used in the literature (see Section 3.1). Three questions indicate a person’s general views on other members of society, namely questions on (i) whether people can be trusted, (ii) people are helpful or act in their own interest, or (iii) whether people are fair or exploit you. These questions refer to other members in society in general, not specifically immigrants or natives. As a proxy for a person’s view about immigrants, we use a question on whether one should exert caution towards foreigners. To capture concerns about safety – a central argument of conflict theory – we use a question on whether a person frequently worries about crime. Finally, we consider a question on whether a person has donated money to charity in the previous year, which proxies for a person’s commitment to a common cause.

With each question, respondents are presented with mutually exclusive answer options that differ between questions. For example, for the question whether “people can generally be trusted”, respondents could choose between four options, ranging from “disagree completely” to “agree completely”, whereas questions about concerns about crime and job security offer three choices – “not concerned at all”, “somewhat concerned” or “very concerned.” To facilitate the analysis, we convert all responses into binary indicators. For all questions except the one on perceptions on crime, a value of one represents an answer that is consistent with a higher level of social cohesion. For example, for the question about trust, we assign a value of one when a person agrees or completely agrees that people can be trusted, and zero otherwise. For the question “Do you believe that most people i) exploit you or ii) are fair” we assign a value of one if a respondent states that people are fair and zero otherwise. For the variables that expresses a person’s concern with crime, we assign a value of one if the person is very concerned about crime, and zero if they are somewhat concerned or not concerned at all. The precise wording of the questions along with the answer scales and the definition of the binary indicators can be found in Appendix A.1.

In choosing how to code survey responses, researchers face a trade-off. Having binary outcomes facilitates the comparison of the effect sizes across different outcomes, and allows one to use simple methods such as linear probability models or probit or logit. The downside is that binary indicators may not capture subtle changes in a person’s perceptions. For example, if a person’s self-reported trust changes from “agree completely [that people can generally be trusted]” to “agree”, our binary indicator would not capture this change. The alternative would be to have several mutually exclusive indicators for the different answer options, and to use appropriate methods such as multinomial logit. In our judgment, it is more useful to have results that are comparable across outcomes and to use simpler yet more transparent methods, which is why we choose to construct binary indicators.

The SOEP includes geo-identifiers for a person’s county of residence. We link the survey data to the asylum seeker inflow data based on a person’s county of residence in 2014, the year before the

**SOEP – Descriptive Evidence** Figure 4 displays the means of the outcome variables in the survey waves before and after the inflow (2013 and 2018). Each panel reports the share of people whose outcome takes value one, split by people residing in counties with above- and below-median increases in the number of asylum seekers. For five out of seven questions, there is a difference in responses before the inflow. Before the inflow, people in low-inflow areas are more likely to agree that other

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