Invalid ballots are relatively common in countries with compulsory voting, yet there is no cross-national evidence as to who is more likely to cast a blank or spoiled ballot where voting is forced. I argue that increased rates of blank and spoiled balloting where voting is obligatory result from the behavior of the politically unknowledgeable, uninterested, untrusting, and disaffected, who are incentivized to turn out to the polls where they can be sanctioned for abstention. To test this, I conduct an individual-level examination of the influence of compulsory voting on invalid balloting across countries. I find support for my expectations with analyses of survey data from several American democracies, many of which compel electoral participation.

This paper examines the Trio Presidency and its impact on voting behavior in the Council of the European Union. Trios of Member States cooperate for 18-month periods, with each member holding the Presidency for six months. We study whether belonging to the Trio increases the probability that a Member State votes in favor of a measure. We analyze roll call votes on 1038 legislative acts from January 2007 till June 2014. Conducting probit analyses we present evidence that Member States are indeed more likely to vote in favor during their participation in Trios. We show that this results mainly from the Trio’s agenda-setting powers. Moreover, holding the Presidency itself does not further increase the probability of voting in favor.

In close elections, a sufficiently high share of invalid ballots—if driven by voter mistakes or electoral fraud—can jeopardize the electoral outcome. We study how the closeness of electoral race relates to the share of invalid ballots, under the traditional paper-ballot hand-counted voting technology. Using a large micro-level data set from the Italian parliamentary elections in 1994–2001, we find a strong robust negative relationship between the margin of victory of the leading candidate over the nearest rival and the share of invalid ballots. We argue that this relationship is not driven by voter mistakes, protest, or electoral fraud. The explanation that garners most support is that of rational allocation of effort by election officers and party representatives, with higher rates of detection of invalid ballots in close elections.

Position change is an essential feature of political competition. Implicitly, policy change on an issue dimension is often equated with opinion change on specific issues within that dimension. However, in addition to opinion-based policy change, we highlight that parties and candidates can change their overall position by increasing their emphasis on certain opinions within that issue dimension (emphasis-based policy change). Using party manifesto data, we find that parties differ in their use of each type of policy change based on aspects of party organization, particularly the relative power of leaders and activists. Leader-dominated parties are more likely to engage in opinion-based policy change, also in reaction to systemic policy shifts. In contrast, activist-dominated parties tend to change their overall position in reaction to systemic shifts by emphasizing certain positions more. Our approach links salience-based to spatial models of party competition and has broader implications for how we study party competition.

We investigate the effect of personality on prosocial behavior in a Bayesian multilevel meta-analysis (MLMA) of 15 published, interdisciplinary experimental studies. With data from the 15 studies constituting nearly 2500 individual observations, we find that the Big Five traits of Agreeableness and Openness are significantly and positively associated with prosocial behavior, while none of the other three traits are. These results are robust to a number of different model specifications and operationalizations of prosociality, and they greatly clarify the contradictory findings in the literature on the relationship between personality and prosocial behavior. Though previous research has indicated that incentivized experiments result in reduced prosocial behavior, we find no evidence that monetary incentivization of participants affects prosocial tendencies. By leveraging individual observations from multiple studies and explicitly modeling the multilevel structure of the data, MLMA permits the simultaneous estimation of study- and individual-level effects. The Bayesian approach allows us to estimate study-level effects in an unbiased and efficient manner, even with a relatively small number of studies. We conclude by discussing the limitations of our study and the advantages and disadvantages of the MLMA method.

A growing literature in political science has pointed to the importance of heuristics in explaining citizens’ political attitudes, beliefs, and behaviors. At the same time, the multidisciplinary research on heuristics in general has revealed that individuals seem to use heuristics sensibly—applying them (perhaps subconsciously) when they are likely to be helpful but not otherwise. We extend this multidisciplinary work to political behavior and present a general theory of contextual variation in political heuristic use applied to discover under what conditions (i.e., what political contexts) voters will use a partisanship heuristic to infer the legislative votes of their legislators in imperfectly disciplined voting contexts. More specifically, we predict that US constituents of loyal partisan senators will use the partisanship heuristic more often than constituents of less loyal senators. Our empirical analysis reveals strong support for our theory, contributing to our understanding of political heuristics in general and adding nuance to our understanding of the partisanship heuristic in particular.

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

Using new data on roll-call voting of US state legislators and public opinion in their districts, we explain how ideological polarization of voters within districts can lead to legislative polarization. In so-called “moderate” districts that switch hands between parties, legislative behavior is shaped by the fact that voters are often quite heterogeneous: the ideological distance between Democrats and Republicans within these districts is often greater than the distance between liberal cities and conservative rural areas. We root this intuition in a formal model that associates intradistrict ideological heterogeneity with uncertainty about the ideological location of the median voter. We then demonstrate that among districts with similar median voter ideologies, the difference in legislative behavior between Democratic and Republican state legislators is greater in more ideologically heterogeneous districts. Our findings suggest that accounting for the subtleties of political geography can help explain the coexistence of polarized legislators and a mass public that appears to contain many moderates.

Do economic perceptions affect voters’ electoral choices? There is ample evidence showing a correlation between how people perceive the current state of the economy and electoral decisions. However, there are reasons to believe that political preferences can also determine how voters evaluate economic conditions, which will reverse the causality arrow. The strategies previously implemented to address this problem have been based on the use of structural equations and instrumental variables, but they require very strong parametric or identification assumptions. In this paper, I follow a design-based approach by emphasizing the study design rather than statistical modeling. In contrast to previous studies that used the same panel data in Brazil, I find evidence that supports egotropic, rather than sociotropic, voting. This finding shows that traditional research designs may be overstating the magnitude of sociotropic economic voting.

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Technology and the decreased cost of survey research have made it possible for researchers to collect data using new and varied modes of interview. These data are often analyzed as if they were generated using similar processes, but the modes of interview may produce differences in response simply due to the presence or absence of an interviewer. In this paper, we explore the differences in item non-response that result from different modes of interview and find that mode makes a difference. The data are from an experiment in which we randomly assigned an adult population to an in-person or self-completed survey after subjects agreed to participate in a short poll. For nearly every topic and format of question, we find less item non-response in the self-complete mode. Furthermore, we find the difference across modes in non-response is exacerbated for respondents with low levels of cognitive abilities. Moving from high to low levels of cognitive ability, an otherwise average respondent can be up to six times more likely to say “don’t know” in a face-to-face interview than in a self-completed survey, depending on the type of question.

This manuscript presents a new measure of safety net program effectiveness—a “poverty relief ratio”—that is based on the estimated relationship between market income and social transfers, and reports the amount of income support provided, relative to the amount required to provide for all low-income households’ basic needs. In an important advance over the standard poverty reduction rate measures, the poverty relief ratio preserves the rank order of observations across varying poverty thresholds. In this paper, we introduce this measure and demonstrate its validity by tracking major changes in federal policy and cross-state variation in safety net programs.

We apply recent advances in machine learning to measure Congressmember personality traits using floor speeches from 1996 to 2014. We also demonstrate the superiority of text-based measurement over survey-based measurement by showing that personality traits are correlated with survey response rates for members of Congress. Finally, we provide one empirical application showcasing the importance of personality on congressional behavior.

This study uses a discordant sibling-based design to assess the extent to which education is proxying for pre-adult experiences and predispositions rooted in the family. It draws on a unique data set that combines official voting records with Census data on siblings and their parents. The results show that the association between education and voting is considerably reduced when parental education, parental voting and unobserved characteristics that are shared by siblings within the same family are taken into account. This finding is confirmed by a variety of robustness checks. We end with a discussion of the benefits and limitations of sibling-based designs for testing causal hypotheses.

As non-democratic regimes have adapted to the proliferation of social media, they have began actively engaging with Twitter to enhance regime resilience. Using data taken from the Twitter accounts of Venezuelan legislators during the 2014 anti-Maduro protests in Venezuela, we fit a topic model on the text of the tweets and analyze patterns in hashtag use by the two coalitions. We argue that the regime’s best strategy in the face of an existential threat like the narrative developed by La Salida and promoted on Twitter was to advance many competing narratives that addressed issues unrelated to the opposition’s criticism. Our results show that the two coalitions pursued different rhetorical strategies in keeping with our predictions about managing the conflict advanced by the protesters. This article extends the literature on social media use during protests by focusing on active engagement with social media on the part of the regime. This approach corroborates and expands on recent research on inferring regime strategies from propaganda and censorship.

Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

How does media influence beliefs, attitudes, and behaviors? While many scholars have studied the effect of media on social and political outcomes, we know surprisingly little about the channels through which this effect operates. I argue that two mechanisms can account for its impact. Media provides new information that persuades individuals to accept it (individual channel), but also, media informs listeners about what others learn, thus facilitating coordination (social channel). Combining a field experiment with a plausibly natural experiment in Mexico, I disentangle these effects analyzing norms surrounding violence against women. I examine the effect of a radio program when it is transmitted privately versus when it is transmitted publicly. I find no evidence supporting the individual mechanism. The social channel, however, increased rejection of violence against women and increased support for gender equality, but unexpectedly increased pessimism about whether violence would decline in the future.

Recent studies of attitudes toward economic inequality suggest that most people around the world prefer very low levels of inequality, despite well-known trends toward greater inequality within many countries. Even within countries, people across the political spectrum are said to be in remarkable agreement about the ideal level of economic inequality. Using survey data from 40 countries and a novel survey experiment in the United States, we show that this apparent agreement is illusory. When relying on a widely used cross-national survey measure of Ideal Pay Ratios, preferred levels of inequality are heavily influenced by two well-documented sources of perceptual distortion: the anchoring effect and ratio bias. These effects are substantial and many times larger than the influence of fundamental political predispositions. As a result, these cross-national survey measures tapping preferences regarding economic inequality produce misleading conclusions about desired levels of inequality.

Many commonly used data sources in the social sciences suffer from non-random measurement error, understood as mis-measurement of a variable that is systematically related to another variable. We argue that studies relying on potentially suspect data should take the threat this poses to inference seriously and address it routinely in a principled manner. In this article, we aid researchers in this task by introducing a sensitivity analysis approach to non-random measurement error. The method can be used for any type of data or statistical model, is simple to execute, and straightforward to communicate. This makes it possible for researchers to routinely report the robustness of their inference to the presence of non-random measurement error. We demonstrate the sensitivity analysis approach by applying it to two recent studies.

We use two original surveys (including survey experiments) conducted the week before the 2015 elections in the Britain and Denmark to explore the best method for measuring individuals’ knowledge of the partisan distribution of legislative seats in multi-party democracies. The complete lack of such questions in the corpus of survey research on multi-party democracies is a testament to the skepticism that many survey researchers have about the feasibility of such complex questions. However, our analysis, which is the first empirical test of this skepticism, reveals little evidence of respondents’ frustration or high levels of non-cooperation with these questions. Additionally, our survey experiments, which examine the usefulness of different question formats, make it clear that such questions should be framed in terms of the numbers of seats each party holds rather than shares or percentages of seats.