Inferring the dynamics of rising radical right-wing party support using Gaussian processes

Abstract

The use of classical regression techniques in social science can prevent the discovery of complex, nonlinear 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 Bayesian statistics techniques. Our modelling approach allows 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 (2002–2018), we find no evidence that the proportion of foreign-born residents is predictive of increases in RRP support. Instead, education levels and population density are the significant variables that impact the change in support for the RRP, in addition to spatial and temporal control variables. We argue that our methodology, which produces models with considerably better fit of the complexity and nonlinearities often found in social systems, provides a better tool for hypothesis testing and exploration of theories about RRPs and other social movements. This article is part of the theme issue ‘Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences’.

Coupling functions: dynamical interaction mechanisms in the physical, biological and social sciences

“…They construct the relevant coupling functions between two tropical climate indices—the El Niño/Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD)—to interpret the mutual interactions between these two air–sea interaction phenomena in the Pacific and Indian Oceans. Taking our final example from social science, Blomqvist et al present a technique based on Gaussian processes for inferring the dynamics of a rising radical right-wing political party [88].…”