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Explaining and Predicting the Rise of a Radical Right-Wing Party Using Gaussian Processes

The use of classical regression techniques in social science can prevent the discovery of complex, non-linear mechanisms, and often relies too heavily on both the expertise and prior expectations of the data analyst. In this paper, we present a regression methodology that combines the interpretability of traditional, well used, statistical methods with the full predictability and flexibility of more recent machine learning techniques. Our modeling approach allow us to find and explain the mechanisms behind the rise of Radical Right-wing Populist parties (RRPs), that we would have been unable to find using traditional methods. Using Swedish municipality level data (2002-2014) we find no evidence, in direct contrast to earlier results, that `ethnic competition theory' explains the rise of the RRP, and we only find support for the `social marginality theory' for education levels. Instead, spatial location (latitude \& longitude), time, and population density (together with education) are the significant variables that impact the change in support for the RRP, all other are redundant. We argue that our methodology, which produces models with considerably better fit of the complexity and non-linearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements.