Fear, Political Legitimization, and Racism: Examining Anti-Asian Xenophobia During the COVID-19 Pandemic

Abstract

Unfortunately, there is a long history linking pandemics to Anti-Asian prejudice and xenophobia; these negative outcomes have been particularly pronounced during the COVID-19 pandemic. Some scholars have suggested that these relationships are driven by fear, wherein individuals respond to disease threat by “othering” that threat. Other research has implicated the role of the state, including the political rhetoric of former President Trump, in exacerbating bias by enabling a divisive political environment. Yet, very few existing studies have been able to assess the nature of these impacts or examine the mechanisms behind observed increases in xenophobia. To that end, this research presents results from a survey administered in May 2020 to respondents residing in the U.S., which assessed COVID-19-related attitudes and behaviors, as well as anti-Asian prejudicial attitudes. Using these data, we assessed the direct and interactive relationship between perceptions of risk (i.e., fear), exposure to COVID-19 information, support for Trump, and anti-Asian prejudice. Results reveal that fear—and support for Trump—are associated with increased anti-Asian prejudice, but that exposure to more information about COVID-19 is associated with decreased prejudice; we also note complex interactions between each of these factors.

When COVID-19—a pneumonia-like disease believed to have first originated in China—was first named by the World Health Organization (WHO), they followed their own guidelines for best practices regarding infectious diseases, which state that disease names should not include geographic locations or cultural references, among other factors (WHO, 2015a). The primary intention of these guidelines is to avoid causing offensive or negative consequences, including “backlash against members of particular religious or ethnic communities” (WHO, 2015b). This intention is, unfortunately, well-justified: there is a long history linking pandemics to prejudice and xenophobia, and the Asian population has been a particularly frequent target of disease-related prejudice and discrimination (Clissold et al., 2020; Muzzatti, 2005). There is also evidence that these negative stereotypes and racist tropes linking Asian people and disease have been particularly evident during the COVID-19 pandemic (see Lantz & Wenger, 2022), a pattern that is not particularly surprising, given the magnitude, duration, and societal impact of the disease.

Prior research suggests two primary explanations for this link between the COVID-19 pandemic and elevated anti-Asian prejudice and xenophobia. On the one hand, one body of research has suggested that the links between disease and anti-Asian xenophobia are driven by fear of the disease, of infection, and, resultantly, of the threat posed by any potential carriers of the disease. Individuals thus react to this fear by “othering” that threat (Lăzăroiu & Adams, 2020; Le et al., 2020; Lee et al., 2020; Ren & Feagin, 2021; Reny & Barreto, 2020). In doing so, they rely upon racist paradigms to establish identifiable characteristics (e.g., skin color, appearance)—for a disease that may otherwise not be “visible”—which are associated with potential disease carriers. This suggests that, on average, those who are more fearful of the disease, or perceive themselves or their family as more at risk from the disease, may exhibit more anti-Asian prejudicial attitudes. Importantly, in some cases where individuals are inclined to react to their fear by relying on stereotypes, it is possible this tendency might be attenuated by access to reliable information about the disease, which may serve to buffer against prejudicial stereotypes about Asian people as carriers of disease while also emphasizing alternative measures for resolving or addressing personal perceptions of risk (e.g., social distancing, frequent mask wearing). A lack of information, or misinformation, however, may even exacerbate belief in these stereotypes (Kuklinski et al., 2000; Rubinstein et al., 2018).

A second, separate body of research has implicated the role of the state—and highly influential political figures that represent that state—in exacerbating, emboldening, or legitimizing bias through the use of targeted inflammatory rhetoric. Former U.S. President Donald Trump, in particular, has consistently referred to the disease as the “China virus” or “Wuhan virus,” while also implicitly or explicitly placing blame for the disease on Chinese people, and Asian people more generally (Clissold et al., 2020; Noel, 2020; Ren & Feagin, 2021; Rubin & Wilson, 2021). The Federal Bureau of Investigation (FBI) even noted the dangerous nature of these sentiments—albeit implicitly—by quickly issuing a warning to law enforcement agencies around the country about an anticipated increase in anti-Asian hate crimes (Lantz & Wenger, 2022).

But, while there has been significant discussion regarding these different processes (e.g., Gover et al., 2020; Tessler et al., 2020), very few existing studies have been able to assess the nature of these impacts or examine the mechanisms behind disease-related increases in xenophobia. Following this, the current research attempts the theoretical integration of prior research on fear of disease, information effects, and political legitimization and their impact on prejudicial attitudes. In doing so, we posit that the combination of (a) the scope and impact of the COVID-19 pandemic; (b) differential information consumption regarding the disease; and (c) xenophobic political discourse linking Asian people to COVID-19, has contributed to a dangerous social climate in the U.S. wherein each of these factors has likely contributed both independently and—most importantly—jointly to increased anti-Asian racism in the United States. In examining these patterns, this research has important implications for understanding prejudicial attitudes not just during the COVID-19 pandemic, but also anti-Asian prejudicial attitudes during periods of social turmoil more generally.

