Correctly measuring district preferences is crucial for empirical research on legislative responsiveness and voting behavior. This article argues that the common practice of using presidential vote shares to measure congressional district ideology systematically produces incorrect estimates. I propose an alternative method that employs multiple election returns to estimate voters' ideological distributions within districts. I develop two estimation procedures—a least squared error model and a Bayesian model—and test each with simulations and empirical applications. The models are shown to outperform vote shares, and they are validated with direct measures of voter ideology and out-of-sample election predictions. Beyond estimating district ideology, these models provide valuable information on constituency heterogeneity—an important, but often immeasurable, quantity for research on representatives— strategic behavior.

Political parties field heterogeneous candidates and send a variety of messages about their policy positions. Yet most voting models maintain that office-seeking parties should enforce intraparty homogeneity and cultivate clear party reputations. This article reconciles theory with reality by identifying a strategic rationale for parties to pursue heterogeneity. I develop a model in which two parties each select a distribution of potential candidates to compete in an upcoming election. The model demonstrates that well-positioned parties should indeed offer homogeneous candidate teams, but that parties with platforms distant from the median voter should cast a wide net. Extensions allow for multiple candidate signals, voters who care about party platforms and candidates’ positions, and voter uncertainty.

This paper develops a model of voter turnout that embeds Riker and Ordeshooks (1968 Am. Pol. Sci. Rev. 62, 25-42 (doi:10.2307/1953324)) calculus of voting into the context of a social network. In the model, an individuals expressive benefits to voting depend on the behaviour of their social contacts. We show that there may be multiple equilibria and analyse how these equilibria depend on the structure of the network. We discuss six empirical implications of the model for turnout, some of which suggest novel answers to longstanding puzzles in the turnout literature, such as: why are higher income individuals more likely to vote even in cases when registration costs are low? Why is turnout so difficult to predict? Why does lowering registration costs disproportionately increase turnout among high-income voters? And why do we observe inertia in turnout across elections?

This paper develops and tests arguments about how national-level social and institutional factors shape the propensity of individuals to form attachments to political parties. Our tests employ a two-step estimation procedure that has attractive properties when there is a binary dependent variable in the first stage and when the number of second-level units is relatively small. We find that voters are most likely to form party attachments when group identities are salient and complimentary. We also find that institutions that assist voters in retrospectively evaluating parties—specifically, strong party discipline and few parties in government—increase partisanship. These institutions matter most for those individuals with the fewest cognitive resources, measured here by education.

Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing-an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises.