Electoral systems affect vote choice. While a vast literature studies this relationship by examining aggregate-level patterns and focussing on the interparty dimension of electoral rules, the convenience of analyzing this phenomenon by emphasizing the role played by the incentives to cultivate a personal vote generated by the system and matching voters with the party they vote for has been traditionally overlooked. In this article, we offer new evidence that documents the impact of the intraparty dimension of electoral systems on the levels of ideological voting registered in a democracy. Using spatial models of politics and employing data from the five waves of the Comparative Study of Electoral Systems, we find that ideological voting in proportional representation systems is higher when lists are either closed or flexible. Moreover, the results suggest that this effect is slightly amplified in the case of high numbers of district-level candidates.

Immigration has become a focal debate in politics across the world. Recent research suggests that anti-immigration attitudes may have deep psychological roots in implicit disease avoidance motivations. A key implication of this theory is that individual differences in disease avoidance should be related to opposition to immigration across a wide variety of cultural and political contexts. However, existing evidence on the topic has come almost entirely from the United States and Canada. In this article, we test the disease avoidance hypothesis using nationally representative samples from Norway, Sweden, Turkey, and Mexico, as well as two diverse samples from the United States. We find consistent and robust evidence that disgust sensitivity is associated with anti-immigration attitudes and that the relationship is similar in magnitude to education. Overall, our findings support the disease avoidance hypothesis and provide new insights into the nature of anti-immigration attitudes.

The literature on comparative partisanship has demonstrated the low rates of party identification in Latin America. Such low rates are commonly interpreted as a sign of citizens’ disengagement with parties and democracy in the region. This article revisits this interpretation by considering voters’ adverse affection toward a party, or negative partisanship. It shows that examining the negative side of partisanship can help us develop a clearer perspective on the partisan linkages in the electorate. To support this claim, this study analyzes an original conjoint experiment in Argentina and Mexico, as well as two other public opinion surveys fielded in Brazil, Chile, and Ecuador. The study presents empirical evidence indicating that negative partisanship helps voters without an attachment to a party to distinguish themselves from nonpartisans, is independent of positive partisanship, and is different from a general distrust of the democratic system.

The comparative literature on democratization has shown that election trust depends as much on subjective factors as on the objective conditions of the process. This literature, however, has thus far overlooked the consequences of candidates refusing to concede an electoral defeat. This letter argues that a disputed electoral outcome further inflames negative perceptions of electoral integrity among voters who supported a losing candidate. We bring support for this claim from a multilevel regression that includes data from the AmericasBarometer surveys on almost 100,000 respondents across 49 elections in 18 Latin American countries. We combine these responses with an original database of disputed elections in the region. The empirical findings demonstrate the eroding effect of challenged election outcomes on voters' election trust, particularly among those who voted for a losing candidate. The findings underscore an intuitive yet untested pattern: candidates' refusal to accept the electoral outcome is a strong signal among their supporters, increasing their distrust on the integrity of the process.

We provide an introduction of the functioning, implementation, and challenges of convolutional neural networks (CNNs) to classify visual information in social sciences. This tool can help scholars to make more efficient the tedious task of classifying images and extracting information from them. We illustrate the implementation and impact of this methodology by coding handwritten information from vote tallies. Our paper not only demonstrates the contributions of CNNs to both scholars and policy practitioners, but also presents the practical challenges and limitations of the method, providing advice on how to deal with these issues.

Are women disproportionately more likely than men to have family ties in politics? We study this question in Latin America, where legacies have been historically common, and we focus specifically on legislatures, where women's representation has increased dramatically in many countries. We hypothesize that, counter to conventional wisdom, women should be no more likely than men to have ties to political families. However, this may vary across legislatures with and without gender quotas. Our empirical analysis uses data from the Parliamentary Elites of Latin America survey. We find more gender similarities than differences in legislators’ patterns of family ties both today and over the past 20 years. We also find that women are more likely to have family ties than men in legislatures without gender quotas, whereas this difference disappears in legislatures with quotas.

This paper investigates the opportunities for non-democratic regimes to rely on fraud by documenting the alteration of vote tallies during the 1988 presidential election in Mexico. In particular, I study how the alteration of vote returns came after an electoral reform that centralized the vote-counting process. Using an original image database of the vote-tally sheets for that election and applying Convolutional Neural Networks (CNN) to analyze the sheets, I find evidence of blatant alterations in about a third of the tallies in the country. This empirical analysis shows that altered tallies were more prevalent in polling stations where the opposition was not present and in states controlled by governors with grassroots experience of managing the electoral operation. This research has implications for understanding the ways in which autocrats control elections as well as for introducing a new methodology to audit the integrity of vote tallies.

Good education requires student experiences that deliver lessons about practice as well as theory and that encourage students to work for the public good—especially in the operation of democratic institutions (Dewey 1923; Dewy 1938). We report on an evaluation of the pedagogical value of a research project involving 23 colleges and universities across the country. Faculty trained and supervised students who observed polling places in the 2016 General Election. Our findings indicate that this was a valuable learning experience in both the short and long terms. Students found their experiences to be valuable and reported learning generally and specifically related to course material. Postelection, they also felt more knowledgeable about election science topics, voting behavior, and research methods. Students reported interest in participating in similar research in the future, would recommend other students to do so, and expressed interest in more learning and research about the topics central to their experience. Our results suggest that participants appreciated the importance of elections and their study. Collectively, the participating students are engaged and efficacious—essential qualities of citizens in a democracy.

In this paper, we introduce an innovative method to diagnose electoral fraud using vote counts. Specifically, we use synthetic data to develop and train a fraud detection prototype. We employ a naive Bayes classifier as our learning algorithm and rely on digital analysis to identify the features that are most informative about class distinctions. To evaluate the detection capability of the classifier, we use authentic data drawn from a novel data set of district-level vote counts in the province of Buenos Aires (Argentina) between 1931 and 1941, a period with a checkered history of fraud. Our results corroborate the validity of our approach: The elections considered to be irregular (legitimate) by most historical accounts are unambiguously classified as fraudulent (clean) by the learner. More generally, our findings demonstrate the feasibility of generating and using synthetic data for training and testing an electoral fraud detection system.