Prior Research

The Impact of the COVID-19 Pandemic on Anti-Asian Hate and Bias

The early impacts of the COVID-19 pandemic on anti-Asian hate and bias are well-documented (Gover et al., 2020; Lantz & Wenger, 2022; Tessler et al., 2020; Wong, 2020).¹ According to the Stop AAPI Hate organization, which tracks such incidents, more than 10,000 hate incidents against Asian American and Pacific Islander people were reported between March 2020 and September 2021 (Yellow Horse et al., 2021). Other data sources, including law enforcement data have documented similar increases. One study, for example, found that officially recorded anti-Asian hate crimes in 16 major U.S. cities had increased by 145% in 2020 (CSU Center for the Study of Hate & Extremism, 2021). There are also several anecdotal examples of such incidents in the national media. In New York, for example, which established a task force specifically to deal with COVID-19-related increases, there were several anti-Asian incidents, including one in which a man poured acid on an Asian woman (Lee & Yadav, 2020). Some media reports have indicated that nearly one-third of Americans—and as much as 60% of Asian Americans—witnessed someone blaming Asian people in the early months of the COVID-19 pandemic (Ellerbeck, 2020).

Early empirical research has also found significant evidence of anti-Asian xenophobia and bias during the pandemic. Nguyen et al. (2020), for example, found that the proportion of tweets expressing negative attitudes toward Asian people increased significantly and concurrently with the spread of the disease across the U.S. Another study, conducted by Gray and Hansen (2021) used a difference-in-difference approach to assess the impact of the pandemic on anti-Asian hate crime in London and found that, in the first months following the identification of COVID-19, there was a significant increase in hate crimes against Chinese people, yet no such increases in other hate crimes or crime more generally. They argued, like many others have, that these patterns highlight an “intrinsic link” between COVID-19 and anti-Asian racism. Lu and Sheng (2020) similarly found that tweets containing anti-Asian slurs significantly increased in geographic areas immediately following local COVID-19 diagnoses.

Individual Fear and Anti-Asian Prejudice

Prior research has suggested at least two primary explanations for this demonstrable anti-Asian prejudice and xenophobia in the context of the COVID-19 pandemic. On the one hand, some research has suggested that increases in xenophobic attitudes may be driven by individual-level increases in fear, wherein individuals respond to the threat of infectious disease by “othering” that threat (Reny & Barreto, 2020; Silva et al., 2022). Indeed, there is an extensive body of research linking disease outbreak to prejudice and xenophobia (Clissold et al., 2020; Lee, 2019; Taylor, 2019). The disease-related stigmatization of Asian people, in particular, is situated within a longstanding history of racist tropes linking Asian people to stereotypes of disease, like the “Yellow Peril” myth propagated during the nineteenth century (see Chun, 2020; Molina, 2006; Mudambi, 2019; Power, 1995).

Within this context, Faulkner and colleagues (2004) drew on research in evolutionary psychology to present a theoretical model in which they posited that individual concerns regarding feelings of vulnerability to disease motivate negative reactions toward foreign people (see also, Christensen et al., 2020; Kurzban & Leary, 2001; Park et al., 2003). Others have also emphasized that individuals are more likely to activate stereotypical beliefs and attitudes when they feel threatened (e.g., Bodenhausen, 1993; Smith, 1993). According to this model, people have a basic tendency to avoid the risk of disease infection, and those who may be “likely” carriers of disease. In assigning “likelihood” in this scenario, individuals draw upon visibly identifiable features. In some cases, these features may be actual—albeit largely imperfect—indicators of individual ailments (e.g., lesions). In other cases, however, these features are grounded in prejudicial stereotypes and racist tropes, and include basic features like skin color. Racial outgroup members are conceptualized as importing or directly carrying disease pathogens (Faulkner et al., 2004). Critical to this model, however, is an individual sense of personal vulnerability; if one does not feel personally vulnerable, they are less likely to draw upon these xenophobic stereotypes, while those who feel more personally vulnerable, are more likely to draw upon these stereotypes (other factors aside, which we discuss below). In testing these propositions, Faulkner and colleagues found that those individuals with more concerns regarding chronic disease were more likely to perceive foreign outgroups as dangerous, and to have less positive attitudes toward immigrants.

While research directly examining these processes during the COVID-19 pandemic has been limited given the comparatively recent nature of the disease, research on other global health crises—like the bubonic plague (Markel, 1999), the 2014 Ebola outbreak (see Prati & Pietrantoni, 2016), and a host of other disease outbreaks (Cohn, 2012; Gover et al., 2020; McKiven, 2007)— has provided support for this conceptual link between personal fear and concern regarding the disease and outgroup prejudice. In one of the few empirical examinations of these processes during the pandemic, Mandalaywala et al. (2021) found evidence that personal perceptions of the intensity of COVID-19 were associated with less willingness to interact with racial outgroups, including both Asian and Black people; these patterns were also associated with increased indicators of prejudicial attitudes (see also, Tabri et al., 2020). Taken together, existing research has provided considerable evidence for the role of fear and racialized disease threat in exacerbating prejudicial outgroup attitudes during significant disease outbreaks.

Political Rhetoric, Legitimization, and Anti-Asian Prejudice

A separate explanation, on the other hand, posits that these relationships are not just situated within the context of the disease itself, but in public discourse surrounding the disease. Unfortunately, narratives placing the onus for COVID-19 on Asian people are pervasive in the U.S., and this was particularly true at the beginning of the pandemic (Noel, 2020). Put simply, anti-Asian prejudice has been blatantly expressed by several public office holders, including the former President (Zhou, 2020), who disparagingly referred to the disease as the “Wuhan virus,” the “Chinese virus,” and even the “Kung Flu.” As Lee and Yadav (2020, p. 17) poignantly stated, “in one fell swoop, the coronavirus—and Trump's blithe description of it—reanimated a century-old racist trope that Asian Americans are vectors of filth and disease.”

This state- and nation-level xenophobic political rhetoric does not exist in a vacuum, however, and inevitably filters down to the local and individual level (see Piatkowska & Lantz, 2021). Specifically, the political legitimization perspective, or the related idea of “emboldenment” (see Dugan & Chenoweth, 2020), posits that negative government attention toward specific population groups can facilitate increases in biased behavior toward that group. Put another way, when government elites and political leaders signal supremacy over other groups, that signal serves to embolden members of the dominant group and to send the message that negative attitudes and behavior—including xenophobic attitudes, and even hate crimes—toward that group can be acted upon or committed without consequence (see Gagnon, 1995; Kalmoe, 2014; Kteily et al., 2015; Müller & Scwarz, 2020). As Dugan and Chenoweth (2020, p. 716) stated, some would-be bigots “perceive that the government is giving them a license to act on their anger.” Indeed, Kalmoe (2014) found that those with aggressive traits who were exposed to political advertisements with violent metaphors were more likely to support political violence, compared to those exposed to nonviolent ads.

This theoretical perspective is specifically applicable to the rhetoric of former President Trump and other high-profile political figures. As the highest elected political official in the US, the president has substantial power to impact and control societal discourse. Within the parlance of this legitimization framework, a number of scholars have pointed to former President Trump's rhetoric toward specific population groups—not exclusive to Asian people during the pandemic—as emboldening individual bias and hate-motivated violence (Dugan & Chenoweth, 2020; Müller & Scwarz, 2020). Indeed, his election campaign relied heavily on provoking antagonism toward Muslim people, Hispanic people, immigrants, and other minority groups. In this context, Müller and Scwarz (2020) found that Trump's Islamic-related tweets—which were frequently hostile or antagonistic toward the Muslim population—were associated with increases in anti-Muslim hate crimes. As a whole, this research provides support for the core notion of the legitimization perspective, which is that negative sentiment from the dominant group—and highly revered, political elites, in particular—has the potential to activate and embolden negative out-group sentiment among the general population. When coupled with disease threat, this emboldening discourse has the potential to exacerbate the negative consequences of fear, stoking individual racism. Put simply, when a group is labeled as dangerous or harmful by those in power, individuals are more likely to respond to them as such, and to perceive them as dangerous or harmful.

Fear, Political Rhetoric, and Collective Blame

Taken together, while both mechanisms described thus far—responding to fear with “othering,” and anti-Asian political rhetoric—are different in process, they are similar in their goal: to place collective blame for the COVID-19 pandemic on a racial outgroup. “Othering” is inherently facilitated by the placement of blame and responsibility on an outgroup which, in the context of the COVID-19 pandemic, is Asian people (Nelkin & Gilman, 1988). The implicit goal of negative race-centered COVID-19 discourse is also to place—or displace—blame; indeed, some scholars have posited that the incitement of negative outgroup attitudes is intentional, with the goal of displacing blame. Gagnon (1995) argued that elite political figures are aware of their power and influence, such that they engage in negative outgroup discourse so as to intentionally incite violent intergroup conflict, because this conflict tends to serve a distractive function which draws attention away from other contentious or politically charged topics. Tolnay and Beck (1995) similarly noted that White political elites in the South benefited from intergroup conflict between the Black population and poor White population. In the context of the COVID-19 pandemic, the displacement of blame onto the Asian population—and Chinese people especially—may very well have served the dual function of assigning responsibility to an “other,” while simultaneously drawing attention away from the governmental response to the pandemic itself, which was perceived by many to be ineffective, or inefficient (Kim & Kreps, 2020; Miller et al., 2020; Rosenberg, 2021).

When viewed within the context of research on collective blame and retribution, this displacement of blame may have serious consequences. Lickel and colleagues (2006) argued that vicarious retribution, which is when a member of a group commits an act of aggression toward an outgroup member in response to a provocation or perceived provocation against other ingroup members, is often directed at outgroup members who have no direct influence on the perceived grievance. The first stage of this vicarious retribution framework, in particular, focuses on the initial event categorization and “act construal,” wherein individuals identify who caused the event, the “malevolence of their intentions,” and the degree of harm inflicted by the event. Applied to the COVID-19 pandemic, former President Trump and other major political figures simplified this part of the process, by explicitly telling their followers who they believed was responsible, and overtly blaming Asian people for the pandemic (see Fisher et al., 2019; Holland & Fermor, 2017). In this context, Lickel and colleagues (2006) also note that social influence from prominent ingroup members can have an important influence on this interpretation.

The Role of Information Consumption

aken together, existing research has suggested that both disease threat-related fear and political legitimization may be important facilitators of anti-Asian prejudice during the COVID-19 pandemic. There is likely significant variation, however, in individual susceptibility to these mechanisms, a notion that has received far less attention. Recent research by Mandalaywala and colleagues (2021), for example, has demonstrated that some factors—like intergroup contact (see Pettigrew et al., 2010)—can mitigate the relationship between fear of COVID-19 and anti-Asian prejudice. As outlined previously, the social comprehension of disease is often rooted in prejudice, stigma, and the ostracization of an outgroup (Clissold et al., 2020); the “strength” of these roots varies, however, and one factor that is likely to be particularly important in predicting variation in individual susceptibility is the consumption of COVID-19-related information, which might reasonably be conceptualized as a proxy for individual access to alternative explanations and narratives.

Following this, the current research suggests that the consumption of information regarding the pandemic—which is likely to include information about the origins of the disease, as well as safe practices for reducing risk—might be a critical factor impacting differences in anti-Asian prejudice. There is some evidence for the importance of information in mitigating the effect of fear on negative outgroup attitudes during disease outbreak. Prati and Pietrantoni (2016), for example, found that increased knowledge and access to factual information during the 2014 Ebola outbreak was associated with decreased prejudice toward African immigrants, while low information, false information, and rumors were associated with increased prejudice. There is good reason, however, to suspect an important impact of information consumption on the legitimizing effect of racialized political discourse. As McVeigh (2004) noted in his seminal theory of structured ignorance, there are certain contexts in which individuals are more likely to be receptive or accepting of racist white supremacist discourse. While McVeigh was referring to neighborhood or contextual structure (e.g., inequality) as an important predictor of susceptibility to racist messaging, it is reasonable to assume that the consumption of factual information, or lack thereof, functions much the same. Specifically, McVeigh argued that contextual features impact receptivity to racist messaging by providing evidence consistent with white supremacist claims, without providing alternative explanations.

Applying a similar theoretical process to racist messaging and political discourse during the COVID-19 pandemic, it follows that (a) those who are exposed to racist political messaging, but are consuming less information regarding the disease—and are thus less exposed to alternative information—are likely to exhibit greater prejudicial attitudes; while (b) those who are exposed to racist messaging, but are consuming more information regarding the disease and are exposed to more alternative information, are likely to exhibit less prejudicial attitudes. In other words, anti-Asian racist rhetoric related to the COVID-19 pandemic is likely to be particularly appealing to those who lack alternative explanations and information sources. Likewise, the impacts of anti-Asian rhetoric are likely to be less potent among those who have access to information that can be used to reject the racist messaging (Nguyen et al., 2020). Put simply, those with more information are likely to be less inclined to diagnose the problem within a racial framework, and to attribute blame for the pandemic to an entire racial outgroup.

Current Study

The primary objective of the current research is to contribute to a more complete understanding of racism and xenophobia directed toward Asian Americans and Asian people during the COVID-19 pandemic, and to understand the role that personal fear, political discourse, and information consumption play in facilitating these attitudes. To that end, the current study employs data from a web-based opt-in survey administered in May 2020 to respondents residing in the U.S., which assessed COVID-19-related attitudes and behaviors, as well as anti-Asian prejudicial attitudes. Using the information gathered in this survey, this research proceeds in three steps. First, we assess the relationship between personal fear of COVID-19 and anti-Asian prejudice using an indicator of perceptions of the personal risk presented by COVID-19. Second, we examine the role of information consumption in anti-Asian prejudice. Third, we examine the influence of political legitimization mechanisms during the COVID-19 pandemic by assessing differences in anti-Asian prejudice among Trump supporters, compared to others. Finally, we assess the joint relationship between each of these mechanisms, individual support for Trump, and anti-Asian prejudice.

Data and Methods

To examine these relationships, the current research used data from a web-based survey administered to 4,188 American adults throughout the United States (U.S.). The sample was populated and administered using Prime Panels by CloudResearch, a research firm with access to a pool of 50 million participants worldwide across dozens of research platforms. CloudResearch works with researchers to arrange inclusion criteria, and only those who satisfy criteria are able to participate. The survey was designed to be broadly representative of the U.S. population in terms of race/ethnicity and gender. Specifically, a targeted quota sampling approach was used to produce a final sample that mirrored the U.S. population in terms of race/ethnicity based on the American Community Survey (ACS) 2014–2018 five-year estimates of the U.S. racial composition. Because the focus of the current research is on political legitimization and anti-Asian xenophobia, we used data specifically from the 2,054 survey respondents who were willing to share information regarding political attitudes (i.e., who they supported in the 2016 Presidential election) and indicated that they did not identify as Asian. All study procedures were approved by the university institutional review board and all participants were provided with a statement of informed consent prior before beginning the administration of the survey.

Measures

Our primary dependent measure is anti-Asian prejudice, which we measured using an indicator of anti-Asian xenophobia adapted from Van Der Veer et al. (2013). To construct this measure, respondents were asked to report their level of agreement with nine statements aimed at revealing anti-Asian sentiment: “Asian immigration in this country is out of control;” “Asian immigrants cause increase in crimes;” “Asian immigrants take jobs from people who are here already;” “Interacting with Asian immigrants makes me uneasy;” “I worry that Asian immigrants may spread unusual diseases;” “I am afraid that in case of war or political tension, Asian immigrants will be loyal to their own country of origin;” “With increased Asian immigration I fear that our way of life will change for the worse;” “I doubt that Asian immigrants will put the interest of this country first;” and “I am afraid that our own culture will be lost with increases in Asian immigration.” For each statement, respondents were given a 5-point Likert scale ranging from strongly disagree to strongly agree; we then averaged the 5-point scales across each of the indicators to create a summary measure of anti-Asian xenophobia (α = 0.95).

The current research uses three primary independent measures. First, we include a measure intended to assess individual-level fear of COVID-19. Specifically, we account for individual assessments of the personal risk respondents believe they face from COVID-19 using a question asking each respondent: “What level of threat do you think COVID-19 poses to you or your family?” This measure was operationalized as a continuous measure, such that higher values indicate higher assessments of risk. Political attitudes, which serve as a proxy for the potential influence of political legitimization in the current study, were measured using an indicator of voting behavior; specifically, respondents were asked the following: “Did you vote in the 2016 Presidential Election?” Respondents then chose from the following options: “yes,” “no,” “don't remember,” or “prefer not to say.” If the respondent answered “yes,” they were then asked: “If so, who did you vote for?” Respondents chose from “Donald Trump,” “Hillary Clinton,” “other candidate,” or “don't know.” This measure was then operationalized as a dummy measure equal to 1 if the respondent indicated they voted for “Donald Trump” and equal to 0 if the respondent indicated they voted for “Hillary Clinton” or “other candidate.” Those who did not vote or refused to share voting information were excluded from the analyses (see Malcom et al., 2022 for a similar approach). Third, we assess the role of information consumption. Specifically, we account for the amount of information about COVID-19 that the respondent has been exposed to using a question asking: “How much information have you seen, read, or heard about COVID-19?” Respondents were then given the following four options: “nothing at all,” “not very much,” “a fair amount,” or “a great deal.” This measure was then operationalized as a continuous measure, wherein higher values indicate that respondents report seeing, reading, or hearing more about COVID-19 and lower values indicate seeing, reading, or hearing less about COVID-19.

We also included a number of control measures which might be related to our research questions. Respondent race/ethnicity was measured using three questions from the survey, which asked respondents whether they are of Spanish, Latino, or Hispanic origin (yes/no/I don't know); which racial category describes their race (with the option to select more than one); and then which racial category they identify with most (if they indicated more than one racial category in the previous question). Response options for this question included White, Black, Asian, Native Hawaiian or other Pacific Islander, American Indian or Alaska Native, and other race; all respondents who indicated that they identified as Asian were excluded from the study, given our focus on anti-Asian attitudes. All remaining respondents who answered yes to the first question were coded as Hispanic. Respondents were coded as non-Hispanic Black if they indicated Black as one of their racial identities and the racial category they identify with most, coded as non-Hispanic white if they indicated white as one of their racial identities and the racial category they identify with most; and coded as other race if they did not fall into one of these group. White was used as the reference category in all multivariate regression models.

Several additional respondent characteristics are included as control measures as well, including age, whether the respondent has a high school education or less (1 = yes), whether the respondent was employed (1 = yes), and whether the respondent identified as Protestant or Catholic (1 = yes).² Respondents were also asked to indicate their gender/gender identity and were given the option of selecting male; female; transgender male; transgender female; gender variant/non-conforming; other; and prefer not to answer. Gender was then coded such that respondents who identified as “male” or “transgender male” were coded as male (=0), while those who identified as “female” or “transgender female” were coded as female (=1). Because of the small number of observations, all other respondents were coded as missing (n = 13). We controlled for foreign-born status by including a dichotomous measure indicating whether respondents were born in the United States (1 = yes). Next, we control for right-wing authoritarianism (RWA), given the demonstrated relationship between RWA and prejudicial attitudes (e.g., Altemeyer & Altemeyer, 1996). Specifically, we measure this construct using a six-item scale adapted from Bizumic and Duckitt (2018), in which respondents are asked to what extent they agree with each of the following statements: “it's great the many young people today are prepared to defy authority,” (reverse coded), “what our country needs most is discipline, with everyone following our leaders in unity,” “God's laws about abortion, pornography, and marriage must be strictly followed before it's too late,” “there is nothing wrong with premarital sexual intercourse (reverse coded),” “our society does NOT need tough government and stricter laws (reverse coded),” and “the facts on crime and the recent public disorders show we have to crack down harder on troublemakers, if we are going to preserve law and order.” Responses were averaged to create an index ranging from 0 to 4, with higher values signifying more right-wing authoritarian attitudes (α = 0.64). Finally, because perceptions of risk and related attitudes might be related to such behavior, we account for the extent of socializing engaged in at the time of the survey using a continuous measure where higher values indicate more socializing (0 = complete social distancing).

Analytic Strategy

The current research proceeds in four steps. First, we conduct an OLS regression model in which anti-Asian xenophobic attitudes is regressed on our independent measures, in order to assess the main effects of each of our primary independent measures. Second, we include all independent variables and an interaction between perceptions of COVID-19 risk and political attitudes. Third, we include all independent variables and an interaction between information consumption and political attitudes, respectively. Finally, we present each of these interactions graphically.

Results

Descriptive statistics for all variables are presented in Table 1. Briefly, the average score for anti-Asian xenophobia in the sample was roughly 1.253 (SD = 0.970). Regarding perceptions of risk presented by the COVID-19 pandemic, on average, respondents indicated the belief that COVID-19 presented a moderate to high risk level (x̅ = 2.373, SD = 1.188). Respondents also indicated that they have seen, read, or heard—on average—somewhere between a fair amount and a great deal of information about the COVID-19 pandemic (x̅ = 2.716, SD = 0.492). Finally, roughly 44.2% of the sample indicated that they voted for former President Donald Trump in the 2016 Presidential election.

Table 1.

Descriptive Statistics (N = 2,054).

Variable	Mean	SD	Min	Max
Anti-Asian xenophobia	1.253	0.970	0	4
COVID-19 risk	2.373	1.188	0	4
Information	2.716	0.492	0	3
Trump support	0.442		0	1
Individual Characteristics
Black	0.172		0	1
Hispanic	0.175		0	1
Other race/ethnicity	0.037		0	1
Age	47.941	15.388	19	99
Female	0.451		0	1
High school education or less	0.133		0	1
Protestant	0.554		0	1
Employed	0.600		0	1
Native-born	0.957		0	1
Right-wing authoritarianism	1.971	0.742	0	4
Social distancing	1.631	1.120	0	4

Abbreviations: SD = standard deviation; Min = minimum value; Max = maximum value.

Results from the multivariate regression analyses examining the relationship between perceptions of COVID-19 risk, information consumption, and political attitudes are presented in Table 2. Results are presented in three models; Model 1 presents the main effects of each of the independent measures, net of controls, while Model 2 and Model 3 present the same model with the addition of interaction terms between perceptions of risk and political attitudes, and information consumption and political attitudes, respectively.

