Much of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975 to 2005. We illustrate the measure's utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.

Cognitively demanding electoral systems increase the chance that voters make their choices based on politically irrelevant cues. To illustrate this argument, I analyze the effect of candidate name complexity—a visual cue that contains no politically meaningful information—in Japan, where voters need to write their preferred candidate's name on a blank ballot paper. I find that when electoral systems require voters to weigh a large number of candidates and simultaneously reduce the usefulness of partisan cues, candidates with more complex names tend to receive lower vote shares. By contrast, under less cognitively demanding systems, candidate name complexity has no effect on election outcomes. These findings have important implications for the debate on the “best” electoral system design.

In an effort to break the link between districts' lack of competitiveness and the election of ideologues, Washington and California recently adopted the “top-two” primary election system. Among other features, the top-two primary allows members of the same party to run against one another in the general election. Although proponents argue that this system encourages the election of more moderate candidates in highly partisan districts, early reports have uncovered mixed evidence of this effect. This study addresses this puzzle by first disentangling the conditions under which one should expect such primaries to encourage the election of more moderate candidates. Using election returns data from the 2008 through 2014 elections, I find that districts facing same-party general-election competition do elect more moderate legislators than similar districts not subject to same-party competition. However, using an application of a common regression discontinuity diagnostic test, I also find that elite actors appear able to strategically avoid this kind of competition—partially explaining why broader effects of the top-two have not been uncovered. The findings contribute not only to ongoing debates about the effectiveness of the top-two primary, but also to our understanding of how political elites may maneuver institutional changes to their own benefit.

We developed a maximum likelihood estimator corresponding to the predicted hazard rate that emerges from a continuous time game of incomplete information with a fixed time horizon (i.e., Kreps and Wilson, 1982, Journal of Economic Theory27, 253–279). Such games have been widely applied in economics and political science and involve two players engaged in a war of attrition contest over some prize that they both value. Each player can be either a strong or weak competitor. In the equilibrium of interest, strong players do not quit whereas weak players play a mixed strategy characterized by a hazard rate that increases up to an endogenous point in time, after which only strong players remain. The observed length of the contest can therefore be modeled as a mixture between two unobserved underlying durations: one that increases until it abruptly ends at an endogenous point in time and a second involving two strong players that continues indefinitely. We illustrate this estimator by studying the durations of Senate filibusters and international crises.

We use nationwide deed-level records on home foreclosures to examine the effects of economic distress on electoral outcomes and individual voter turnout. County-level difference-in-differences estimates show that counties that suffered larger increases in foreclosures did not punish or reward members of the incumbent president's party more than less affected counties. Linking the Ohio voter file to individual foreclosures, difference-in-differences estimates show that individuals whose homes were foreclosed on were less likely to turn out, rather than being mobilized. However, in 2016 counties more exposed to foreclosures supported Trump at substantially higher rates. Taken together, the evidence suggests that the effect of local economic distress on incumbent performance is generally close to zero and only becomes substantial in unusual circumstances.

The relationship between participation in revolt and individuals' economic conditions is among the most debated in political science. While conventional economic theory suggests that those who face the poorest economic prospects are most inclined to fight, extant evidence is decidedly mixed. We address this puzzling variation by analyzing the interplay between macro-structural conditions and individuals' micro-level circumstances. Under conditions of severe group repression, we show how a “glass-ceiling” logic may operate: among the repressed group, those with relatively high productive potential may be most motivated to revolt. We test this with in-depth analysis of participation in the 1993–2003 Burundian insurgency. The data are consistent with numerous implications of the glass-ceiling logic and inconsistent with extant alternative explanations.

In small samples, maximum likelihood (ML) estimates of logit model coefficients have substantial bias away from zero. As a solution, we remind political scientists of Firth's (1993, Biometrika, 80, 27–38) penalized maximum likelihood (PML) estimator. Prior research has described and used PML, especially in the context of separation, but its small sample properties remain under-appreciated. The PML estimator eliminates most of the bias and, perhaps more importantly, greatly reduces the variance of the usual ML estimator. Thus, researchers do not face a bias-variance tradeoff when choosing between the ML and PML estimators—the PML estimator has a smaller bias and a smaller variance. We use Monte Carlo simulations and a re-analysis of George and Epstein (1992, American Political Science Review, 86, 323–337) to show that the PML estimator offers a substantial improvement in small samples (e.g., 50 observations) and noticeable improvement even in larger samples (e.g., 1000 observations).

Political security is often viewed as a necessary precondition for rulers to develop property-protecting legal institutions. I argue that because these institutions can build political support and generate revenue, domestically insecure rulers may also invest in them. I test this argument using newly collected 12th-century data on the operation of the nascent English common law system. Leveraging the 1192 shipwreck and subsequent kidnap of Richard I as an exogenous shock to domestic political security, I find that the catastrophe appears to have prompted the English Royal Court's short-term deployment to raise political support in areas vulnerable to rebellion. I present suggestive evidence that this effect in vulnerable areas persisted into the medium-term, and appears to have expanded to the rest of the country. Drawing on this and other evidence of changes in Royal Court funding, activity, and organization between 1184 and 1203, I argue that the shock may have helped to bring about a permanent increase in the Court's capacity and accessibility. These findings are relevant to studies of the common law and the political economy of legal institutions generally.

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.

During the political process, the electorate needs to determine the competence of the government by both observing its policy decisions and acquiring information from the media. However, media reports are often criticized for not being independent and truthful. This paper discusses whether the public can determine the quality of a government from media reports. In other words, are media outlets more likely to act as watchdogs or just as “yes men” to the government? This paper argues that, because of reputational concerns, the media usually avoid criticizing the government. The media only report truthfully when the expected competence of the government is sufficiently low and the probability for the voter to learn from other information sources is sufficiently high. Otherwise, media outlets—especially low-quality outlets—will pander to the government in their reporting. Policy bias or media capture is not required for the yes-man problem to prevail.

Signaling games are central to political science but often have multiple equilibria, leading to no definitive prediction. We demonstrate that these indeterminacies create substantial problems when fitting theory to data: they lead to ill-defined and discontinuous likelihoods even if the game generating the data has a unique equilibrium. In our experiments, currently used techniques frequently fail to uncover the parameters of the canonical crisis-signaling game, regardless of sample size and number of equilibria in the data generating process. We propose three estimators that remedy these problems, outperforming current best practices. We fit the signaling model to data on economic sanctions. Our solutions find a novel U-shaped relationship between audience costs and the propensity for leaders to threaten sanctions, which current best practices fail to uncover.

How do changes in Election Day polling place locations affect voter turnout? We study the behavior of more than 2 million eligible voters across three closely-contested presidential elections (2008–2016) in the swing state of North Carolina. Leveraging within-voter variation in polling place location change over time, we demonstrate that polling place changes reduce Election Day voting on average statewide. However, this effect is almost completely offset by substitution into early voting, suggesting that voters, on average, respond to a change in their polling place by choosing to vote early. While there is heterogeneity in these effects by the distance of the polling place change and the race of the affected voter, the fully offsetting substitution into early voting still obtains. We theorize this is because voters whose polling places change location receive notification mailers, offsetting search costs and priming them to think about the election before election day, driving early voting.

Though the demographic characteristics of judicial nominees in the United States have gained increased political attention in recent years, relatively little is known about how they affect public opinion toward judicial nominees and courts. We evaluate these relationships in the context of race and gender using a conjoint experiment conducted during a recent vacancy on the U.S. Supreme Court. We find consistent evidence that Americans are more supportive of coracial nominees, particularly among white Republicans and Black Democrats, but no evidence of a similar effect on the basis of gender. Our results have important implications for theories of descriptive representation and suggest limits to its use as a means for generating political support for judicial nominees.

Are policy arguments more or less persuasive when they are made by female politicians? Using a diverse sample of American respondents, we conduct a survey experiment which randomly varies the gender associated with two co-partisan candidates across four policy debates. We find strong effects contingent on respondent partisanship and gender, most notably on the issue of access to birth control. On this issue, regardless of the candidate's stance, Democratic respondents, particularly Democratic men, are much more likely to agree with the female candidate. Conversely, Republican respondents, particularly Republican women, are much more likely to agree with the male candidate. We discuss the implications of our findings for the meaning of gender as a heuristic in a highly partisan environment.

The use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

Existing research on the revolving door examines why employers hire former politicians. I complement this demand-side approach by demonstrating the importance of the supply-side. In particular, I argue that one important institutional factor that shapes politicians' willingness to leave office for a private sector job is campaign finance legislation. Less restrictive rules increase campaign spending for incumbents, which makes revolving door employment less attractive. Empirically, I use novel data from the US states and a difference-in-differences design to show that the exogenous removal of campaign finance legislation through Citizens United reduced the probability that incumbents left office to work as lobbyists. The supply-side approach provides insights into comparative differences in the prevalence of the revolving door.

Interactions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.

Loyalty is a permanent concern to dictators. It is unclear, however, whether loyalty to a dictator assures the ruling group's cohesiveness. This study shows that authoritarian political elites under promotion pressure, while remaining loyal to their superior, also compete within factions to outrival their peers. Exploiting data on Chinese provincial leaders and local media reports on corruption investigations and industrial accidents (2000–2014), we find that Chinese elites promote negative news related to their co-faction peers as frequently as - or, depending on the measure, even more often than - they promote similar news regarding members of other factions. We also find that negative reports indeed reduce the promotion probability of reported cadres, while increasing that of reporting ones.

Event data provide high-resolution and high-volume information about political events and have supported a variety of research efforts across fields within and beyond political science. While these datasets are machine coded from vast amounts of raw text input, the necessary dictionaries require substantial prior knowledge and human effort to produce and update, effectively limiting the application of automated event-coding solutions to those domains for which dictionaries already exist. I introduce a novel method for generating dictionaries appropriate for event coding given only a small sample dictionary. This technique leverages recent advances in natural language processing and machine learning to reduce the prior knowledge and researcher-hours required to go from defining a new domain-of-interest to producing structured event data that describe that domain. I evaluate the method with the production of a novel event dataset on cybersecurity incidents.

In the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions.