Pandemic and Attitudes towards Immigrants: Evidence from South Korea during the COVID-19 Crises

Abstract

This article investigates the changes in South Koreans’ acceptance towards multiculturalism and acceptance towards North Korean defectors during the early months of the COVID-19 pandemic in South Korea and potential factors affecting the changes. Individual-level survey data conducted in 2018–2020 were used to compare pre- and post-COVID-19 period. The results demonstrate that the regions with severe local outbreaks during the early COVID-19 pandemic experienced increased hostile attitudes towards multiculturalism and increased discriminatory preference. On the other hand, the pandemic did not affect attitudes towards North Korean defectors. The change may be associated with fear of infection as interaction effects between vulnerability and affected regions after the pandemic were statistically significant. This finding suggests that efforts to disassociate infectious diseases and foreign migrants can mitigate increased hostility towards multiculturalism and foreigners.

Keywords

COVID-19 immigrants multiculturalism discrimination North Korean defectors

I. Introduction

During the COVID-19 pandemic, xenophobic attitudes towards Asians grew throughout the world. The negative stereotype and blaming towards Chinese people surfaced and, even in some instances, stigmatization directed towards Asians, led to verbal and physical violence (Jackson et al., 2020; Roberto et al., 2020). South Korea was no exception. During the early period of the pandemic, xenophobic sentiment and discriminatory behaviour against China and Chinese people were observed (Fottrell, 2020; Kasulis, 2020; Kim, 2020). The government policies were also discriminatory, excluding foreign nationals from health supplies and subsidies and ordering all foreign workers to get mandatory testing in certain provinces (Lee et al., 2021; Smith & Cha, 2021).

South Korea is considered an ethnically homogenous country, having a small percentage of foreign-born populations compared to other OECD countries (OECD & European Union, 2015). Although the foreign population had been gradually increasing until the COVID-19 pandemic, reaching almost 5% of the total population in 2019, perceptions towards migrants have not been keeping up. South Koreans’ support for multiculturalism deteriorated over the last decade, from 61% of the respondents agreeing to the idea of coexistence of multi-ethnicities and multi-cultures in 2010 to 50% in 2015 and 44% in 2020 (Choi & Cho, 2020). Among 37 OECD countries, South Korea ranked 31st on the community acceptance for minorities (OECD, 2020a).

As of May 2021, 90% of long-term foreign residents in South Korea are from Asia, with Korean Chinese, Chinese and Vietnamese being the three largest groups (Ministry of Law, 2021). The majority of immigrant workers have low-skilled occupations; 39% are craft, equipment, machine operating and assembling workers; 31% are elementary workers; and 14% are service/sales workers (Ministry of Law, 2021). Immigrants in South Korea often face discrimination based on race, skin colour and country of origin. Studies find that white ethnics are favourably perceived while Southeast Asian ethnics are not, and that foreigners from higher-income countries face less discrimination (Ha, 2012; Jeong et al., 2017; Lew & Lee, 2006). A much smaller in number, but equally salient, group is North Korean defectors, who share the same ethnicity as South Koreans. North Korean defectors have a unique status among immigrant groups: they are immediately guaranteed South Korean citizenship upon arrival to South Korea, unlike other refugees. However, several studies find that adjustment to the new environment is not easy for them and many of them face discrimination (Yi et al., 2007; Yu et al., 2012).

In a setting where ethnic diversity is low and immigrants are not favourably perceived in general, negative shocks such as a pandemic can negatively affect attitudes towards migrants even more. Framing a stigmatized group as responsible for the disease, also known as ‘othering’, is often observed (Eichelberger, 2007; Reny & Barreto, 2022). These ‘others’ are typically ethnic minorities, presented as outsiders in the media, and may not necessarily be associated with the disease (Dionne & Seay, 2015; Jordan, 2008). This othering sometimes occurs not because of prejudice or stigma but due to the feeling of disgust towards possible infection sources or due to behavioural changes to reduce risks of infection (Faulkner et al., 2004; Fincher et al., 2008; Kam, 2019; Murray et al., 2013; Navarrete & Fessler, 2006). In this article, I explore the impact of the pandemic on attitudes towards immigrants based on the annual survey data conducted in 2018–2020 in South Korea using a difference-in-difference estimation method. During this time frame, only a few regions in South Korea experienced intense COVID-19 outbreaks locally. Three measures of anti-immigrant attitudes are employed: discriminatory attitudes and disacceptance towards multiculturalism and North Korean defectors—a co-ethnic group not associated with the disease but is marginalized.

I find that the regions severely affected by the early outbreak of COVID-19 have more hostile attitudes towards multiculturalism and are more discriminatory. However, a difference in the attitudes towards North Korean defectors is not observed between the regions. On the other hand, the older respondents are more likely to be hostile towards multiculturalism in the severely affected regions. By contrast, stronger ethnic identity does not have an additional effect on the attitudes towards multiculturalism. This implies that the decreased preference for multiculturalism partly stems from concerns of potential infection, rather than from ethnocentrism.

The rest of the article is organized as the following. Section II explains the characteristics of immigrant groups in South Korea, and Section III describes the early period of the COVID-19 pandemic in South Korea. In Section IV, theoretical background and hypotheses on the relationship between people’s attitudes towards migrant groups and exposure to infectious diseases are introduced. Section V introduces the empirical model and the data utilized for the analysis. Section VI demonstrates the empirical results, and Section VII concludes the article.

