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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.

AbstractConventional 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.

AbstractRecent 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.

How voters use political issues to elect political candidates is of central importance to our understanding of democratic representation. Research on voting behavior often assumes that American voters hold distinct economic and cultural issue preferences. In this research note, we point out that this does not necessarily imply that preferences for candidates' positions on the two issue dimensions are also additively separable in voters' decisions. Analyzing survey data on US presidential elections from 1996 to 2016, we estimate to what extent voters' economic and socio-cultural preferences are nonseparable and find that the two general dimensions act as substitutes in their decisions. Our finding implies that voting decisions are partially structured by an underlying single dimension, as liberal deviations from a voter's ideal point on social issues can be compensated by conservative deviations on economic issues.

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.

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.

AbstractThe free movement of people is a fundamental principle of the European Union (EU) that has led to an increase in EU‐internal migration. This study investigates the impact of increased immigration to Germany resulting from the 2004 and 2007 eastern enlargement of the EU on concerns about immigration within the German population. By merging 20 years of annual migration statistics with panel data on individual attitudes and exploiting exogenous variation in the gradual enlargement of the free movement policy, we examine the causal effects of EU‐internal migration on immigration concerns. Our findings suggest that the influx of immigrants from new member states did not have a clear average effect on concerns about immigration, but increased concerns among German natives with materialist‐survival values. The study provides insights into the societal division caused by opposition to immigration as part of the European integration process.

We present the PARTYPRESS Database, which compiles more than 250,000 published press releases from 68 parties in 9 European countries. The database covers the press releases of the most relevant political parties in these countries from 2010 onward. It provides a supervised machine learning classification of press releases into 21 unique issue categories according to a general codebook. The PARTYPRESS Database can be used to study parties' issue agendas comparatively and over time. We extend a recent analysis in Gessler and Hunger (2022) to illustrate the usefulness of the database in studying dynamic party competition, communication, and behavior.

AbstractVoters' beliefs about the strength of political parties are a central part of many foundational political science theories. In this article, we present a dynamic Bayesian learning model that allows us to study how voters form these beliefs by learning from pre-election polls over the course of an election campaign. In the model, belief adaptation to new polls can vary due to the perceived precision of the poll or the reliance on prior beliefs. We evaluate the implications of our model using two experiments. We find that respondents update their beliefs assuming that the polls are relatively imprecise but still weigh them more strongly than their priors. Studying implications for motivational learning by partisans, we find that varying adaptation works through varying reliance on priors and not necessarily by discrediting a poll's precision. The findings inform our understanding of the consequences of learning from polls during political campaigns and motivational learning in general.