Racial resentment and support for COVID-19 travel bans in the United States

Abstract

Travel bans were a globally prevalent policy response to the COVID-19 pandemic. In the United States, travel bans against China and European countries proved a broadly popular mitigation tool among Americans. Why did Americans support COVID-19 travel bans? We fielded two novel survey experiments, surveying 3000 American citizens across five waves (between March 2020 and March 2021). In randomizing the country of origin of those potentially subject to travel ban measures, we find consistent evidence that racial attitudes drive support for travel bans. The strength of this relationship varies across political parties and across hypothetical target countries but is not explained by objective caseloads that change across countries and over the course of the pandemic.

On 31 January 2020, President Trump introduced a travel ban against China to slow the spread of the novel coronavirus SARS-CoV-2 into the United States. Bans against foreign nationals traveling to the US from Europe soon followed in March. Travel restrictions can be effective in slowing viral spread during global pandemics (Grépin et al., 2021), but they may also activate latent xenophobic attitudes against their targets (Adida et al., 2018). The COVID-19 pandemic was no exception, with elites othering racial minorities, connecting them to the virus itself (Reny and Barreto, 2020; Dionne and Turkmen, 2021). President Trump popularized racialized rhetoric referring to it as the “Chinese virus,” and anti-Asian attitudes and violence increased substantially during the early months of the pandemic (Chan et al., 2021).

We study racial attitudes and American support for COVID-19 international travel bans using a panel survey of 3000 Americans between March 2020 and March 2021, which included two novel survey experiments on support for entry bans as a COVID-19 mitigation policy. In the first experiment in March 2020, respondents were asked their level of support for an entry ban and randomly presented with a proposed entry ban targeting China, Italy, or Great Britain—the first two of which were experiencing peaks in COVID caseloads at the time. To reflect the evolution of the pandemic and the emergence of new global hotspots, in four subsequent waves respondents indicated their support for a ban from China, Great Britain or Brazil, a country experiencing surges at the time. This study design allows us to examine support for entry bans by target country amidst changing pandemic threat conditions.

We find that support for travel bans is shaped by racial attitudes and does not correspond to objective levels of threat from the target countries. Specifically, racial resentment predicts higher support for travel bans regardless of the country to which they are applied. As attitudes on race and immigration vary across parties (Abramowitz and McCoy, 2019), we also test whether partisanship conditions support for border closures, finding that both partisanship and racial attitudes are stronger predictors of entry ban support than caseloads.

Our findings provide new evidence to a growing literature showing the COVID-19 pandemic has not softened American attitudes toward immigration (Reny and Barreto, 2020, Esses and Hamilton, 2021). We contribute to these studies by showing individuals maintain racialized human mobility preferences despite objective threat conditions. Tough immigration rules are an expression of border insecurity (Kenwick and Simmons, 2020) and in future crises, like security threats or climate change, willingness to support migrants may similarly be driven by racial attitudes.

1. What shapes support for travel bans?

International border closure to China was among the first visible policy steps taken by the Trump Administration to mitigate the COVID-19 pandemic, following by a rollout of travel bans in response to emerging global peaks in the pandemic. By mid-March 2020, the entry ban was extended to include 38 countries. Globally, 127 countries had similar no-entry policies in place (Dzankic et al., 2022).

The China travel ban was popular among Americans. Even as early as 2–4 February 2020, which precedes the (known) outbreak of COVID in the United States by a month, 51 percent of Americans supported denying entry to foreigners who have coronavirus (17 percent reporting “not sure”) and 41 percent of Americans supported blocking admittance to individuals traveling from China (32 percent “not sure”).¹ In emergencies, the public gives presidents more leeway to make policy, especially in foreign affairs, and often presidential approval increases (Mueller, 1973). But political actors remain sensitive to domestic public opinion. Even then-candidate Joe Biden backed the travel ban, with his deputy campaign manager carefully noting “Science supported this ban, therefore he did too.”²

