-the-nation-is-diversifying-even-faster-than-predicted/.

30.

Gee

Gilbert C.

2002. “A Multilevel Analysis of the Relationship between Institutional and Individual Racial Discrimination and Health Status.” American Journal of Public Health 92(4):615–23.

31.

Gee

Gilbert C.

Ponce

Ninez

. 2010. “Associations between Racial Discrimination, Limited English Proficiency, and Health-Related Quality of Life among 6 Asian Ethnic Groups in California.” American Journal of Public Health 100(5):888–95.

32.

Goldhaber-Fiebert

Jeremy D.

Holsinger

Alexander F.

Holsinger

Erin

Long

Elizabeth

. 2021. “State- and County-Level COVID-19 Public Health Orders in California: Constructing a Dataset and Describing Their Timing, Content, and Stricture.” medRxiv. Retrieved September 6, 2022. https://www.medrxiv.org/content/10.1101/2020.11.08.20224915v8.

33.

Goto

Sharon G.

Gee

Gilbert C.

Takeuchi

David T.

2002. “Strangers Still? The Experience of Discrimination among Chinese Americans.” Journal of Community Psychology 30(2):211–24.

34.

Grimshaw

Allen D.

1960. “Urban Racial Violence in the United States: Changing Ecological Considerations.” American Journal of Sociology 66(2):109–19.

35.

Hall

Matthew

. 2013. “Residential Integration on the New Frontier: Immigrant Segregation in Established and New Destinations.” Demography 50(5):1873–96.

36.

Hou

Feng

Zheng

. 2009. “Racial Diversity, Minority Concentration, and Trust in Canadian Urban Neighborhoods.” Social Science Research 38(3):693–716.

37.

Hunt

Matthew O.

Wise

Lauren A.

Jipguep

Marie Claude

Cozier

Yvette C.

Lynn

Rosenberg

. 2007. “Neighborhood Racial Composition and Perceptions of Racial Discrimination: Evidence from the Black Women’s Health Study.” Social Psychology Quarterly 70(3):272–89.

38.

Iceland

John

Weinberg

Daniel

Hughes

Lauren

. 2014. “The Residential Segregation of Detailed Hispanic and Asian Groups in the United States: 1980–2010.” Demographic Research 31(1):593–624.

39.

Iceland

John

Wilkes

Rima

. 2006. “Does Socioeconomic Status Matter? Race, Class, and Residential Segregation.” Social Problems 53(2):248–73.

40.

Jeung

Russell

Horse

Aggie Yellow

Popovic

Tara

Lim

Richard

. 2021. “Stop AAPI Hate National Report 3/19/20–2/28/21.” Retrieved September 6, 2022. https://stopaapihate.org/wp-content/uploads/2021/05/Stop-AAPI-Hate-Report-National-210316.pdf.

41.

Kibria

Nazli

. 2000. “Race, Ethnic Options, and Ethnic Binds: Identity Negotiations of Second-Generation Chinese and Korean Americans.” Sociological Perspectives 43(1):77–95.

42.

Lee

Erika

. 2007. “The ‘Yellow Peril’ and Asian Exclusion in the Americas.” Pacific Historical Review 76(4):537–62.

43.

Lee

Jennifer

. 2021. “Asian Americans, Affirmative Action & the Rise in Anti-Asian Hate.” Daedalus 150(2):180–98.

44.

Lee

Jennifer C.

Kye

Samuel

. 2016. “Racialized Assimilation of Asian Americans.” Annual Review of Sociology 42:253–73.

45.

Lee

Jennifer

Zhou

Min

. 2015. The Asian American Achievement Paradox. New York: Russell Sage.

46.

Lee

Rennie

. 2016. “Doing Good in the Neighborhood ? The Effect of Coethnic Concentration on the Educational Attainment of Mexican, Filipino, and Vietnamese Children of Immigrants.” International Journal of Sociology of Education 5(3):214–43.

47.

Lee

Tiane L.

Fiske

Susan T.

2006. “Not an Outgroup, Not Yet an Ingroup: Immigrants in the Stereotype Content Model.” International Journal of Intercultural Relations 30(6):751–68.

48.

Legewie

Joscha

. 2018. “Living on the Edge: Neighborhood Boundaries and the Spatial Dynamics of Violent Crime.” Demography 55(5):1957–77.

49.

Legewie

Joscha

Schaeffer

Merlin

. 2016. “Contested Boundaries: Explaining Where Ethnoracial Diversity Provokes Neighborhood Conflict.” American Journal of Sociology 122(1):125–61.