II. Immigrant Groups in South Korea

The foreign population in South Korea is low compared with other high-income countries, with less than 5% of the population as of 2021. The salient foreigner groups are less-skilled foreign workers, of whom the majority are from China and Southeast Asia. As of May 2021, among 855 thousand foreign workers in South Korea, 773 thousand are from Asia, comprising 341 thousand Korean Chinese, 92 thousand Vietnamese and 44 thousand Chinese (Ministry of Law, 2021). Another salient group is North Korean defectors. While they are much fewer in number, accumulating to 33,815 as of 2021 (Ministry of Unification, 2022), they are more often present in the media than other major immigrant groups, depicted as migrants who embody the image of an ideal citizen working hard to assimilate in the South Korean society and sharing the hardships while in North Korea (Cho, 2018).

Korea has a relatively short history of immigration policies for immigrant workers and workers’ rights. In the 1990s, foreign workers began to increase as domestic wages increased and the economic growth paced up. The Industrial and Technical Training System and the Industrial Trainee Program were established to assign foreign workers in vacant positions that could not be filled with the supply of Korean workers. Korea being a historically ethnically homogenous country, the government adopted a temporary migration scheme to prevent potential social and cultural changes due to foreign workers (Shin, 2009). Basic labour rights to foreign workers were only granted in 2004, when the Employment Permit System was introduced (Chung, 2020). But as foreign workers were seen only as the means to fill in the void in the labour market, the basic principle was that foreign workers should not cause unemployment to native workers and native workers were always prioritized over foreigners (Seol, 2005; Shin, 2009). The temporary nature of the employment permit resulted in the accumulation of undocumented workers over time. The proportion of undocumented workers accounted for more than half of the less-skilled workers as early as mid-2000s (Seol & Skrentny, 2009). Due to the short history of foreign workers’ migration and the large share of unskilled and undocumented workers among the foreign worker population, foreign workers in South Korea are often seen as low-income, and the Korean word for foreign workers often entails a negative connotation. Southeast Asians, Chinese and Korean Chinese, as they represent the majority of foreign worker population, are often associated with foreign workers and their negativeness.

It is interesting to note that while Korean Chinese and North Korean defectors are ethnic Koreans, they are differently perceived. Since the 1990s, Korean Chinese have formed the largest immigrant group and also the largest undocumented worker group. Many of them live in semi-segregated residential areas in lower-income districts, which are often negatively depicted in the media. The history that Joseonjok—the Korean word for Korean Chinese—were activists against the Japanese colonial rule is not well-known. They are not easily accepted by South Koreans and are perceived similarly as South East Asians in terms of likeability (Chung, 2020; Ha et al., 2016; Ka et al., 2014; Seol & Skrentny, 2009).

On the other hand, North Korean defectors are quite opposite to Korean Chinese in certain aspects. They are a minority group and are rarely visible in South Korean society in real life. Institution-wise, they are treated better than non-ethnic refugees and have their own special support programs under the North Korean Defectors Protection and Settlement Support Act. North Korean defectors receive five years of support, consisting of financial, employment, housing, social security, education, counselling and security support, in addition to emergency support in Hanawon, a temporary residential centre for North Korean defectors.

South Koreans’ views towards North Korean defectors are more complex than those towards other migrants. For older South Koreans who experienced the Korean War, North Koreans are Koreans who once lived in the same land and were separated by force majeure. ‘Sharing the same ethnicity’ is still the most popular reason to reunify for South Koreans, dominating practical reasons such as ‘removing war threats’ and ‘becoming a more advanced country’ (Kim et al., 2021a). Because of such preference for co-ethnicity, the public sentiment for ‘North Korean escapees (Talbukmin)’, which literally means ‘people who fled the North’, is generally welcoming when a defector is reported to have arrived. North Korean defectors are more favoured than immigrant workers from Southeast Asia and Koreans Chinese (Ha et al., 2016; Ka et al., 2014). However, successful integration of North Korean defectors in the South Korean society is still a remaining task. On average, North Korean defectors have lower-paying jobs and less social support networks than South Koreans and many report to have experienced discrimination (Kim et al., 2022; Yu et al., 2012). As maladjustment issues emerged, the perception that they are a social and financial burden has also increased (Lankov, 2006; Yi et al., 2007; Yu et al., 2012). Furthermore, unification is increasingly considered unnecessary and is becoming a subject of disinterest for young South Koreans (Kim et al., 2021a). The factors comprising ethnic identity, such as ethnicity and place of birth, which had been previously considered important in national identity, have become less so since the 2010s (Kim et al., 2021b). According to the 2020 Unification Consciousness Survey, only South Koreans aged 60s and up had more people in their age group who feel close to North Korean defectors than those who feel distant (Kim et al., 2021a).

III. Early Stage of COVID-19 Outbreak in South Korea

In the early stage of the COVID-19 pandemic, the South Korean government was relatively successful in preventing countrywide massive outbreaks. On 20 January 2020, South Korea’s first confirmed COVID case was found, but a mass outbreak only occurred one month later, in Daegu, Korea’s fourth most populous city with a population of approximately 2.4 million. The number of confirmed cases began to dramatically increase from 20 February, centred around a church in Daegu. By late February, due to the high number of confirmed cases, Daegu and Gyeongsangbuk-do (hereafter ‘Gyungbuk’) areas were declared ‘special disaster zones’ or ‘special management areas’ by the government (Kang et al., 2020). However, the contagion was limited. The cumulative number of confirmed cases per 10,000 people by the end of March was 28.0 in Daegu and 4.8 in Gyeongbuk, but remained less than 0.9 in other regions (Figure 1a). Even before the announcement of strong social distancing measures in late March, people had already been voluntarily distancing themselves by wearing masks and reducing social gatherings (Park & Yum, 2020). While a lockdown policy was not implemented in South Korea, travelling, which involves the risk of getting infected or infecting others, would have become burdensome as contacts and mobility information had to be publicly disclosed in case of infection (Kang et al., 2020).¹ As such, the first wave of COVID outbreaks was centred around the southeastern part of Korea while the rest of the regions experienced very few cases of infection.

Figure 1.

Source: The author.

Starting from May, other regions started to see more confirmed cases, but the increase was minimal (Figure 2). The cumulative number of confirmed cases per 10,000 by June was 29.0 for Daegu and 5.1 for Gyeongbuk but less than 1.3 for other regions (Figure 1b). Until mid-August when the second wave—which was still less severe than the first—started, South Korea was relatively successful in containing the transmission of the virus compared with European countries and the United States (Khosrawipour et al., 2020; Lim et al., 2020; Walensky & del Rio, 2020). Thus, until the late summer of 2020, the areas that were severely affected by the pandemic were concentrated in Daegu and Gyeongbuk Province.

Figure 2.

Source: The author.

IV. Theoretical Background and Hypotheses

A vast literature reports the stigmatization of marginal groups due to infectious diseases: the Portuguese and Spanish during the Spanish flu (Dionne & Turkmen, 2020); Chinese and Asian immigrants during the SARS pandemic (Eichelberger, 2007; Wallis & Nerlich, 2005); and West Africans during the Ebola pandemic (Prati & Pietrantoni, 2016). During the early months of the COVID-19 pandemic, Chinese, Chinese immigrants and other Asians experienced discrimination not only in the western countries but in Asia as well (Dionne & Turkmen, 2020).

Multiple theories predict that a rise of a pandemic causes people’s xenophobic attitudes, or more broadly, partitioning the world between ‘us’ and ‘them’. Each theory provides different explanations for this othering behaviour. Xenophobic attitudes can be the result of feelings of disgust caused by the potential source of contaminant—the disease-avoidance hypothesis by Kam (2019), of conforming to a more exclusive behaviour to decrease potential infection risk—the pathogen prevalence hypothesis by Fincher et al. (2008), or of framing the marginalized groups as the culprits—the ‘othering’ hypothesis (Dionne & Seay, 2015).

Although each theory has a different boundary between the ingroup and the outgroup, no systematic ways have been developed to distinguish between the two. According to the disease-avoidance hypothesis, infectious diseases evoke emotions of disgust, which is an evolutionary response to potential contaminants (Faulkner et al., 2004; Kam, 2019; Navarrete & Fessler, 2006). Sources of disgust can be both real and imaginary. People who are more prone to experiencing the feelings of disgust are more likely to be wary of infectious diseases and are more supportive of policies that are designed to prevent infection by protecting healthy ‘us’ against contaminated ‘them’. Kam (2019) finds a significant relationship between disgust sensitivity and support for policies that restrict acceptance of people coming from countries where outbreaks of the Zika or Ebola virus occurred, whether the entrants were natives or foreigners. In this case, it can be seen that the outgroup is all people from ‘contaminated’ regions and the ingroup is people who stayed within the country border.

The distinction between ingroup and outgroup is vague for the pathogen prevalence hypothesis. The ingroup can either be people with the same ethnicity or natives. Fincher et al. (2008)’s pathogen prevalence hypothesis posits that disease prevalence leads to a cautious behaviour or personality that reduces the risk of infection. The cautiousness not only applies to foreigners but also to anything ‘unfamiliar’ that can potentially increase the risk of infection, such as trying new foods or new practices. As a result, people are more likely to prefer collectivistic and ethnocentric values, support authoritarian political structures and conform to societal pressure (Murray et al., 2013).

In the ‘othering’ hypothesis, emotions associated with the disease and support for restrictive policies simultaneously influence each other. Anxiety and fear increase support for restrictive policies and these policies shape perception towards associated groups. The groups can either be the specific group associated with the disease, or simply marginalized groups even if they are not associated with the disease (Dionne & Seay, 2015; Dionne & Turkmen, 2020; Jordan, 2008). Framing the outgroup as the responsible for the disease by the media or the government (e.g., perceiving African immigrants as a whole as dangerous, calling for travel bans from the Ebola zone), increases prejudice and xenophobia.

In this article, I test whether the COVID-19 pandemic had an impact on South Koreans’ anti-immigrant attitudes. The immigrants in South Korea are small in number but are diverse. They comprise ethnic Koreans—namely, North Korean defectors and Korean Chinese—and non-ethnic immigrants (Chinese, Japanese, Southeast Asians, etc.). All immigrants coming from abroad may be associated with COVID-19. North Korean defectors are an exception, as few of them defected to South Korea during the pandemic (229 and 63 defectors in 2020 and 2021, respectively, vs. 1,037 in 2019; Ministry of Unification, 2022). Furthermore, it would have been difficult to associate North Korean defectors with COVID as North Korea did not admit its first confirmed case until May 2022 (Mao, 2022). Among the immigrant groups, immigrants from China—both Chinese and Korean Chinese—could particularly be more associated with the virus. As they constitute the majority of immigrant population in South Korea, the term ‘immigrants’ is likely to be associated with the pandemic for South Koreans. To examine whether a change in perception occurred towards immigrants due to the pandemic shock, I compare acceptance towards multiculturalism in general and towards North Korean defectors and discrimination preference with the following research questions.

RQ1: Did the COVID-19 outbreak affect attitudes towards multiculturalism and North Korean defectors in South Korea?

Some studies find that infectious diseases increase ethnocentrism and collectivism during the pandemic (Kim et al., 2016; Murray et al., 2013; Na et al., 2021). If so, individuals with higher ethnic identity in the regions with higher COVID-19 infection rates would have more hostile attitudes towards foreigners. The following research question is proposed to examine whether higher ethnic identity influences attitudes towards multiculturalism during the COVID-19 pandemic in South Korea.

RQ2: Did stronger ethnic identity preference have interaction effects with highly impacted areas during the pandemic in South Korea?

By contrast, negative attitudes towards multiculturalism may be higher for population groups that are more vulnerable to the disease. Age is an important factor that determines vulnerability to disease, and the risk of COVID-19 infection was significantly higher for older adults as well (Chee, 2020; Lloyd-Sherlock et al., 2020). The following research question is proposed to examine whether vulnerability to the disease influences people’s response to the preference for diversity.

RQ3: Did vulnerability to infection have interaction effects with highly impacted areas during the pandemic in South Korea?

V. Data and Empirical Model

The dataset used for this article is the Institute of Peace and Unification Studies’ Unification Perception Survey (hereafter ‘IPUS Survey’). Due to the availability of the variables used for the analysis, I use the years 2018–2020, which cover pre- and post-COVID-19 pandemic. The IPUS Survey is conducted in July every year. From the survey population, all South Koreans between the ages of 19 and 74 living in South Korea, multi-stage stratified sampling was used to obtain a sample of 1,200 respondents each year. Removing a few samples with missing information resulted in a total sample of 3,591. Although the IPUS Survey focuses on issues of unification and North Korean defectors, the questions related to receptivity to multiculturalism are included in recent survey waves, enabling comparative analyses between immigration-related attitudes.

As described in Section III, the sporadic patterns of outbreak did not occur until late August and only Daegu and Gyeongbuk were heavily affected by the COVID pandemic before July 2020, the survey period (Figure 2). This timing enables us to the difference-in-difference estimation, comparing heavily affected regions and mildly affected regions before and after the pandemic. The main hypothesis of this article is that a COVID outbreak in the local area can affect people’s feelings and attitudes towards immigrants. Since the initial expansion of the virus had regional variations that randomly occurred, mainly due to a religious cult in Daegu/Gyeongbuk area, the regions heavily affected by the pandemic can be considered as the treatment group.

Three measures of anti-immigrant attitudes were used: multicultural disacceptance, discriminatory attitudes and North Korean disacceptance. The dependent variable that measures ‘multicultural disacceptance’ is based on the level of agreement with the statement, ‘It is better to coexist between races, religions, and cultures for any country’. The variable ‘discriminatory attitudes’ measures discriminatory preference towards immigrants and is based on the level of agreement with the statement, ‘All legal foreign residents living in South Korea should be treated equally as South Koreans at school or workplace’. ‘Multicultural disacceptance’ and ‘discriminatory attitudes’ are on a five-point scale and coded such that higher values indicate greater hostility.

The variable ‘disacceptance toward North Korean defectors’ is based on the question, ‘What do you think about North Korean defectors who want to come to South Korea?’ A respondent chooses one of the three responses, ‘We should accept everyone who wants to come’, ‘We should select among those who want to come’ and ‘We should not accept any more defectors’. This variable is also coded such that higher values indicate greater hostility. Using the aforementioned dependent variables enables us to compare South Koreans’ attitudes towards general immigrants and a co-ethnic migrant group and preference for discrimination.²

In order to check the common trends assumption, I present the graphical evidence of yearly variations between the treatment and control group (Figure A1). In each graph, the average levels of respective anti-immigrant attitudes and their 95% confidence intervals are presented. The horizontal axis represents the baseline of the event time. For North Korean disacceptance, the trends between the groups seem fairly parallel for all years. For multicultural disacceptance and discriminatory attitudes, the differences between treatment and control group are statistically insignificant at 5% level in years 2018 and 2019. Thus, the graphical evidence suggests that the control and treatment groups have parallel trends in terms of anti-immigrant attitudes. State-specific time trends were controlled in the regression model for potential region-specific trends affecting the treatment effect. Placebo test results with fake treatment groups and fake treatment years also confirmed the parallel trends.

Since all dependent variables are ordinal variables, the ordered probit model was used for all regression estimation. The following equation presents the empirical model used to investigate RQ₁:

\begin{matrix} Y_{i c t} = β_{0} + β_{1} C O V I D * y e a r_{2020} + β_{2} C O V I D + \\ γ_{1} X_{c t} + γ_{2} X_{i t} + F_{c} + F_{t} + λ t_{c} + ε_{i c t}, \end{matrix}

(1)

where Y_ict is a degree of hostile attitudes of an individual i living in province c in year t, COVID is an indicator variable for Daegu or Gyeongbuk, the affected regions during the first wave of the pandemic, year₂₀₂₀ is the indicator variable for the year 2020, and X_ct and X_it are the province-year and individual covariates. F_c and F_t denote province fixed effects and year fixed effects, respectively, t_c is province-specific time trends, and ε_ict is an error term. The coefficient β₁ captures the pandemic effect on the respective anti-immigrant attitudes measure. The coefficient β₂ captures the time-invariant effect of the treatment region.

