Recent scholarship on affective polarization documents partisan animosity in people's everyday lives. But does partisan dislike go so far as to deny fundamental rights? We study this question through a moral dilemma that gained notoriety during the COVID-19 pandemic: triage decisions on the allocation of intensive medical care. Using a conjoint experiment in five countries we analyze the influence of patients’ partisanship next to commonly discussed factors determining access to intensive medical care. We find that while participants’ choices are consistent with a utilitarian heuristic, revealed partisanship influences decisions across most countries. Supporters of left or right political camps are more likely to withhold support from partisan opponents. Our findings offer comparative evidence on affective polarization in non-political contexts.

Conventional multidimensional statistical models of roll call votes assume that legislators’ preferences are additively separable over dimensions. In this article, we introduce an item response model of roll call votes that allows for non-separability over latent dimensions. Conceptually, non-separability matters if outcomes over dimensions are related rather than independent in legislators’ decisions. Monte Carlo simulations highlight that separable item response models of roll call votes capture non-separability via correlated ideal points and higher salience of a primary dimension. We apply our model to the U.S. Senate and the European Parliament. In both settings, we find that legislators’ preferences over two basic dimensions are non-separable. These results have general implications for our understanding of legislative decision-making, as well as for empirical descriptions of preferences in legislatures.

Citizens’ beliefs about uncertain events are fundamental variables in many areas of political science. While beliefs are often conceptualized in the form of distributions, obtaining reliable measures in terms of full probability densities is a difficult task. In this letter, we ask if there is an effective way of eliciting beliefs as distributions in the context of online surveys. Relying on experimental evidence, we evaluate the performance of five different elicitation methods designed to capture citizens’ uncertain expectations. Our results suggest that an elicitation method originally proposed by Manski (2009) performs well. It measures average citizens’ subjective belief distributions reliably and is easily implemented in the context of regular (online) surveys. We expect that a wider use of this method will lead to considerable improvements in the study of citizens’ expectations and beliefs.

Recent research on electoral behavior has suggested that policy-informed vote choices are frequently obstructed by uncertainty about party positions. Given the significance of clear and distinct party platforms for meaningful representation, several studies have investigated the conditions under which parties are perceived as ambiguous. Yet previous studies have often relied on measures of perceived positional ambiguity that are fairly remote from the concept, casting doubt on their substantive conclusions. This article introduces a statistical model to estimate a comprehensive measure of perceived ambiguity that incorporates the two principal factors: non-positions and positional inconsistency. The two-faces model employs issue perceptions in an item response framework to explicitly parametrize the perceived ambiguity of party positions. The model is applied to data from the Chapel Hill Expert Survey and subsequently associated with party characteristics that drive perceptions of party ambiguity. The results suggest that (a) there are notable differences between the proposed and competing measures, highlighting the need to be mindful of the intricacies of political information processing in research on perceptions of ambiguity and (b) involuntary ambiguity might be an underexplored explanation for unclear party perceptions.

This letter investigates how voter transitions between parties affect parties’ policy positioning. While a growing literature investigates the role of election results as signals for parties’ policy adaption, it has mostly focused on vote changes of individual parties. However, parties do not know only whether they have won or lost in an election; they also have detailed information on which parties they won votes from and which parties they lost votes to. We make two arguments about how voter transitions should affect the strategic policy choices of political parties. First, when a party has lost votes to another party it will adapt its policy positions toward that party. Second, parties that have overall lost more votes become more likely to adapt their positions. Making use of a data set on individual voter transitions and party positions we can demonstrate that voter transitions indeed affect parties’ competitive behavior.

We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.

The application of spatial voting theories to popular elections presupposes an electorate that chooses political representatives on the basis of their well-structured policy preferences. Behavioral researchers have long contended that parts of the electorate instead hold unstructured and inconsistent policy beliefs. This article proposes an extension to spatial voting theories to analyze the effect of varying consistency in policy preferences on electoral behavior. The model results in the expectation that voters with less consistent policy preferences will put less weight on policy distance when learning about candidates who should represent their political positions. The study tests this expectation for the 2008 US presidential election, and finds that for respondents with less consistent self-placements on the liberal–conservative scale, policy distance less strongly affects their voting decision. The results have implications for the quality of political representation, as certain parts of the electorate are expected to be less closely represented.

In most multidimensional spatial models, the systematic component of agent utility functions is specified as additive separable. We argue that this assumption is too restrictive, at least in the context of spatial voting in mass elections. Here, assuming separability would stipulate that voters do not care about how policy platforms combine positions on multiple policy dimensions. We present a statistical implementation of Davis, Hinich, and Ordeshook's (1970) Weighted Euclidean Distance model that allows for the estimation of the direction and magnitude of non-separability from vote choice data. We demonstrate in a Monte-Carlo experiment that conventional separable model specifications yield biased and/or unreliable estimates of the effect of policy distances on vote choice probabilities in the presence of non-separable preferences. In three empirical applications, we find voter preferences on economic and socio-cultural issues to be non-separable. If non-separability is unaccounted for, researchers run the risk of missing crucial parts of the story. The implications of our findings carry over to other fields of research: checking for non-separability is an essential part of robustness testing in empirical applications of multidimensional spatial models.