Table 2.

Correlates of Anti-Asian Xenophobic Attitudes, OLS Regression (N = 2,054).

Variable	Model 1: Main Effects			Model 2: Risk*Trump Support Interaction			Model 3: Information*Trump Support Interaction
	Coef.	SE	p-value	Coef.	SE	p-value	Coef.	SE	p-value
COVID-19 risk	0.086	0.017	***	0.025	0.024		0.087	0.017	***
Information	−0.149	0.040	***	−0.141	0.040	***	−0.294	0.053	***
Trump support	0.428	0.046	***	0.147	0.090		−0.467	0.217	*
Risk*Trump support				0.120	0.033	***
Info*Trump support							0.331	0.079	***
Individual Characteristics
Black	0.166	0.056	**	0.162	0.056	**	0.158	0.056	**
Hispanic	−0.057	0.054		−0.054	0.054		−0.060	0.054
Other race/ethnicity	−0.092	0.104		−0.084	0.103		−0.100	0.103
Age	−0.003	0.001	*	−0.003	0.001	*	−0.003	0.001	*
Female	−0.059	0.040		−0.061	0.039		−0.055	0.039
High school education or less	0.115	0.058	*	0.117	0.058	*	0.108	0.058
Protestant	0.097	0.041	*	0.100	0.041	*	0.095	0.041	*
Employed	−0.026	0.043		−0.029	0.043		−0.032	0.043
Native-born	0.193	0.096	*	0.194	0.096	*	0.180	0.096
Right-wing authoritarianism	0.331	0.029	***	0.329	0.029	***	0.326	0.029	***
Social distancing	0.110	0.018	***	0.110	0.018	***	0.109	0.018	***
Constant	0.373	0.181	*	0.499	0.184	**	0.790	0.206	***

Abbreviations: Coef. = coefficient; SE = standard error; *p < 0.05; **p < 0.01; ***p < 0.001.

Beginning with Model 1, there are three important results worth noting. First, as hypothesized, perceptions of COVID-19-risk are positively and significantly associated with more anti-Asian xenophobic attitudes (b = 0.086, p < 0.001). In other words, those who indicate that they perceive COVID-19 to be a greater risk to their personal safety also report higher anti-Asian prejudicial attitudes, net of controls. Second, information consumption is negatively and significantly associated with anti-Asian xenophobia (b = −0.149, p < 0.001), such that those who report consuming, or being exposed to, more information about the COVID-19 pandemic—through the news media and other sources—generally report lower anti-Asian xenophobic attitudes. Finally, one of the strongest predictors of anti-Asian xenophobic attitudes during the COVID-19 pandemic is individual political attitudes: those who reported supporting former President Trump reported significantly higher anti-Asian xenophobic attitudes, relative to those who did not support Trump (b = 0.428, p < 0.001). While not the central focus of this study, there were several other important individual predictors as well; anti-Asian attitudes were, on average, higher among younger respondents, those with lower education levels, native-born respondents (compared to non-U.S.-born respondents), and those with more right-wing authoritarian attitudes. We observe no statistically significant gender differences in anti-Asian xenophobic attitudes, suggesting some support for recent research pushing back on the common presumption that hateful attitudes and behavior (i.e., hate crime) are largely the purview of men (see Lantz, 2022).

Next, Model 2 and Model 3 include the interaction terms between (a) perceptions of risk and individual support for Trump; and (b) information consumption and individual support for Trump, respectively. The results from Model 2 indicate that the interaction between perceptions of risk and support for Trump is positive and significant (b = 0.120, p < 0.001), indicating that the nature of the association between perceptions of risk and anti-Asian xenophobia depends on whether an individual is a Trump supporter; this relationship is presented graphically in Figure 1. There are several important patterns worth noting in this figure. First, on average, Trump supporters exhibit greater anti-Asian xenophobic attitudes than non-Trump supporters, regardless of individual perceptions of the risk of COVID-19. Second, differences in perceptions of COVID-19 risk have very little substantive impact on non-Trump supporters; indeed, although the trend line for non-Trump supporters appears to depict a slightly positive trend, the conditional effect of COVID-19 risk in Model 2 indicates that perceptions of COVID-19 risk are not significantly related to anti-Asian attitudes among non-Trump supporters. Finally, increases in perceptions of COVID-19 risk have a much greater impact on anti-Asian attitudes among Trump supporters, relative to non-Trump supporters. Taken together, anti-Asian attitudes are greatest among those individuals who perceive a high degree of risk from COVID-19 and are Trump supporters, and lowest among non-Trump supporters who perceive a low degree of risk from COVID-19.

Figure 1.

Relationship between perceptions of risk and anti-Asian xenophobic attitudes among Trump supporters and non-Trump supporters.

Next, the results from Model 3 indicate that the interaction between information consumption and Trump support is positive and significant (b = 0.331, p < 0.001), indicating that the nature of the association between information consumption and anti-Asian xenophobia depends on whether an individual is a Trump supporter; to aid in interpretation, this relationship is presented graphically in Figure 2. There are at least three important findings here. First, the highest predicted probability of anti-Asian attitudes is, surprisingly, among non-Trump supporters who have not consumed any information regarding COVID-19. Second, as an important qualification on the previous finding, the lowest predicted anti-Asian attitudes are, on average, among those respondents who consumed a great deal of information regarding COVID-19 and did not support Trump; taken together, these results imply a substantial “information effect” among non-Trump supporters, such that increased information regarding COVID-19 may serve to decrease prejudicial attitudes. Finally, and most importantly, the results indicate that information consumption has virtually no measurable impact on prejudicial attitudes among Trump supporters; in other words, Trump supporters exhibit similar levels of anti-Asian prejudice regardless of whether they indicate consuming no information at all regarding COVID-19, a great deal of information regarding COVID-19, or some amount of information in between.