We consider two broad explanations to account for public support for travel bans. First, in the US, immigration attitudes are specifically shaped by group attitudes (Nelson and Kinder, 1996). For instance, negative views of Latino/a/x people increase support for restrictive immigration policy generally (Valentino et al., 2013; Pérez, 2016). We draw on a rich literature showing how racial attitudes shape public opinion, including vote choice (Tesler, 2016) but also attitudes beyond race-based policies, exhibiting what Tesler (2012b) calls a “spillover of racialization”. Work in this vein specifically illustrates how racial resentment, measured as attitudes about Black Americans, shapes opposition to immigration (Kinder and Sanders, 1996; Kinder and Kam, 2010).³

We therefore expect those with high racial resentment to exhibit higher support for COVID travel bans. As these attitudes vary by the national group in question, so too will support for travel bans vary by target country in the context of a global pandemic. Specifically, we expect those higher in racial resentment to support bans against China and Brazil more strongly than they do predominately white, high-income European countries (Great Britain, Italy). In this approach, China and Brazil represent racial outsider groups for white Americans, compared to European countries.

Our second theoretical expectation is that individuals support travel bans in response to their fears about the pandemic. Behavioral immune system theory predicts that individuals seek to mitigate risk (real or perceived) and participate in high pathogen-avoidance behavior, including taking on conservative attitudes (Rodriguez et al., 2022) and opposing immigration (Aarøe et al., 2017). Large, disruptive events—including terrorism (Huddy et al., 2005), economic crises (Creighton et al., 2015), and previous pandemics like Ebola (Adida et al., 2018)—create uncertainty and anxiety, which can lead to patriotic rallying but also negative attitudes toward outsiders like immigrants (Hellwig and Sinno, 2017), as well as higher support for restrictive policies (Albertson and Gadarian, 2014). And where work from Pew Research Center showed that as COVID-19 infection rates and death tallies climbed, individuals reported greater health concerns (2020) and an aggregate increase in worries (2021), we might expect protectionist attitudes. Therefore, we predict Americans will support bans during a pandemic against travelers from countries experiencing high caseloads.

In testing the effects of racial attitudes and pandemic threat on support for travel bans, we include partisanship as a moderator. Partisanship is a social identity comprised of an increasingly aligned set of attitudes and positions (Mason, 2018) and is a significant explanation for differences in public attitudes toward immigration policy (Levy and Wright, 2020), including during health scares (Adida et al., 2018). Evidence from the pandemic already shows individuals follow ideological positions in evaluating border policy (Williams et al., 2022). We therefore expect that the effects of racial attitudes and pandemic threat will vary by party affiliation: the relationship between racial resentment and support for travel bans will be higher among Republicans than Democrats, whereas the relationship between pandemic threat and support for travel bans may be stronger among Democrats than Republicans.

2. Research design

To examine American support for COVID-19 travel bans, we first fielded a large survey of American adults (N = 3000) between March 20 and 23 March 2020, collected by YouGov using its standard methodology (see the Appendix for sample details and recruitment procedures). We fielded a revised version of the survey experiment re-interviewing the same respondents four times: 6–25 June 2020 (Wave 2), 4–14 August 2020 (Wave 3), 15–21 October 2020 (Wave 4), and 24 March–5 April 2021 (Wave 5). The five-panel wave structure allows us to follow support for entry bans by country of origin over time.

In Wave 1, we embedded an experiment to investigate whether support for travel bans varied by the target country. Initially focusing on China, Italy, and Great Britain enables us to distinguish between racial attitudes and pandemic threat, as both Italian and Chinese outbreaks peaked in caseloads during our first survey wave, whereas Great Britain's first peak occurred over a month later (See Figure S1, though early peak sizes pale in comparison to later waves). Figure S1 also shows that global hotspots changed as the pandemic wore on. As rates in Italy, China, and Great Britain began to decline, cases in Brazil started to surge, leading it to be added to the U.S. entry ban list on 24 May. In Waves 2–5, respondents were randomly assigned to consider a travel ban from China, Great Britain, and Brazil (with Italy omitted).

