Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.
This paper presents a method for inferring the distribution of voter ideal points on a single dimension from individual-level binary choice data. The statistical model and estimation technique draw heavily on the psychometric literature on test taking and, in particular, on the work of Bock and Aitkin (1981) and are similar to several recent methods of estimating legislative ideal points (Londregan 2000; Bailey 2001). I present Monte Carlo results vali dating the method. The method is then applied to determining the partisan and ideological basis of support for presidential candidates in 1992 and to U.S. mass and congressional partisan realignment on abortion policy since 1973. Parties, candidates, and voters are becoming increasingly engaged in political conversations through the micro-blogging platform Twitter. In this paper I show that the structure of the social networks in which they are embedded has the potential to become a source of information about policy positions. Under the assumption that social networks are homophilic (, 2001), this is, the propensity of users to cluster along partisan lines, I develop a Bayesian Spatial Following model that scales Twitter users along a common ideological dimension based on who they follow. I apply this network-based method to estimate ideal points for Twitter users in the US, the UK, Spain, Italy, and the Netherlands. The resulting positions of the party accounts on Twitter are highly correlated with oine measures based on their voting records and their manifestos. Similarly, this method is able to successfully classify individuals who state their political orientation publicly, and a sample of users from the state of Ohio whose Twitter accounts are matched with their voter registration history. To illustrate the potential contribution of these estimates, I examine the extent to which online behavior is polarized along ideological lines. Using the 2012 US presidential election campaign as a case study, I nd that public exchanges on Twitter take place predominantly among users with similar viewpoints. Little is known about the American public’s policy preferences at the level of Congressional districts, state legislative districts, and local municipalities. In this article, we overcome the limited sample sizes that have hindered previous research by jointly scaling the policy preferences of 275,000 Americans based on their responses to policy questions. We combine this large dataset of Americans’ policy preferences with recent advances in opinion estimation to estimate the preferences of every state, congressional district, state legislative district, and large city. We show that our estimates outperform previous measures of citizens’ policy preferences. These new estimates enable scholars to examine representation at a variety of geographic levels. We demonstrate the utility of these estimates through applications of our measures to examine representation in state legislatures and city governments. Despite the centrality of the median voter prediction in political economy models, overwhelming empirical evidence shows that legislators regularly take positions that diverge significantly from the preferences of the median voter in their districts. However, all these empirical studies to date lack the necessary data to directly measure the preferences of the median voter. We utilize a unique data set consisting of individual-level voting data that allows us to construct direct measures of voter preferences. We find that legislators are most constrained by the preferences of the median voter in homogeneous districts.