50.

Wei

. 1998. “Anatomy of a New Ethnic Settlement: The Chinese Ethnoburb in Los Angeles.” Urban Studies 35(3):479–501.

51.

Lim

Sungwoo

Stella S.

De La Cruz

Nneka Lundy

Trinh-Shevrin

Chau

. 2017. “Defining Ethnic Enclave and Its Associations with Self-Reported Health Outcomes Among Asian American Adults in New York City.” Journal of Immigrant and Minority Health 19(1):138–46.

52.

Link

Michael W.

Oldendick

Robert W.

1996. “Social Construction and White Attitudes toward Equal Opportunity and Multiculturalism.” The Journal of Politics 58(1): 149–168.

53.

Liu

Ying

Finch

Brian Karl

Brenneke

Savannah G.

Thomas

Kyla

PhuongThao D.

2020. “Perceived Discrimination and Mental Distress amid the COVID-19 Pandemic: Evidence from the Understanding America Study.” American Journal of Preventive Medicine 59(4):481–92.

54.

Logan

John R.

2013. “The Persistence of Segregation in the 21st Century Metropolis.” City and Community 12(2):160–68.

55.

Logan

John R.

Wenquan

Zhang

. 2011. “Global Neighborhoods: New Evidence from Census 2010.” Retrieved September 6, 2022. https://s4.ad.brown.edu/Projects/Diversity/Data/Report/globalfinal2.pdf.

56.

Logan

John R.

Wenquan

Zhang

Alba

Richard D.

2002. “Immigrant Enclaves and Ethnic Communities in New York and Los Angeles.” American Sociological Review 67(2):299–322.

57.

Long

Scott J.

1997. Regression Models for Categorical and Limited Dependent Variables. Newbury Park, CA: Sage.

58.

Lyu

Wei

Wehby

George L.

2020. “Community Use of Face Masks and COVID-19: Evidence from a Natural Experiment of State Mandates in the US.” Health Affairs 39(8):1419–25.

59.

Massey

Douglas S.

Denton

Nancy A.

1985. “Spatial Assimilation as a Socioeconomic Outcome.” American Sociological Review 50(1):94–106.

60.

Mize

Trenton D.

Doan

Long

J. Scott

. 2019. “A General Framework for Comparing Predictions and Marginal Effects across Models.” Sociological Methodology 49:152–89.

61.

Morey

Brittany N.

Gee

Gilbert C.

Shariff-Marco

Salma

Yang

Juan

Allen

Laura

Gomez

Scarlett Lin

. 2020. “Ethnic Enclaves, Discrimination, and Stress among Asian American Women: Differences by Nativity and Time in the United States.” Cultural Diversity and Ethnic Minority Psychology 26(4):460–71.

62.

Mossakowski

Krysia N.

Zhang

Wei

. 2014. “Does Social Support Buffer the Stress of Discrimination and Reduce Psychological Distress among Asian Americans?” Social Psychology Quarterly 77(3):273–95.

63.

Oliver

J. Eric

Mendelberg

Tali

. 2000. “Reconsidering the Environmental Determinants of White Racial Attitudes.” American Journal of Political Science 44(3):574.

64.

Olzak

Susan

. 1992. The Dynamics of Ethnic Competition and Conflict. Stanford, CA: Stanford University Press.

65.

Paul

Ayan

Englert

Philipp

Varga

Melinda

. 2021. “Socio-economic Disparities and COVID-19 in the USA.” Journal of Physics: Complexity 2(035017).

66.

Pettigrew

Thomas F.

1998. “Intergroup Contact Theory.” Annual Review of Psychology 49:65–85.

67.

Pong

Suet-ling

Hao

Lingxin

. 2007. “Neighborhood and School Factors in the School Performance of Immigrants’ Children.” International Migration Review 41(1):206–41.

68.

Portes

Alejandro

Rumbaut

Rubén G.

2014. Immigrant America: A Portrait. 4th ed. Palo Alto: University of California Press.

69.

Quillian

Lincoln

. 1996. “Group Threat and Regional Change in Attitudes toward African-Americans.” American Journal of Sociology 102(3):816–60.

70.

Ramakrishnan

Karthick

Wong

Janelle

Lee

Jennifer

Lee

Taeku

. 2017. “2016 Post-Election National Asian Survey. Retrieved Jan 5, 2022 from https://naasurvey.com/wp-content/uploads/2017/05/NAAS16-post-election-report.pdf

71.

Rosenstein

Judith E.

2008. “Individual Threat, Group Threat, and Racial Policy: Exploring the Relationship between Threat and Racial Attitudes.” Social Science Research 37(4):1130–46.

72.

Schelling

Thomas C.

1971. “Dynamic models of segregation.” Journal of Mathematical Sociology 1(2):143–186.

73.

Silver

Laura

Devlin

Kat

Huang

Christine

. 2020. Americans fault China for its role in the spread of COVID-19.” Pew Research Center. Accessed September 19, 2022 https://www.pewresearch.org/global/wp-content/uploads/sites/2/2020/07/PG_20.07.30_U.S.-Views-China_final.pdf

74.

Small

Mario L.

Pager

Devah

. 2020. “Sociological Perspectives on Racial Discrimination.” Journal of Economic Perspectives 34(2):46–97.

75.

Taylor

Marylee C.

1998. “How White Attitudes Vary with the Racial Composition of Local Populations : Numbers Count.” American Sociological Review 63(4):512–35.

76.

Taylor

Marylee C.

Mateyka

Peter J.

2011. “Community Influences on White Racial Attitudes: What Matters and Why?” Sociological Quarterly 52(2):220–43.

77.

Tessler

Hannah

Choi

Meera

Kao

Grace

. 2020. “The Anxiety of Being Asian American: Hate Crimes and Negative Biases during the COVID-19 Pandemic.” American Journal of Criminal Justice 45(4):636–46.

78.

The U.S. Congress Joint Economic Committee. 2018. The geography of social capital in America. Accessed on September 19, 2022 https://www.jec.senate.gov/public/index.cfm/republicans/2018/4/the-geography-of-social-capital-in-america

79.

Tran

Van C.

Jennifer

Lee

Khachikian

Oshin

Lee

Jess

. 2018. “Hyper-selectivity, Racial Mobility, and the Remaking of Race.” RSF 4(5):188–209.

80.

Vega

William A.

Ang

Alfonso

Rodriguez

Michael A.

Finch

Brian K.

2011. “Neighborhood Protective Effects on Depression in Latinos.” American Journal of Community Psychology 47(1):114–26.

81.

Viruell-Fuentes

Edna A.

Morenoff

Jeffrey D.

Williams

David R.

House

James S.

2013. “Contextualizing Nativity Status, Latino Social Ties, and Ethnic Enclaves: An Examination of the ‘Immigrant Social Ties Hypothesis.’” Ethnicity and Health 18(6):586–609.

82.

Walton

Emily

. 2012. “Resurgent Ethnicity among Asian Americans: Ethnic Neighborhood Context and Health.” Journal of Health and Social Behavior 53(3):378–94.

83.

Weill

Joakim A.

Matthieu

Stigler

Deschenes

Olivier

Springborn

Michael R.

2020. “Social Distancing Responses to COVID-19 Emergency Declarations Strongly Differentiated by Income.” Proceedings of the National Academy of Sciences 117(33):19658–60.

84.

Wen

Ming

Lauderdale

Diane S.

Kandula

Namratha R.

2009. “Ethnic Neighborhoods in Multi-ethnic America, 1990–2000: Resurgent Ethnicity in the Ethnoburbs?” Social Forces 88(1):425–60.

85.

Williams

David R.

Yan

Jackson

James S.

Anderson

Norman B.

1997. “Racial differences in physical and mental health: Socio-economic status, stress and discrimination.” Journal of Health Psychology 2(3):335–351.

86.

Wilson

Franklin D.

2003. “Ethnic Niching and Metropolitan Labor Markets.” Social Science Research 32(3):429–66.

87.

Cary

Qian

Yue

Wilkes

Rima

. 2021. “Anti-Asian Discrimination and the Asian-White Mental Health Gap during COVID-19.” Ethnic and Racial Studies 44(5):819–35.

88.

Zhang

Yan

Zhang

Lening

Benton

Francis

. 2022. “Hate Crimes against Asian Americans.” American Journal of Criminal Justice 47(3):441–61.

89.

Zhou

Min

. 2009. Contemporary Chinese America. Philadelphia: Temple University Press.

90.

Zhu

Pengyu

Liu

Cathy Yang

Painter

Gary

. 2013. “Does Residence in an Ethnic Community Help Immigrants in a Recession?” Regional Science and Urban Economics 47(1):112–27.

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