Narcissistic Rivalry, Anger, and Radical Right Populist Party Support

In the last two decades, radical right populist parties (RRP) have successfully established themselves in many Western and Central European countries. As a consequence, researchers have become increasingly interested in the drivers for supporting such parties. Initially, many studies focused on short-term explanations why voters might vote for such a party, e.g. economic, cultural, and status threat perceptions or preferences for specific policies. More recently, a growing number of studies has focused on the general psychological factors underpinning this support. These studies explore stable personality traits such as the five factor model or narcissism, and show that a considerable proportion of the population is, at least in principle, receptive to the appeal of RRPs. But while the focus on the psychological underpinnings of RRP support allows us to understand its relationship with radical right populist vote choice in a more thorough way, it also raises a more general question of political psychology: What are the mechanisms through which stable personality traits are translated into political perceptions, attitudes, and behaviors? While some personality traits are in general connected to RRP support, not all individuals with these traits support these parties. Conversely, not all supporters of RRPs have the same personality structure. In this paper, we explore this question by focusing on the relationship between narcissistic rivalry, anger, and RRP support. Combining affective intelligence theory and the Narcissistic Admiration and Rivalry Concept, we argue narcissists, especially those with high levels of narcissistic rivalry, are more likely to express anger when their social status is threatened, which in turn increases their affinity for the populist radical-right Alternative for Germany (AfD). We explore this relationship by using high-quality panel data from a mixed-mode access panel representative of the German population (GESIS panel) and estimating autoregressive, cross-lagged panel models.