Our dependent variable measures respondents' support for policies that restrict entry of citizens to manage the spread of COVID-19 in the United States. In Wave 1, the question reads: “The United States must continue to ban the entry of citizens of [China/Great Britain/ Italy] into the United States.” In Waves 2–5, the question replaces Brazil for Italy. Response options ranged from 1 to 5 on a Likert scale, where 1 indicates “strongly disagree” and 5 indicates “strongly agree.” Our treatment variable is the group to which this policy would be applied, represented by the terms in italics above. These were randomized with equal probability across all respondents. We report balance tests among treatment groups across all five survey waves in the Appendix (see Table S1).

We estimate the effects of varying targets countries on support for entry bans using ordinary least squares regression with robust standard errors. Let State ∈ {China, Italy, Great Britain} denote the randomly assigned state to which the entry ban is applied. Our baseline empirical model for Wave 1 is

(1)$$Y = \alpha + \beta _1{\rm State} + \gamma X + \varepsilon $$

Our baseline results adjust for no control variables. In further analyses, the elements of X adjust for pretreatment covariates: respondent age, gender, race, marital status, education, income, urban-rural status, and state of residence.

To explore heterogeneity in treatment effects by racial resentment and partisanship, we interact the treatment variable with measures of racial resentment and partisanship. We measure partisanship as a categorical variable of Democrats (with leaners), Republicans (with leaners), and Others (i.e., Independents and those without a partisan identification). We measure racial resentment using a survey item asking respondents the extent to which they agree or disagree with the view that, like other minorities, Black Americans should work their way up without special favors (for the complete question wording, see Appendix Table S2). This item is highly correlated with the other questions in the racial resentment scale (r = 0.75 in the 2020 American National Election Studies) and has been used as a proxy for the full scale in prior research (Tesler, 2012a). In Appendix Table S3, we validate our racial resentment measure as a predictor for general outgroup dispositions like anti-immigrant attitudes, where racial attitudes are tightly correlated to xenophobia and other ethnocentric positions (Kinder and Kam, 2010).

Let Party ∈ {Republican, Democrat, Other} denote the respondent's party and RR represent our measure of racial attitudes. Our model is

(2)$$\eqalign{Y = & \,\alpha + \beta _1{\rm State} + \beta _2{\rm Party} + \beta _3{\rm State} \times {\rm Party} + \beta _4{\rm RR} \cr & \quad + \beta _5{\rm RR} \times {\rm Party} + \beta _6{\rm RR} \times {\rm State} + \beta _7{\rm RR} \times {\rm State} \times {\rm Party} + \gamma X + \varepsilon } $$

which allows us to estimate a separate effect of State for members of each party and across the levels of RR. We include the triple interaction because we expect the relationship between racial resentment and travel bans to vary by party and target country. Although random assignment allows us to estimate the effect of State without controls, including X helps to ensure that the Party- and RR-specific effects that we estimate do not reflect respondents' other demographic characteristics. The survey items asking about racial resentment appeared prior to the experiment to ensure that they capture respondents' pre-treatment attitudes.⁴

Last, for our four-wave (Waves 2–5), over-time analysis, we estimate least squares regressions that interact treatment status, partisanship, and RR with survey wave. With i denoting individuals and t waves, we estimate

