Empirical applications of the spatial theory of elections typically rely on the discrete choice framework to arrive at probabilistic voting models. Whereas in the classic model voter choice is solely a function of spatial proximity, neo-Downsian models also incorporate voter-specific nonpolicy attributes, which are represented by sociodemographic characteristics. One prominent line of such probabilistic models, Schofield’s Valence Model, additionally includes party valences into voter utility functions. The model rests on the estimated party intercepts to measure the valence advantages empirically. The party intercepts are ordered based on their values, and then this valence ranking is used further to predict equilibrium locations. The paper demonstrates that this measurement strategy does not provide unique results in fully specified models due to central properties of discrete choice models and the specific nature of party intercepts in these models. Drawing on a simple example based on mass election surveys from Germany, it is shown that the valence ranking, the crucial factor to investigate how valence differences affect the nature of spatial competition, is highly sensitive to arbitrary coding decisions. As a consequence, it is impossible to represent valence with the constants and to infer something substantial from the resulting valence ranking.

The use of cluster robust standard errors (CRSE) is common as data are often collected from units, such as cities, states or countries, with multiple observations per unit. There is considerable discussion of how best to estimate standard errors and confidence intervals when using CRSE (Harden 2011; Imbens and Kolesár 2016; MacKinnon and Webb 2017; Esarey and Menger 2019). Extensive simulations in this literature and here show that CRSE seriously underestimate coefficient standard errors and their associated confidence intervals, particularly with a small number of clusters and when there is little within cluster variation in the explanatory variables. These same simulations show that a method developed here provides more reliable estimates of coefficient standard errors. They underestimate confidence intervals for tests of individual and sets of coefficients in extreme conditions, but by far less than do CRSE. Simulations also show that this method produces more accurate standard error and confidence interval estimates than bootstrapping, which is often recommended as an alternative to CRSE.

Information about the ideological positions of different political actors is crucial in answering questions regarding political representation, polarization, and voting behavior. One way to obtain such information is to ask survey respondents to place actors on a common ideological scale, but, unfortunately, respondents typically display a set of biases when performing such placements. Key among these are rationalization bias and differential item functioning (DIF). While Aldrich–McKelvey (AM) scaling offers a useful solution to DIF, it ignores the issue of rationalization bias, and this study presents Monte Carlo simulations demonstrating that AM-type models thus can give inaccurate results. As a response to this challenge, this study develops an alternative Bayesian scaling approach, which simultaneously estimates DIF and rationalization bias, and therefore performs better when the latter bias is present.

Electoral boundaries are an integral part of election administration. District boundaries delineate which legislative election voters are eligible to participate in, and precinct boundaries identify, in many localities, where voters cast in-person ballots on Election Day. Election officials are tasked with resolving a tremendously large number of intersections of registered voters with overlapping electoral boundaries. Any large-scale data project is susceptible to errors, and this task is no exception. In two recent close elections, these errors were consequential to the outcome. To address this problem, we describe a method to audit the assignment of registered voters to districts. We apply the methodology to Florida’s voter registration file to identify thousands of registered voters assigned to the wrong state House district, many of which local election officials have verified and rectified. We discuss how election officials can best use this technique to detect registered voters assigned to the wrong electoral boundary.

After seeking a “manageable standard” to apply to claims of partisan gerrymandering for over three decades, the Supreme Court has finally given up the chase, ruling that such claims are nonjusticiable. What is to be done? An extended history of successful congressional action suggests that the legislative pathway is more practical than often believed. Statutory requirements also make it possible to consider a broader suite of districting objectives. This paper presents a flexible new software and a framework for evaluating the practical implications of explicit objectives. I apply this approach to the conditions last required by Congress, generating equipopulous, contiguous, and compact districts. Among these conditions, the formal definition of compactness has proven contentious. Does it matter? I contrast the representation of the political parties and of racial and ethnic minorities under plans optimized according to 18 different definitions of compactness. On these grounds, the definitions are markedly consistent. These methods may be extended to alternative districting objectives and criteria.

Political scientists often wish to classify documents based on their content to measure variables, such as the ideology of political speeches or whether documents describe a Militarized Interstate Dispute. Simple classifiers often serve well in these tasks. However, if words occurring early in a document alter the meaning of words occurring later in the document, using a more complicated model that can incorporate these time-dependent relationships can increase classification accuracy. Long short-term memory (LSTM) models are a type of neural network model designed to work with data that contains time dependencies. We investigate the conditions under which these models are useful for political science text classification tasks with applications to Chinese social media posts as well as US newspaper articles. We also provide guidance for the use of LSTM models.

We consider evidence for the assertion that backbench members of parliament (MPs) in the UK have become less distinctive from one another in terms of their speech. Noting that this claim has considerable normative and substantive implications, we review theory and findings in the area, which are ultimately ambiguous on this question. We then provide a new statistical model of distinctiveness that extends traditional efforts to statistically characterize the “style” of authors and apply it to a corpus of Hansard speeches from 1935 to 2018. In the aggregate, we find no evidence for the claim of more homogeneity. But this hides intriguing covariate effects: at the MP-level, panel regression results demonstrate that on average, more senior backbenchers tend to be less “different” in speech terms. We also show, however, that this pattern is changing: in recent times, it is more experienced MPs who speak most distinctively.

Instrumental-variable (IV) estimation is an essential method for applied researchers across the social and behavioral sciences who analyze randomized control trials marred by noncompliance or leverage partially exogenous treatment variation in observational studies. The potential outcome framework is a popular model to motivate the assumptions underlying the identification of the local average treatment effect (LATE) and to stratify the sample into compliers, always-takers, and never-takers. However, applied research has thus far paid little attention to the characteristics of compliers and noncompliers. Yet, profiling compliers and noncompliers is necessary to understand what subpopulation the researcher is making inferences about and an important first step in evaluating the external validity (or lack thereof) of the LATE estimated for compliers. In this letter, we discuss the assumptions necessary for profiling, which are weaker than the assumptions necessary for identifying the LATE if the instrument is randomly assigned. We introduce a simple and general method to characterize compliers, always-takers, and never-takers in terms of their covariates and provide easy-to-use software in R and STATA that implements our estimator. We hope that our method and software facilitate the profiling of compliers and noncompliers as a standard practice accompanying any IV analysis.