What is the role of trade policy in military alliances? We analyze and test a game-theoretic model of economic and security cooperation in which allies hold different interests across the security and commercial aspects of the relationship. In equilibrium, allies with little market power who are valuable politically to larger states engage in sociallysuboptimal protectionism, as their allies’ threats of retaliation are incredible. Stable cooperation emerges in the form of unretaliated protection rather than mutually low trade barriers. We test the model’s implications against a dyadic data set of antidumping petitions from 1980 to 2013 and find that larger allies are more likely to tolerate protectionism by smaller allies by denying domestic petitions to retaliate against dumping measures by the latter.

Studies of regime type and war show that democracies tend to win the wars they fight, but questions remain about why this is the case. A simple, if underappreciated, explanation for democratic success is that democracies fight alongside larger and more powerful coalitions. Coalition partners bring additional material capabilities and may also provide intangible benefits to the war effort, such as increased legitimacy or confidence. Democracies may also find it less costly to join coalitions, as democratic war aims may be easier to apportion among the victors without diluting the spoils. Evaluating our hypotheses in a sample of all wars (or all militarized disputes) during the period 1816–2000, we find that democracies fight alongside larger coalitions and that states fighting alongside larger coalitions are more likely to win major contests. Coalition size subsumes most (and in some specifications all) of the direct effect of regime type on victory.

We examine a problem that is confronted frequently by political science researchers seeking to model longitudinal data: what to do when one suspects a lag between the realization of a regressor and its effect on the outcome variable, but one has no theoretical reason to suspect a particular lag length. We examine the theoretical challenges posed by atheoretic lags, review existing methods for atheoretic lag analysis—most notably distributed lag specifications—and their shortcomings, and present an alternative approach for atheoretic lag analysis based on Bayesian model averaging (BMA). We demonstrate the use and utility of our approach with two examples: the litigant signal model in American politics and modernization theory in political economy. Our examples show the increasing difficulty of analyzing models with atheoretic lags as the set of possible specifications increases, and demonstrate the effectiveness of BMA for the modal type of specification in time-series cross-sectional applications.

The possibility that citizens expose themselves to information in biased ways—so-called selective exposure—has acquired new importance as the media environment has evolved to provide more choices concerning what to watch and read. But evidence for the most prominent idea in selective exposure research—that citizens prefer attitude-consistent information—is notably mixed. Methodological challenges likely contribute to the inconclusive nature of findings, as researchers face trade-offs between the artificiality of lab environments and the difficult-to-disentangle confounds of observational analysis. We improve understanding of selective exposure in two ways. First, we consider how message aspects other than attitude-consistency affect exposure decisions. Second, we study selective exposure with an innovative field experiment conducted in the United States that addresses limitations of other approaches. Our results allow us to reach more confident conclusions about the prevalence of motivated selective exposure, and help to illuminate underpinnings of the oft-lamented tendency for campaign media to focus on candidate miscues rather than substantive policy differences.

I exploit the unique institutional framework of Spanish local elections, where municipalities follow different electoral systems depending on their population size, as mandated by a national law. Using a regression discontinuity design, I compare turnout under closed list proportional representation and under an open list, plurality-at-large system where voters can vote for individual candidates from the same or different party-lists. I find that the open list system increases turnout by between 1 and 2 percentage points. The results suggest that open list systems, which introduce competition both across and within parties, are conducive to more voter turnout.

A prominent worry in the measurement of political knowledge is that respondents who say they don’t know the answer to a survey question may have partial knowledge about the topic—more than respondents who answer incorrectly but less than those who answer correctly. It has also been asserted that differentials in respondents’ willingness to guess, driven strongly by personality, can bias traditional knowledge measures. Using a multinomial probit item response model, I show that, contrary to previous claims that “don’t know” responses to political knowledge questions conceal a good deal of “hidden knowledge,” these responses are actually reflective of less knowledge, not only than correct responses but also than incorrect answers. Furthermore, arguments that the meaning of “don’t know” responses varies strongly by respondent personality type are incorrect. In fact, these results hold for high- and low-trait respondents on each of the five most commonly used core personality measures.

Scholarly interest in the role of emotion in accounting for how people react to political figures, events, and messages has escalated over the past two plus decades in political science and psychology. However, research on the validity of the measurement of subjective self-report of emotional responses is rather limited. We introduce here a new measurement approach, a “slider” format and compare it with the long used “radio button” item format. We assess the reliability and validity of these two approaches to the measurement of affect. The study examines self-report measures of emotion to three generated news stories about terrorist threats. We report that both measurement formats are able to extract the expected threefold affect structure from a ten affect word battery. The slider format is, however, modestly more reliable, and more efficient in time to complete, has the ability to limit missing data, and generates continuous data that is less truncated than data derived from the radio button format. Finally, we report on three tests of construct validity. Both approaches exhibit equivalent results on two of those tests. However, the radio button format does poorly on one test of construct validity, that on the anticipated relationship between anxiety and interest in novel information. We present an assessment of two methods for measuring emotional reactions to stimuli such as political issues, political figures, or events. Both methods are suitable for use in online surveys or computer-driven experiments. The traditional method utilizes labeled “radio buttons” that enable a participant in a study to select by clicking on one of an array of typically five response options, ranging from lower to higher of some identified affect term (e.g., how angry one might feel). Second, the slider method offers a participant the ability to move an “arrow” up or down to indicate how much (up) or little (down) they feel. The goal of both measures is to ascertain the level of a targeted emotion, i.e., how little or how much, say anger. The slider method has been specifically developed to be used with participants using a computer. The slider approach falls within the category of visual analog scales. This method for measuring affective responses to stimuli of whatever sort has not hitherto been examined to determine its reliability and validity. The literature on the reliability and validity of these measurement strategies is thin and we found no studies including an explicit comparison.

Ballot initiatives and referendums are increasingly popular methods for addressing important political issues. Studies of voting in these events has found that people rely on party leader and candidate image heuristics when deciding how to cast their ballots. Some analysts have argued that these effects are heterogeneous, being larger for people with lower levels of political knowledge. However, research in experimental economics and political psychology suggests that the impact of heuristics may be greater among more knowledgeable individuals. This paper investigates these rival hypotheses using survey data on voting in a ballot initiative to repeal California’s climate change legislation. Analyses using methods appropriate for studying interaction effects in nonlinear multivariate models demonstrate that candidate heuristics are stronger among more knowledgeable people.

Analyzing the British Election Study from 1964 to 2010, we examine the influence of electoral context on turnout, focusing on the closeness of elections in terms of lagged seat and constituency-level winning margins. Using cross-classified multilevel models to account for individual and contextual factors and disentangle life cycle, cohort- and election-specific effects, we find that closeness strongly affects voting behavior, particularly among new electors. Widening seat margins in British elections over the last decades have had a persistent impact on turnout. Respondents who faced less competitive environments when young are more likely to abstain in subsequent elections than those reaching voting age after close-fought races. We conclude that variations in competitiveness have had both short- and long-term effects on turnout.