The adage “All politics is local” in the United States is largely true. Of the United States’ 90,106 governments, 99.9% are local governments. Despite variations in institutional features, descriptive representation, and policy-making power, political scientists have been slow to take advantage of these variations. One obstacle is that comprehensive data on local politics is often extremely difficult to obtain; as a result, data is unavailable or costly, hard to replicate, and rarely updated. We provide an alternative: crowdsourcing this data. We demonstrate and validate crowdsourcing data on local politics using two different data collection projects. We evaluate different measures of consensus across coders and validate the crowd’s work against elite and professional datasets. In doing so, we show that crowdsourced data is both highly accurate and easy to use. In doing so, we demonstrate that nonexperts can be used to collect, validate, or update local data.

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.

Researchers employing qualitative comparative analysis (QCA) and its variants use two-element Boolean algebra to compare cases and identify putative causal conditions. I show that the two-element Boolean algebra constrains research in three important ways: it restricts what we can say about sets and the interactions between sets, it embodies a logical language that is too weak to capture modern social science theories, and it restricts our analysis of causation to necessity and sufficiency accounts and does not allow for counterfactuals. Modern quantitative analysis suffers none of these restrictions and provides a much richer way to understand the social world.

Word embeddings, the coefficients from neural network models predicting the use of words in context, have now become inescapable in applications involving natural language processing. Despite a few studies in political science, the potential of this methodology for the analysis of political texts has yet to be fully uncovered. This paper introduces models of word embeddings augmented with political metadata and trained on large-scale parliamentary corpora from Britain, Canada, and the United States. We fit these models with indicator variables of the party affiliation of members of parliament, which we refer to as party embeddings. We illustrate how these embeddings can be used to produce scaling estimates of ideological placement and other quantities of interest for political research. To validate the methodology, we assess our results against indicators from the Comparative Manifestos Project, surveys of experts, and measures based on roll-call votes. Our findings suggest that party embeddings are successful at capturing latent concepts such as ideology, and the approach provides researchers with an integrated framework for studying political language.

Power law distributions have a certain appeal to researchers, not least because they often insinuate a general empirical law. Hence, searching for them in data generated by social and political processes has become popular. In the political science literature, however, power law behavior has rarely been assessed rigorously. Relying mainly on qualitative appraisals of log–log plots, merely a necessary, but no sufficient, condition of power law behavior is tested. This letter therefore seconds the note of caution expressed recently. Moreover, it showcases the use of a principled statistical framework to test power law behavior in a quantitative manner. Applying this method to a seminal case in political science, the results of the analysis invite an empirical as well as theoretical refinement of the claim that changes in public budgets follow a power law. In a more general sense, the letter wishes to contribute to a more thorough practice of stochastic process methods in political science.

Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.

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.

This letter deals with a very simple question: if we have grouped data with a binary-dependent variable and want to include fixed effects in the specification, can we meaningfully compare results using a linear model to those estimated with a logit? The reason to doubt such a comparison is that the linear specification appears to keep all observations, whereas the logit drops the groups where the dependent variable is either all zeros or all ones. This letter demonstrates that a linear specification averages the estimates for all the homogeneous outcome groups (which, by definition, all have slope coefficients of zero) with the slope coefficients for the groups with a mix of zeros and ones. The correct comparison of the linear to logit form is to only look at groups with some variation in the dependent variable. Researchers using the linear specification are urged to report results for all groups and for the subset of groups where the dependent variable varies. The interpretation of the difference between these two results depends upon assumptions which cannot be empirically assessed.

While the spatial weights matrix $\boldsymbol{W}$ is at the core of spatial regression models, there is a scarcity of techniques for validating a given specification of $\boldsymbol{W}$. I approach this problem from a measurement error perspective. When $\boldsymbol{W}$ is inflated by a constant, a predictable form of endogeneity occurs that is not problematic in other regression contexts. I use this insight to construct a theoretically appealing test and control for the validity of $\boldsymbol{W}$ that is tractable in panel data, which I call the K test. I demonstrate the utility of the test using Monte Carlo simulations.

I develop a procedure for estimating local-area public opinion called stacked regression and poststratification (SRP), a generalization of classical multilevel regression and poststratification (MRP). This procedure employs a diverse ensemble of predictive models—including multilevel regression, LASSO, k-nearest neighbors, random forest, and gradient boosting—to improve the cross-validated fit of the first-stage predictions. In a Monte Carlo simulation, SRP significantly outperforms MRP when there are deep interactions in the data generating process, without requiring the researcher to specify a complex parametric model in advance. In an empirical application, I show that SRP produces superior local public opinion estimates on a broad range of issue areas, particularly when trained on large datasets.