What Drives Local Xenophobia: Evidence from Naturalization Decisions in Swiss Municipalities
Research project funded by the Swiss National Science Foundation (CHF/USD 200'000). Co-PI (with Jens Hainmueller and Marco Steenbergen).
Immigration is a long-simmering policy issue in Switzerland and many other countries. One of the most controversial aspects of immigration policy is the naturalization of immigrants, since citizenship is often tied to important rights. Supporters tout liberal naturalization rules as an effective vehicle to facilitate the integration of migrants. But many others reject the integration of immigrants as citizens because they believe that naturalizations amplify the negative consequences of immigration. Citizenship is seen as a privilege that is not to be bestowed upon immigrants who primarily poach jobs from native workers, balkanize local communities, and undermine traditional values. Why do some people oppose and others favor immigration and the naturalization of immigrants? Existing research has generated inconsistent findings and no clear consensus view. Scholars are deeply divided over the relative importance of economic, political, and cultural considerations that may motivate anti-immigrant sentiment.
The goal of our study is to contribute to the literature on opposition to immigration by providing new micro-level evidence from naturalization decisions in Swiss municipalities. Naturalization applications in Switzerland are largely decided at the local level where municipalities use many different rules to decide on citizenship applications. These rules range from secret ballot referenda, to voting in citizens’ assemblies, city councils, or executive commissions. This unique arrangement not only aroused heated political debates in recent decades, but also generated a wealth of invaluable behavioral data on anti-immigrant sentiment comprising decisions over thousands of immigrants with radically different backgrounds and attributes. Our goal is to create a new, multi-layered dataset that for the first time collects detailed information at the applicant level (e.g. age, gender, race, country of origin, occupation, etc.) and links it with detailed data at the municipality level (e.g. application regime, local party strength, labor market conditions, etc.). We aim to measure this data across a large, representative set of municipalities; extracting data as far back as it is stored in the local archives.
This dataset will allow us to address fundamental questions pertaining to the drivers of local xenophobia based on behavioral data and credible cross-applicant and cross-municipality comparisons. These questions include which factors (economic, political, or cultural) explain successful applications, whether there is systematic discrimination against particular types of immigrants, how the institutional arrangements affect application decisions and aggregate rejection rates, and how these contextual institutional effects vary across particular types of immigrants and across underlying community characteristics.
Project period 2010-2012