Figure 2.

Relationship between information about COVID-19 and anti-Asian xenophobic attitudes among Trump supporters and non-Trump supporters.

Discussion

Since the start of the COVID-19 pandemic, there has been significant attention to pandemic-related increases in bias and discrimination toward Asian people; very few existing studies, however, have been able to assess the potential mechanisms behind these increases during this pandemic, or during mass disease events more generally. Following this, the current study attempts to assess and disentangle the influence of fear of disease, information consumption, and political legitimization on anti-Asian prejudicial attitudes. In doing so, we posit that the combination of (a) the scope and impact of the COVID-19 pandemic; (b) differential information (and misinformation) consumption regarding the disease; and (c) xenophobic political discourse linking Asian people to COVID-19, has contributed to a dangerous social climate in the U.S. wherein each of these factors has likely contributed both independently and—most importantly—jointly to increased anti-Asian racism in the United States. Taken together, our results largely support these hypotheses and point to four important findings.

First, individual perceptions of risk from COVID-19—a construct we interpret as analogous to individual fear of infection—is significantly and positively associated with greater anti-Asian prejudicial attitudes, net of controls. In other words, our study suggests further evidence for the impact of disease threat in facilitating prejudicial attitudes toward Asian people, adding to a growing body of research demonstrating the dangerous disease-related stigmatization of the Asian population. As Faulkner et al. (2004) and others have suggested, this relationship provides further evidence for the tendency to rely on xenophobic stereotypes of Asian people as disease carriers in order to rectify personal feelings of vulnerability. Those who perceive themselves as more vulnerable exhibit, on average, more prejudicial attitudes toward Asian people.

Second, those respondents who consumed more information about COVID-19 generally exhibited lower anti-Asian attitudes, on average. As Mandalaywala et al. (2021) noted, there was substantial uncertainty about the actual severity of COVID-19 in the early months of the pandemic. It is well documented that there is a link between fear, uncertainty, and prejudicial attitudes. Information, however, reduces uncertainty. For those respondents who were primarily exhibiting anti-Asian attitudes as a response to uncertainty—however inappropriate—information may have served to reduce uncertainty, thereby also reducing their prejudicial attitudes. Put another way, varying levels of information consumption can promote differential understandings of social problems. In the context of the COVID-19 pandemic, accurate information is essential for individuals to establish an accurate understanding of the disease. The lack of information—or the consumption of misinformation—prohibits understanding.

Third, we find that one of the single strongest predictors of anti-Asian prejudicial attitudes during the early months of the pandemic was individual political attitudes in the form of support for former President Donald Trump. Put simply, support for Trump was strongly associated with increased prejudicial attitudes, net of controls. And, while we recognize that voting for Trump in the 2016 Presidential election does not necessarily mean that one is still receptive to his anti-Asian rhetoric in 2020, it is hardly a stretch—given that anti-minority rhetoric was a feature of both his 2016 Presidential campaign and political tenure more generally—to believe this measure to be an effective proxy measure for individual receptivity to this anti-Asian rhetoric. Viewed within this context, this research suggests strong support for the role of “political legitimization” or “emboldenment” in exacerbating anti-Asian attitudes during the pandemic. While prior research has examined potential emboldenment effects among bias toward specific populations and found some mixed support, Dugan and Chenoweth (2020) emphasize the need to disaggregate these impacts across groups, rather than assuming that this mechanism operates uniformly for all groups. The current research provides important evidence for the emboldening of bias and prejudice toward Asians and Asian Americans.

Finally, we observe significant interactions between support for Trump, fear of COVID-19, and information consumption. Results reveal a complex interaction between each of these factors, such that (a) Trump supporters who report higher levels of personal risk report particularly high levels of anti-Asian prejudice; and (b) the protective effect of increased information is significantly muted among Trump supporters, compared to non-Trump supporters. Put simply, some people feel more vulnerable to COVID-19 than others, and our research indicates that those who perceive themselves to be more vulnerable not only exhibit more anti-Asian xenophobic attitudes, but may also display increased susceptibility to the legitimizing and emboldening exhibited by Trump and other politicians. While negative rhetoric toward certain groups was a feature of Trump's political tenure, our research suggests that the combination of this rhetoric with individual fear had particularly severe consequences. In other words, the discourse surrounding Asian people in the early months of the COVID-19 pandemic was likely amplified when combined with higher levels of individual fear. This focused negative attention—from the highest public office in the United States—likely has a profound impact on prejudicial attitudes toward that group.

Unfortunately, our results indicate that this pattern is also coupled with a decreased susceptibility to the otherwise beneficial effects of information consumption. Past research has indicated that Trump's base was largely unwavering in response to information which otherwise undermined Trump (Baum-Baicker, 2020; Brady et al., 2017; Fording & Schram, 2017; Reny & Barreto, 2020); this research suggests this unwavering response to information may extend to information about the COVID-19 pandemic. Another, more nefarious explanation for these patterns might lie in the differential consumption of misinformation. Specifically, when considered within the context of existing empirical research showing that misinformation and the consumption of misleading news is heavily concentrated among conservatives (e.g., Grinberg et al., 2019; Guess et al., 2019; Lee, 2010), it is reasonable to conclude that one potential explanation driving these differences is likely to be increased consumption of misinformation among Trump supporters, relative to non-Trump supporters. And, while we cannot measure this directly, such an explanation would be consistent with the patterns we observe, including: (1) reductions in prejudicial attitudes among non-Trump supports, who on average consume less misinformation; and (2) the absence of an observable relationship among Trump supporters, who on average consume more misinformation. Put simply, misinformation is unlikely to have the same effects as factual information, and if Trump supporters are more likely to consume this misinformation than others, as previous research suggests, then our results suggest further evidence of the dangerous consequences of the proliferation of misinformation in recent years. Put another way, our results may indicate that Trump supporters do not, on average, exhibit different prejudicial attitudes based on information exposure because they are not actually consuming factual information at all, but are instead consuming misinformation.

Taken together, these results have several important policy implications. First, given the potential influence of anti-Asian political discourse, our results highlight the importance of messaging from prominent politicians during global health crises. Put simply, it is critical that political figures and others in positions of authority conduct themselves in positive, non-prejudicial ways. The absence of negative rhetoric is not enough, however. It is important not only that these figures not engage in xenophobic rhetoric, but also that they provide strong positive messaging that emphasizes that such attitudes—and related behavior, like hate crimes—are not tolerated. Clear condemnation of xenophobia is an essential measure for countering the attitudes we observe here (Lunn et al., 2020). Second, anti-Asian prejudicial attitudes were surprisingly frequent, even pervasive, in our sample, and research has suggested both that consistent exposure to bias and discrimination may be related to acute mental health consequences, depression, or event desensitization (see Lantz et al., 2022; Wenger et al., 2022; Wenger & Lantz, 2021), and that Asian victims of discrimination are less likely than others to seek help after victimization (Lantz & Wenger, 2021). Following this, researchers and policymakers should make efforts to develop appropriate interventions aimed at addressing these consequences.

Third, these results point to the pressing need to both emphasize fact-based messaging surrounding risk during a global health crisis, and to address the pervasive nature of misinformation. Efforts in this regard need to be proactive, not just reactive. Congress, for example, recently responded to the increase in anti-Asian hate crime incidents by passing the COVID-19 Hate Crimes Act, which is intended to increase resources for responding to pandemic-related hate crimes. Yet, while the symbolic impact of this legislation is important, our research suggests that such approaches are unlikely to be enough. Instead, it is critical that we attend to the more deeply-seeded prejudicial attitudes that motivated such hate crimes. In this regard, the use of positive messaging and factual information—combined with a concentrated effort to combat misinformation—may be a promising avenue from a policy perspective. Efforts to counter anti-Asian stigmatization should focus on communication efforts aimed at providing factual information about the spread of the disease, both as COVID-19 continues to spread, and in the context of future pandemic-level health crises.

Conclusion

Medical historian and physician Markel (1999) famously observed that: “society has no shortage of means available to dehumanize “undesirable” groups. The grave risks of this process are magnified when combined with the threat of infectious disease” (p. 61). Our results suggest that these processes are even further magnified when high-profile political figures employ negative and prejudicial rhetoric toward outgroups and actively associate those groups with the infectious disease. When taken together, this research suggests that the social and political context of COVID-19 combined to create a dangerous climate for the proliferation of anti-Asian prejudice. Unfortunately, however, the implications of these results are unlikely to be limited only to the COVID-19 pandemic. Inflammatory, racist, political rhetoric is, regrettably, not exclusive to discourse surrounding global pandemics. And, as some scholars have noted, COVID-19 is but one—albeit particularly extreme in magnitude—example of a pandemic, and global pandemics are likely to become more frequent in the future (see Jones et al., 2008). Moving forward, it is thus critical that those in high-profile political positions are careful to condemn xenophobia and otherwise engage in positive discourse toward racial/ethnic minority groups whenever possible, but especially in social contexts in which misinformation is prolific and individual concerns about health and safety are high. Only then can we hope to avoid the proliferation of intergroup conflict and prejudicial attitudes when facing the next global health crisis.

Author Biographies

Brendan Lantz is an assistant professor of Criminology and Criminal Justice and Director of the Hate Crime Research and Policy Institute at Florida State University. He received his Ph.D. from the Pennsylvania State University, during which time he was a Bureau of Justice Statistics Graduate Research Fellow. He was the recipient of the 2020 Academy of Criminal Justice Sciences (ACJS) Victimology New Scholar Award and the 2021 ACJS Academy New Scholar Award. His research focuses on hate crime, violence, victimization, and race/ethnicity, and has been funded by the National Institutes of Health, National Institute of Minority Health Disparities, National Science Foundation, and Bureau of Justice Statistics (BJS), among others. He has published more than 30 articles appearing in a variety of research outlets, including Social Problems, Journal of Research in Crime and Delinquency, Justice Quarterly, British Journal of Criminology, and Psychology of Violence, among others.

Marin R. Wenger is an associate professor in the College of Criminology and Criminal Justice at Florida State University. Her research has been funded by the National Institutes of Health (NIH) and her research interests include race/ethnicity, stratification, hate crime, communities and crime, and quantitative methods. Her recent work has appeared in various research outlets, including Justice Quarterly, Social Problems, Journal of Experimental Criminology, Crime & Delinquency, Social Science Research, Psychology of Violence, and Law and Human Behavior.

Jack M. Mills is a doctoral student in the College of Criminology and Criminal Justice at Florida State University. His primary research interests include prejudice, hate crime, communities and crime, and political systems.