The control variables, X, include basic demographic variables such as age, gender, marital status and the following. First, education levels are controlled, as anti-immigrant attitudes are known to be affected by education levels (human capital theory; Gang et al., 2010). Income levels and occupation are associated with immigrant acceptance through the following mechanisms. The higher income natives may be anti-immigrant because of tax burden concerns (Hanson et al., 2007) and lower-skilled natives may be anti-immigrant because of competition concerns (Scheve & Slaughter, 2001). Political stance is also one of the determinants of attitudes towards immigrants. The attitudes are known to be more hostile if natives have conservative political preferences (Rustenbach, 2010). Religion is also controlled because it can affect immigration attitudes if religious backgrounds are different between natives and migrants (cultural marginality theory; Huntington, 1996). The indicator variable of rural residence, because it can affect the level of contact with migrants, is controlled along with province dummies. Regional characteristics, such as unemployment rate and percentage of foreigners, are also controlled.

For investigating RQ₂, the following equation was used:

\begin{matrix} Y_{i c t} = β_{0} + β_{3} C O V I D * y e a r_{2020} * e t h n i c + β_{1} C O V I D * y e a r_{2020} + \\ β_{2} C O V I D + γ_{1} X_{c t} + γ_{2} X_{i t} + F_{c} + F_{t} + λ t_{c} + ε_{i c t}, \end{matrix}

(2)

where ethnic represents the normalized value of the difference between ethnic identity score and civic identity score.³ The coefficient β₃ captures the heterogeneity of the pandemic effect by ethnic identity score. If β₃ is statistically significant and positive, having higher ethnic identity has an additional effect on the respective measure of hostility in the affected regions. This setting tests whether stronger ethnic identity has an additional effect on anti-immigrant attitudes in severely affected regions. Pathogen prevalence theory suggests that a pandemic strengthens solidarity between insiders and rejection of outsiders. If ethnic identity has an additional effect on hostility towards diversity, it can serve as evidence to the pathogen prevalence theory.

The decrease in hospitality towards outgroup can stem from the fear of potential infection from people who came from abroad (disease-avoidance theory). For investigating RQ₃, the following equation was used:

\begin{matrix} Y_{i c t} = β_{0} + β_{4} C O V I D * y e a r_{2020} * a g e + β_{1} C O V I D * y e a r_{2020} + \\ β_{2} C O V I D + γ_{1} X_{c t} + γ_{2} X_{i t} + F_{c} + F_{t} + λ t_{c} + ε_{i c t}, \end{matrix}

(3)

The coefficient β₄ captures the heterogeneity of the pandemic effect by age. If β₄ is statistically significant and positive, being one year older has an additional effect on the respective measure of hostility in the affected regions. In that case, the decreased hostility can be linked to the disease-avoidance model, although the direct evidence that people’s feeling of disgust increases with severity of COVID-19 cannot be proven with the current data.

VI. Findings

Descriptive Overview

Table 1 presents the level of hostility towards multiculturalism and towards North Korean defectors before and during the pandemic, represented by 2018–2019 and 2020. The contrast between severely affected regions and mildly affected regions is observed. In the severely affected regions, multicultural disacceptance and discriminatory attitudes increased in 2020, while the levels are similar or decreased in the mild outbreak regions. The receptivity to North Korean defectors, on the other hand, slightly decreased in the mildly affected regions.

Table 1.

Anti-Immigrant Attitudes before and after the COVID-19 Outbreak in South Korea.

Year	Severe Outbreak Regions			Mild Outbreak Regions
Year	Multicultural Disacceptance	Discriminatory Attitudes	North Korean Disacceptance	Multicultural Disacceptance	Discriminatory Attitudes	North Korean Disacceptance
2018–2019	2.61	2.50	1.96	2.51	2.50	1.80
2020	2.81	2.90	2.02	2.37	2.47	1.87
Difference p-value	.03	.00	.41	.00	.27	.00

Source: The author.

Notes: ‘Multicultural disacceptance’ and ‘Discriminatory attitudes’ variables are 5-scale variables based on the following statements, respectively: ‘It is better to coexist between races, religions and cultures for any country’ and ‘All legal foreign residents living in South Korea should be treated equally as South Koreans at school or workplace’. ‘North Korean disacceptance’ is a 3-scale variable based on the question, ‘What do you think about North Korean defectors who want to come to South Korea?’ Higher values denote more hostile attitudes for all variables. The severe outbreak regions consist of a total of 251 respondents and 126 respondents in 2018–2019 and 2020, respectively. The mild outbreak regions consist of a total of 2,144 and 1,070 respondents in 2018–2019 and 2020, respectively.

Table A1 provides descriptive statistics of the variables used for the analysis. The mean value of multicultural disacceptance is 2.49, which is between moderate and neutral acceptance. The mean level of discriminatory attitudes is 2.50, which is between moderate and neutral as well. Mean disacceptance for North Korean defectors is 1.84, which is close to selective admission. The respondents who live in the severely affected regions, namely, Daegu and Gyeongbuk, are approximately 10.5% of the total sample. Half of the respondents are male, and the average age of the sample is 45.5 years. The education distribution of the sample is similar to that of the South Korean population, which have 39% of adults aged 25–64 with completed secondary education and 51% with higher than secondary education (OECD, 2020b).

Over half of the respondents have a household income over KRW 4 million, which approximates to USD 3,683 (1 USD = 1,086 KRW in 2020). A quarter of the respondents have a household income of KRW 3–3.99 million.⁴ Approximately a quarter of the respondents do not work or are unemployed, and 28% and 21% of the respondents have blue- and white-collar jobs, respectively. The mean value of political attitudes is 2.85, which is between a progressive and neutral political stance. While the majority of the respondents are not religious, Buddhist and Christian respondents account for 17% and 23% of the sample, respectively.

Regression Results

Given that the dependent variables are ordinal variables, the ordered probit model was used for estimation. Table 2 presents the estimation results of Equation (1). All coefficients indicate marginal effects when other covariates are at the mean value. The coefficient of interest, β₁—that of COVID*year, is statistically significant and positive for multicultural disacceptance and discriminatory attitudes, meaning that the regions severely affected by the pandemic experienced increased hostility towards immigrants after the outbreak of COVID-19 (Columns 1–2). By contrast, when the dependent variable is disacceptance for North Korean defectors β₁ is not statistically significant (Column 3). The results imply that hostility towards the outsider group that is associated with either the disease or different ethnicity increased during the period.

Table 2.

Predicting Anti-Immigrant Attitudes.

Variables	(1)	(2)	(3)
Variables	Multiculturalism Disacceptance	Discriminatory Attitudes	North Korean Disacceptance
COVID	–0.164***	–0.025	0.276
	(0.054)	(0.031)	(0.316)
COVID*year	0.046***	0.024***	–0.074
	(0.010)	(0.007)	(0.053)
Male	–0.003**	–0.002*	–0.026***
	(0.001)	(0.001)	(0.009)
Age	–0.000	–0.000	–0.002
	(0.000)	(0.000)	(0.003)
Age-squared	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
(Ref: Never married)
Married	–0.003	0.000	–0.022
	(0.003)	(0.001)	(0.016)
Other	–0.007*	–0.003	–0.040
	(0.004)	(0.002)	(0.028)
(Ref: Lower than secondary education)
Completed secondary education	–0.006	–0.001	–0.023
	(0.004)	(0.002)	(0.023)
College or higher	–0.008*	–0.003	–0.049**
	(0.004)	(0.002)	(0.025)
(Ref: Lower than 2 million)
KRW 2–2.99 million	0.001	0.003	–0.012
	(0.002)	(0.002)	(0.023)
KRW 3–3.99 million	0.006**	0.002	–0.010
	(0.003)	(0.002)	(0.023)
KRW 4 million+	0.006**	0.003*	–0.008
	(0.003)	(0.002)	(0.022)
(Ref: Agriculture worker)
Self-employed	–0.008	–0.001	–0.008
	(0.007)	(0.004)	(0.034)
Blue-collar worker	–0.004	–0.002	0.014
	(0.007)	(0.004)	(0.034)
White-collar worker	–0.007	0.001	0.011
	(0.007)	(0.004)	(0.035)
Not working	–0.008	–0.000	0.027
	(0.007)	(0.004)	(0.035)
Conservative	0.006***	0.002***	0.033***
	(0.001)	(0.001)	(0.006)
(Ref: Buddhist)
Protestant	–0.001	–0.003**	–0.013
	(0.002)	(0.001)	(0.013)
Catholic	–0.004	0.004*	–0.001
	(0.003)	(0.003)	(0.021)
No religion	0.002	0.000	0.026**
	(0.002)	(0.001)	(0.012)
Rural area	0.005*	0.001	0.002
	(0.003)	(0.002)	(0.017)
Unemployment rate (%)	0.039***	0.001	–0.081
	(0.012)	(0.007)	(0.085)
Immigrants (%)	–0.190***	–0.015	0.270
	(0.062)	(0.036)	(0.377)
Observations	3,591	3,591	3,591

Source: The author.

Notes: The coefficients denote marginal effects when other controls are evaluated at their mean values. ‘Year’ is an indicator variable that has a value 1 if the survey year is 2020 and 0 otherwise. The models include year-fixed effects, province-fixed effects and province-specific time trends. Sampling weights were applied. Robust standard errors are in the parentheses. ***p < .01, **p < .05, * p < .1.

Compared with the respondents with lower education, having tertiary education increases the predicted probability of acceptance towards multiculturalism and North Korean defectors. By contrast, discriminatory attitudes and education are not associated, which contradicts common sense. The reasons are unclear and cannot be explored with the current dataset. Higher household income is associated with higher hostility towards multiculturalism but not discriminatory attitudes or hostility towards North Korean defectors. Having a more conservative political preference is associated with all measures of anti-immigrant attitudes. Being a Protestant is associated with less discriminatory attitudes, but it is not associated with the other acceptance measures. The regional unemployment rate is associated with hostility to multiculturalism and the percentage of foreigners is negatively associated with hostility to multiculturalism.