(3)$$\eqalign{Y_{{\rm it}} = & \,\alpha + \beta _1{\rm Stat}{\rm e}_{{\rm it}} + \beta _2{\rm Part}{\rm y}_{\rm i} + \beta _3{\rm R}{\rm R}_{\rm i} + \beta _4{\rm Wav}{\rm e}_{\rm t} + \beta _5{\rm Stat}{\rm e}_{{\rm it}} \times {\rm Part}{\rm y}_{\rm i} \cr & \quad + \beta _6{\rm Stat}{\rm e}_{{\rm it}} \times {\rm R}{\rm R}_{\rm i} + \beta _7{\rm Stat}{\rm e}_{{\rm it}} \times {\rm Wav}{\rm e}_{\rm t} + \beta _8{\rm Part}{\rm y}_{\rm i} \times {\rm R}{\rm R}_{\rm i} + \beta _9{\rm Part}{\rm y}_{\rm i} \cr & \quad \times {\rm Wav}{\rm e}_{\rm t} + \beta _{10}{\rm R}{\rm R}_{\rm i} \times {\rm Wav}{\rm e}_{\rm t} + \beta _{11}{\rm Stat}{\rm e}_{{\rm it}} \times {\rm Part}{\rm y}_{\rm i} \times RR_{\rm i} + \beta _{12}{\rm Stat}{\rm e}_{{\rm it}} \cr & \quad \times {\rm Part}{\rm y}_{\rm i} \times {\rm Wav}{\rm e}_{\rm t} + \beta _{13}{\rm Part}{\rm y}_{\rm i} \times {\rm R}{\rm R}_{\rm i} \times {\rm Wav}{\rm e}_t + \beta _{14}{\rm Stat}{\rm e}_{{\rm it}} + {\rm Part}{\rm y}_{\rm i} \times {\rm R}{\rm R}_{\rm i} \cr & \quad \times {\rm Wav}{\rm e}_{\rm t} + \gamma X_{\rm i} + \varphi _{\rm i} + \varepsilon _{{\rm it}}} $$

Individual random effects φ_i capture individual-level heterogeneity.⁵

To test our threat hypothesis, we consider the threat from caseloads in the countries mentioned in the experiment. In Waves 2–5 we measure objective threat through COVID-19 caseloads, using data on the smoothed daily number of new cases, collected by ourworldindata.org and represented in logarithmic terms.⁶ To model how new cases condition our results, we replace Wave_t in equation (3) with Cases_State,t, which records the number of cases in the randomly assigned state at the beginning of that survey wave. We analyze these data using random effects regressions that interact target country (the treatment status) with that country's caseload and racial resentment. We measure objective caseloads as the daily number of new cases smoothed over a seven-day window.⁷ In the analyses below we include all respondents in our surveys, but to ensure that our findings are not driven by the racial composition of our sample, we repeat all analyses with nonwhite respondents excluded where racial resentment has been found to be most predictive for white respondents (Kam and Burge, 2018). These results can be found in the Appendix (Tables S10-S15).

We use a graphical approach to interpret the results of our analysis, estimating the predicted mean and standard error of each dependent variable for combinations defined by State, Party, Wave (where applicable), and RR dichotomized around its sample median.

3. Results

Our main estimates from Wave 1 are that respondents expressed modestly greater support for entry bans against China relative to Great Britain (b = 0.112, S.E. = 0.058, p = 0.056), and significantly greater support for entry bans against Italy relative to Great Britain (b = 0.272, S.E. = 0.057, p < 0.001) (full unadjusted and adjusted results are available in the Appendix, Table S4). Figure 1 visualizes support for entry bans across the three treatment arms. If respondents evaluated the actual risk of COVID from caseloads, we expect that support for travel bans would be higher when considering China and Italy compared to Great Britain.

Figure 1. Predicted support for entry bans, March 2020.

We find high support for an entry ban for all three countries, with little differentiation based on threat (though support for a ban against Italians is marginally greater than Great Britain).

We next examine support for bans across countries that vary on COVID caseloads at different levels of racial resentment. We expect racial resentment to matter the most for bans against China compared to Western European countries. (Kinder and Kam, 2010; Tesler, 2012a). To facilitate interpretation, we plot the predicted level of support for entry bans across all combinations of treatment and moderators (dichotomized around the sample median). Our main results for how racial resentment and partisanship moderate support for bans in March 2020 (Wave 1) may be found in Figure 2 (full statistical results may be found in the Appendix, Table S5).

Figure 2. Predicted support for entry bans by racial resentment, March 2020.

