Do Poor Citizens Vote for Redistribution, Against Immigration or Against the Establishment? A Conjoint Experiment in Denmark

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Abstract

What is the link between socioeconomic disadvantage and vote choice? The literature on this question is fragmented and points to motivations based on welfare policy, immigration policy or anti‐establishment sentiments. To test which of these motives explains differences in voting behaviour between classes, a conjoint experiment in which fictitious candidates present randomly assigned positions was designed. The experiment evaluated the relative importance of the position on welfare, immigration and anti‐establishment as well as candidates’ occupational background. By splitting the analyses into lower, middle‐ and upper class voters, it was found that lower class voters are most distinct from other voters in their preferences for anti‐establishment candidates. Strikingly, lower class voters even support welfare retrenchment, as long as it is an anti‐establishment candidate proposing it. The experiment also found a general tendency to vote against career politicians across classes and remarkably few differences regarding immigration preferences.

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The Populist Zeitgeist

Cas Mudde (2004)

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Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Jens Hainmueller, Daniel Hopkins, Teppei Yamamoto (2014)

Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show howconjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.