Table 3 presents estimation results for Equation (2), which examines whether greater ethnic identity relative to civic identity, has an additional effect on anti-immigrant attitudes. The coefficient β₃—that of COVID*year*ethnic, has a value close to zero and is not statistically significant for all dependent variables. Unlike some countries that experienced more hostility towards the marginalized group due to the pandemic, the decreased acceptance towards migrants after the COVID outbreak in South Korea is not related to a stronger preference for ethnic identity.

Table 3.

Interaction between Ethnic Identity and COVID-19 Outbreak.

Variables	(1)	(2)	(3)
Variables	Multiculturalism Disacceptance	Discriminatory Attitudes	North Korean Disacceptance
COVID	–0.164***	–0.025	0.275
	(0.054)	(0.031)	(0.316)
COVID*year	0.047***	0.024***	–0.062
	(0.010)	(0.007)	(0.054)
COVIDyearethnic	0.001	0.001	0.022
	(0.002)	(0.001)	(0.014)
Male	–0.003**	–0.002*	–0.026***
	(0.001)	(0.001)	(0.009)
Age	–0.000	–0.000	–0.002
	(0.000)	(0.000)	(0.003)
Age-squared	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
(Ref: Never married)
Married	–0.003	0.000	–0.022
	(0.003)	(0.001)	(0.016)
Other	–0.007*	–0.003	–0.040
	(0.004)	(0.002)	(0.028)
(Ref: Lower than secondary education)
Completed secondary education	–0.006	–0.001	–0.022
	(0.004)	(0.002)	(0.022)
College or higher	–0.008*	–0.003	–0.048*
	(0.004)	(0.002)	(0.025)
(Ref: Lower than 2 million)
KRW 2–2.99 million	0.001	0.003	–0.012
	(0.002)	(0.002)	(0.023)
KRW 3–3.99 million	0.006**	0.002	–0.010
	(0.003)	(0.002)	(0.023)
KRW 4 million+	0.006**	0.003*	–0.007
	(0.003)	(0.002)	(0.022)
(Ref: Agriculture worker)
Self-employed	–0.008	–0.001	–0.009
	(0.007)	(0.004)	(0.034)
Blue-collar worker	–0.004	–0.002	0.012
	(0.007)	(0.004)	(0.034)
White-collar worker	–0.007	0.001	0.010
	(0.007)	(0.004)	(0.035)
Not working	–0.008	–0.000	0.026
	(0.007)	(0.004)	(0.035)
Conservative	0.006***	0.002***	0.033***
	(0.001)	(0.001)	(0.006)
(Ref: Buddhist)
Protestant	–0.001	–0.003*	–0.012
	(0.002)	(0.001)	(0.013)
Catholic	–0.004	0.005*	–0.000
	(0.003)	(0.003)	(0.021)
No religion	0.002	0.000	0.027**
	(0.002)	(0.001)	(0.012)
Rural area	0.005*	0.001	0.003
	(0.003)	(0.002)	(0.017)
Unemployment rate (%)	0.039***	0.001	–0.081
	(0.012)	(0.007)	(0.085)
Immigrants (%)	–0.190***	–0.015	0.270
	(0.062)	(0.036)	(0.376)
Observations	3,591	3,591	3,591

Source: The author.

Notes: The coefficients denote marginal effects when other controls are evaluated at their mean values. ‘Year’ is an indicator variable that has a value 1 if the survey year is 2020 and 0 otherwise. The models include year-fixed effects, province-fixed effects and province-specific time trends. Robust standard errors are in the parentheses. ***p < .01, **p < .05, *p < .1.

Table 4 presents estimation results for Equation (3), which examines whether greater vulnerability to infection has an additional impact on hostile attitudes towards migrants. The coefficient of interest, β₄—that of COVID*year*age, is statistically significant for only multicultural disacceptance. Additional 10 years of age add 1% point of predicted probability of reporting anti-multiculturalism. Even though β₄ is statistically insignificant when the dependent variable is discriminatory attitudes, heterogeneity within the group is observed. Figure 3(b) suggests that preference for discrimination is higher for people aged 35–69 in the severely exposed regions. This implies that the increased fear of infection may be the reason for increased hostility towards immigrants after the COVID outbreak. As people age, they become more susceptible to sickness or infection and hostility towards immigrants may increase as a result. Figure 3 visualizes the interaction between age and outbreak areas after COVID-19. Contrary to the areas with lower infection rates, the areas with higher infection rates have a positive correlation between age and probabilities of discriminatory and anti-multicultural attitudes. By contrast, such a relationship is not observed for acceptance towards North Korean defectors.

Table 4.

Interaction between Age and COVID-19 Outbreak.

Variables	(1)	(2)	(3)
Variables	Multiculturalism Disacceptance	Discriminatory Attitudes	North Korean Disacceptance
COVID	–0.164***	–0.025	0.275
	(0.054)	(0.031)	(0.316)
COVID*year	0.014	0.015	–0.145
	(0.014)	(0.010)	(0.090)
COVIDyearage	0.001***	0.000	0.001
	(0.000)	(0.000)	(0.001)
Male	–0.003**	–0.002*	–0.026***
	(0.001)	(0.001)	(0.009)
Age	–0.000	–0.000	–0.002
	(0.000)	(0.000)	(0.003)
Age-squared	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
(Ref: Never married)
Married	–0.004	0.000	–0.022
	(0.003)	(0.001)	(0.016)
Other	–0.007*	–0.003	–0.040
	(0.004)	(0.002)	(0.028)
(Ref: Lower than secondary education)
Completed secondary education	–0.006	–0.001	–0.022
	(0.004)	(0.002)	(0.022)
College or higher	–0.008*	–0.003	–0.048*
	(0.004)	(0.002)	(0.025)
(Ref: Lower than 2 million)
KRW 2–2.99 million	0.002	0.003	–0.012
	(0.002)	(0.002)	(0.023)
KRW 3–3.99 million	0.006**	0.002	–0.009
	(0.003)	(0.002)	(0.023)
KRW 4 million+	0.006**	0.003*	–0.007
	(0.003)	(0.002)	(0.022)
(Ref: Agriculture worker)
Self-employed	–0.007	–0.001	–0.007
	(0.007)	(0.004)	(0.034)
Blue-collar worker	–0.004	–0.002	0.014
	(0.007)	(0.004)	(0.034)
White-collar worker	–0.007	0.001	0.012
	(0.007)	(0.004)	(0.035)
Not working	–0.008	–0.000	0.027
	(0.007)	(0.004)	(0.034)
Conservative	0.006***	0.002***	0.033***
	(0.001)	(0.001)	(0.006)
(Ref: Buddhist)
Protestant	–0.001	–0.003*	–0.012
	(0.002)	(0.001)	(0.013)
Catholic	–0.004	0.005*	–0.001
	(0.003)	(0.003)	(0.021)
No religion	0.002	0.000	0.026**
	(0.002)	(0.001)	(0.012)
Rural area	0.005*	0.001	0.003
	(0.003)	(0.002)	(0.017)
Unemployment rate (%)	0.039***	0.001	–0.081
	(0.012)	(0.007)	(0.085)
Immigrants (%)	–0.190***	–0.015	0.269
	(0.062)	(0.036)	(0.377)
Observations	3,591	3,591	3,591

Source: The author.

Notes: The coefficients denote marginal effects when other controls are evaluated at their mean values. ‘Year’ is an indicator variable that has a value 1 if the survey year is 2020 and 0 otherwise. The models include year-fixed effects, province-fixed effects and province-specific time trends. Robust standard errors are in parentheses. ***p < .01, **p < .05, *p < .1.

Figure 3.

Source: The author.

VII. Conclusion

Although South Korea has high ethnic homogeneity, the existence of various ethnic and co-ethnic groups influences South Koreans’ perception on each group and on foreigners in general. When a shock associated with foreigners, such as an infectious disease originating from abroad, occurs, the effect can be more salient. This study observed the perceptional changes of South Koreans during the pandemic period using the difference-in-difference estimation. The estimation results indicate that the regions with severe outbreaks during the initial transmission period of the COVID-19 pandemic report higher hostility towards multiculturalism and higher discriminatory attitudes. The difference in attitudes towards North Korean defectors was not observed between regions. As North Korean defectors share the same ethnicity with South Koreans and were not associated with COVID, such results can be due to natives’ preference to avoid possible infection sources or due to increased ethnocentrism by perceiving North Korean defectors as ‘ingroup’ rather than ‘outgroup’ in terms of ethnicity.

These two possibilities were explored by including interaction variables. The interaction effect between ethnic identity and infection was not statistically significant for all measures of anti-immigrant attitudes, suggesting that ethnocentrism was not the underlying reason of such increase in anti-multiculturalism and discriminatory attitudes. By contrast, the interaction effect between age and infection was statistically significant when the dependent variable was disacceptance towards multiculturalism, implying that vulnerability to diseases may be the underlying reason. However, vulnerability seems to explain only part of the decline in acceptance towards foreigners, as age’s interaction effect with treatment was not statistically significant when explaining discriminatory attitudes. Although there were indications that older people reported higher discriminatory attitudes in the severely exposed regions, the reasons for increased discriminatory attitudes in the severe outbreak regions can only be partly explained.

Owing to the limitations of the data, mechanisms for increased anti-immigrant attitudes were not rigorously explored. Given that the survey data did not contain questions related to the COVID-19 pandemic, we can only conjecture that the changes were due to the disease only because of its timing. Furthermore, the direct linkage between age, vulnerability and disgust emotions cannot be proven. Although this study attempted to explore the underlying mechanism indirectly through investigating interaction effects of ethnic identity or vulnerability, the interpretation of results involves speculation. The assumption of this study was that people’s attitudes change depending on actual confirmed cases. However, there is a possibility that people’s attitudes change depending on the perceived threat of infection, in which case people’s perceived threat may not necessarily be proportional to the actual confirmed cases. The current study design, which divides the regions into a severely affected group and a mildly affected group, may not work under such circumstances.

Nevertheless, the findings of this research imply that framing foreigners responsible for a disease is detrimental to people’s preference for diversity. Although foreigners in general and North Korean defectors are marginalized groups in South Korea, the fact that support for multiculturalism decreased and acceptance for North Korean defectors did not suggests that a marginalized group can also be freed of being blamed if they are not associated with the disease. As previous studies argue, the media reports during the early COVID outbreak that alerted extreme caution against foreigners, as well as discriminatory government policies, augmented the framing of foreigners as culprits. During a period of negative events such as a pandemic, such framing should be avoided to prevent expressions of hostility towards the marginalized.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship and/or publication of this article: The author acknowledges the financial support by the University of Suwon (funding year: 2019).

Appendix

Notes

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