Across all three partisan groups, there is greater support for entry bans among respondents with high racial resentment (see upper panel of Figure 2), with partisan differences in support for bans against specific countries. Among low-RR Republicans, entry ban support is highest against Italy and China. This pattern differs for low-RR Democrats and Others, who exhibit weaker support for entry bans overall, and higher support for bans against Italy relative to China or Great Britain. High-RR respondents of any party most strongly support bans against Chinese and Italians, with lower support for bans against Great Britain (although this difference is only significant among high-RR Republicans). While racial resentment is associated with higher support for travel bans across respondents, heterogeneity across parties and target countries reveals that racial resentment explains attitudes about British—but not Italian—travel bans.

Our over-time analysis, replacing Italy with Brazil, offers useful leverage for assessing the persistence of racial attitudes in the context of changing threat. Aggregating over the four waves, in an unadjusted random effects specification, we find significantly greater support for entry bans against China (b = 0.204, S.E. = 0.032, p < 0.001) and Brazil (b = 0.387, S.E. = 0.028, p < 0.001) relative to Great Britain (full statistical results for both adjusted and unadjusted results appear in the Appendix, Table S6).

Modeling heterogeneity by RR, partisanship, and survey wave (Figure 3; full results in the Appendix, Table S7), we find that high-RR Republicans express the highest level of support for an entry ban against China, followed closely by Brazil and then Great Britain. For low-RR Republicans, the order is similar but the differences between groups are smaller and statistically indistinguishable in later waves. High-RR Democrats distinguish among targets of an entry ban early in the pandemic, but not by wave 5. Low-RR Democrats—in contrast to Republicans—express higher support for a ban against Brazil than either Great Britain or China; higher support against Brazil may be a response to objective pandemic threat (see below) while low support against China may be a rejection of Trump's signature COVID policy. Low-RR Others are indistinguishable from low-RR Democrats, and high-RR Others are comparable to high-RR Republicans.

Figure 3. Predicted support for entry bans by partisanship and racial resentment, June 2020–April 2021.

To what extent are these opinions shaped by objective threat? New cases not only reflect pandemic conditions, but they also reflect greater salience of the pandemic's effects in certain countries. In estimating the relationship between caseloads and support for entry bans across four survey waves from June 2020 to March 2021 (Figure 4; full statistical results in the Appendix, Table S8), we find little evidence that American responses are shaped by objective conditions of COVID threat.

Figure 4. Marginal effect of novel caseload on support for travel bans by target Country, June 2020–March 2021.

Across parties and racial attitudes, there is a positive relationship between new cases and support for a travel ban against China, consistent with the threat hypothesis; this marginal effect is statistically distinguishable from zero in most cases. However, in direct contradiction to the threat hypothesis, there is a negative correlation between new cases and support for a travel ban against Great Britain. These results hold both for all parties and levels of racial resentment. These findings are consistent with theoretical work on ingroup bias (Brewer, 1979), suggesting that American respondents do not exclude British nationals because they are not viewed as racial insiders. The positive correlation between caseloads and support for a travel ban against Brazil only holds among low-RR Democrats, indicating that most Americans support entry bans based on group attitudes, not based on objective threat.

4. Conclusion

We find that racial attitudes shape positions on entry bans during the COVID-19 pandemic in the United States. Early attitudes prove persistent and resilient to changing threat, rewarding countries perceived to be within-group (Great Britain) and penalizing outsiders (China, Brazil) to various degrees. These results are consistent with the overlapping literatures on the role of ethnocentrism, racial attitudes, and sociotropic preferences on immigration policy attitudes. They also complement work that shows racial prejudice shapes support for individual level COVID-19 safety precautions and restrictions (Stephens-Dougan, 2022).

Racial attitudes have been found to affect nearly every aspect of American policy making, from housing to health care, and pandemic measures prove no exception. Our evidence not only shows how strong and enduring those attitudes may be but how they are formed early and persist despite objective threat. Understanding their early formation and persistent nature is particularly valuable for imagining responses to emergent crises, such as climate migration, future pandemics, and other global situations requiring aid.