Pathways to Populism: Economics, Culture, and Ideological Convergence
- Willis, Nicholas Thomas
- Advisor(s): Indridason, Indridi H
This dissertation proposes a variation in motivations for voting for left and right populist parties, respectively. It argues voting for both types of populist parties is motivate by disaffection with government policies and perceived ideological convergence - the perception that mainstream parties are essentially ideologically interchangeable on issues relevant to them. Where the pathways to populist voting diverge, however, is argued to be based on the issue type for which the voter has become disaffected. It is argued left populist voters are disaffected with the economy, while right populist voters are disaffected by cultural policies (e.g. immigration). The respective populist party types are argued to own these issue spaces, based on the frequency and fervency with which they address them, giving them authority on the matter. The dissertation explores these claims through the use of a mixed-methods design. The first part of the dissertation explores the topic through statistical analysis. The association between ideological convergence, government failure on cultural issues and right populist voting finds positive support. The association between ideological convergence, government failure on economic issues and left populist voting does not find support. This result was likely due to a lack of data and cases – something which can be remedied with more of both in the future. Case studies of the Front National in France (right populism) and Podemos in Spain (left populism) are then conducted. The French case study tests the mechanisms suggested by the theory of the dissertation to ensure that the positive association of the statistical analysis was due to the hypothesized factors. The Spanish case study test the mechanisms suggested by the theory of the dissertation to offer evidence that the relationship is functioning as hypothesized, despite the null findings of the left populism statistical model. The dissertation concludes by discussing its findings and contributions.