https://orcid.org/0000-0003-3579-4341
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ABSTRACT
Past research suggests that disease outbreaks drive prejudice towards minorities as they increase economic and disease threats. Based on an open-ended survey question distributed to 7,902 German residents over the course of one year of the Covid-19 pandemic (April 2020 to April 2021), we investigate the link between life-threatening events and ethno-racial prejudice. We find that pandemic-related threats only drive respondents’ tendency to scapegoat ethno-racial groups if they hold left and center leaning ideologies. However, for far-right supporters who are the most likely to attribute the spread of Covid-19 to ethno-racial groups, pandemic-related threats do not affect that attribution. We further find that threat theories are of limited relevance for explaining which ethno-racial groups are targeted: respondents held Chinese accountable at the beginning of the pandemic but quickly shifted their attention to immigrants – a salient figure in pre-Covid-19 rightist rhetoric. We show that ideology, more than pandemic-induced threat, continues to drive prejudice and demonstrate the under-utilized advantages of using open-ended survey questions for understanding the dynamics of intergroup prejudice.
Acknowledgements
We are grateful to the two anonymous reviewers, the participants of the Race and Ethnicity Workshop at New York University, and our colleagues at the Migration & Diversity Colloquium at Berlin Social Science Center for their valuable feedback. Julia Forke and Jasper Jansen provided excellent research assistance. We would also like to thank Berenike Firestone, Ruud Koopmans, Ann Morning, Max Schaub, Joschka Wanner and Anne-Kathrin Will for their close reading of earlier versions of this paper. Any remaining errors are our own.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 Most survey responses fall within the following five, inductively built categories: the category of travelers, followed by lower shares of responses for the categories of rule breakers, age groups, active people and ethno-racial groups.
2 The question was included in 22 waves – stretching from weekly waves between April and June 2020, over bi-weekly waves from July to September 2020 to a third period of data collection from November 2020 to April 2021.
3 Compared to the national average, our sample included fewer respondents with a migration background, i.e. persons who have been born abroad or have at least one parent born abroad (26 compared to 16 percent). This might be a result of the questionnaire being administered in German.
4 Overall, we received n = 10,346 responses. After excluding missing data and non-responses, our sample comprised n = 7,902 individuals, with an average of 376 participants per wave. 16 out of the 22 waves had more than 400 observations.
5 The original German wording was: ‘Welche Gruppen haben Ihrer Meinung nach besonders zur Verbreitung des Coronavirus beigetragen? Nennen Sie bis zu drei Gruppen.’
6 The 21.5 percent of respondents who did not answer the open question do not differ from the other respondents in their socio-demographic characteristics. We are thus confident that there is no strong selection bias in our data. What is more plausible is that many respondents were put off by the additional time and effort that answering an open question takes.
7 Intercoder reliability across three independent coders is substantial for the Brennan and Prediger coefficient (0.98), Cohen and Conger’s Kappa (0.74), Fleiss’ Kappa (0.73), Gwet’s AC (0.99) and Krippendorff’s Alpha (0.73).
8 A short-term work scheme allows employers to drastically reduce workers’ hours instead of laying them off. A significant portion of the lost income is covered by the state. The scheme has helped to avoid lay-offs during the pandemic. In April 2020, the unemployment rate was only slightly above that of April 2019 (5.8 percent compared to 4.9 percent), whereas the number of employees with reduced work hours rose to around 10 million people, i.e. a third of all employees in Germany (Bundesagentur für Arbeit Citation2020).
9 As part of the risk group, we define people over 65 and people that have at least one chronic disease which increases the risk of severe illness if infected by the coronavirus.
10 The index was calculated as 1*neighbor + 2*colleague + 3*friend + 4*family + 5*self, with each category being a dummy indicating whether the respondent knew a person in that category that got infected. The index was then normalized to range from 0 to 1 with higher values signifying more infections among people frequently interacted with. Controlling for all categories separately did not alter our results.
11 Marginal effects for socio-demographic controls are not depicted in table 1. We find consistent and highly statistically significant correlations for gender, age, and education. All coefficients show their expected signs: Women are less likely to name ethno-racial groups, while older and likely more conservative people are more likely to do so. A better education is detrimental to ethno-racial group naming. The coefficient of having a migration background is negative but not significant. The coefficients for household size and frequency of contact to people living abroad do not significantly differ from zero.
12 Also, a narrower definition of economic loss – job loss due to the pandemic – shows no statistically significant impact on the probability of naming ethno-racial groups.
13 A recent study by Richter et al. (Citation2021) shows that counties with a strong far-right votership have significantly lower vaccination rates. One might thus expect a strong correlation between infection rates and AfD-votership to impact our results. However, on the last day of our data collection, April 15, 2021, only 6.5 percent of all Germans were fully vaccinated.
14 The above-mentioned heterogeneity in naming ethno-racial categories due to sociodemographic factors appears to be entirely driven by variation among non-far-right party supporters.
15 One might argue that the mention of ‘Chinese’ is motivated by a realistic threat. If we exclude Chinese mentions from our dependent variable, the effect size decreases (which is fully expectable given their share in our data), but the pattern remains the same and statistically significant at the one percent level. Given the in parts explicitly hostile answers from respondents and the concentration of mentions of Chinese among AfD supporters, we also doubt that they represent simply a realistic assessment of the situation.
16 Observations from September, October and January are excluded from the graph. The survey was paused in September and October 2020. In January 2021, probably due to the Christmas break, there were only 130 valid observations with less than 20 AfD voters suggesting alimited representativeness.