Crime and the electoral success of radical right parties

Rising public approval and electoral gains for radical right parties and populist movements contest liberal democracies all over Europe. By positioning themselves as law and order parties together with a pronounced framing of the migration crisis in terms of security threats, radical right parties aim to obtain issue ownership on immigration and link it with crime. The strategic use of agenda setting and priming to combine these issues is main part of the electoral strategy of populist radical right parties. On the empirical side, the findings are mixed and scarce. Neither the effect immigration, nor the effect of crime on the electoral success of radical right parties is uncontested. Potential sources for the inconclusiveness of these findings are differences in scale and level of aggregation, heterogeneous operationalization of the theoretical constructs, and the complex interaction between the variables at play. Our contribution connects to this research puzzle. We ask, whether and how crime has an influence on the success of populist radical right parties and how this effect is moderated by the local presence of immigrants. Based on previous research, we assume that immigration evokes a perceived threat within parts of the electorate, which leads to increased vote proportions of populist radical right parties. We extend the state research as we study the proposed effects on the local level at several elections at three levels of the political system over seven years: national, state, and local elections in the state of Baden-Württemberg, Germany. As official crime statistics in Germany are published only on the district level, we make use of a corpus of over 500.000 police press reports published since 2015 by police departments in the state of Baden-Wuerttemberg, Germany. To be able to match the crime reports with the electoral results on municipal level, first, the documents are geolocated. In a second step, human coders annotate a sample of the corpus for text classification. The labeled data are then used for supervised machine learning to classify the documents regarding relevant crimes. The crimes that are identified by the classified documents are aggregated on the level of municipal administrative units. With this measure of crime prevalence on the local level, we are able to test the local influence of reported crime on the local vote shares of populist radical right parties and its interaction with immigration. As controls, we account for potential confounders like urbanization and, wealth. Preliminary results reveal heterogeneous relationship between reported crime and votes for radical right parties. Comparing elections over time allows us to see, whether and how these effects change over time.