This paper responds to Evans and Kat’s critique of the valence politics model of electoral choice. Their critique is deficient in several respects. First, the authors do not test the valence politics model, which is motivated by a theory of voting rather than a claim about the relationship between generalized measures of “party preference” and “party performance.” Second, Evans and Kat do not provide theoretical grounding for partisanship, which they claim is strongly exogenous to other variables of interest. Third, there are several specification and testing problems with their structural equation model. We study the properties of the valence model using a vector error correction model of aggregate monthly survey data gathered throughout the New Labour Era. Consistent with theoretical expectations, key valence politics variables constitute a powerful cointegrated system in which the dynamics of partisanship are endogenous to other variables in the system.

List experiments are a widely used survey technique for estimating the prevalence of socially sensitive attitudes or behaviors. Their design, however, makes them vulnerable to bias: because treatment group respondents see a greater number of items (J + 1) than control group respondents (J), the treatment group mean may be mechanically inflated due simply to the greater number of items. The few previous studies that directly examine this do not arrive at definitive conclusions. We find clear evidence of inflation in an original dataset, though only among respondents with low educational attainment. Furthermore, we use available data from previous studies and find similar heterogeneous patterns. The evidence of heterogeneous effects has implications for the interpretation of previous research using list experiments, especially in developing world contexts. We recommend a simple solution: using a necessarily false placebo statement for the control group equalizes list lengths, thereby protecting against mechanical inflation without imposing costs or altering interpretations.

A large class of spatial models of elections converges upon a single prediction: a candidate’s vote share increases in the congruence between her platform and the median voter’s preferences. Though considerable empirical research provides support for this prediction, these studies have not adequately identified the effects of platform positioning net of other factors. In this paper, we study the impact of challenger moderation on vote shares using data from 444 US House elections from 1996 to 2006 in which successive challengers competed against a common incumbent. Our findings are largely null. We uncover no evidence that challengers increase their vote shares by adopting more moderate platform positions. This finding is robust across a wide range of model specifications and subsets of districts.

This paper studies how citizen-initiated referenda affect the decision-making of elected representatives. In the absence of direct democracy, elected officials who do not share the preferences of voters may enact their preferred policies even at the cost of decreasing the likelihood of reelection. Direct democracy diminishes the policy benefits of doing that, as voters may now overturn some of the policy decisions. Hence, elected officials are induced to implement the policies preferred by the voters not only on those issues that are subject to a possible citizen-initiated referendum, but also on those that are not. This result holds even when the voters’ information about their true interests is limited. Moreover, whereas in a representative democracy, being more informed may undermine voters’ ability to control public officials, the possibility of citizen-initiated referenda means that additional information improves voter control, including on issues that may be outside the direct democracy domain.

Recent scholarship on affective polarization documents partisan animosity in people's everyday lives. But does partisan dislike go so far as to deny fundamental rights? We study this question through a moral dilemma that gained notoriety during the COVID-19 pandemic: triage decisions on the allocation of intensive medical care. Using a conjoint experiment in five countries we analyze the influence of patients’ partisanship next to commonly discussed factors determining access to intensive medical care. We find that while participants’ choices are consistent with a utilitarian heuristic, revealed partisanship influences decisions across most countries. Supporters of left or right political camps are more likely to withhold support from partisan opponents. Our findings offer comparative evidence on affective polarization in non-political contexts.

In this research note we replicate, update and expand innovative research by Sniderman et al. conducted in the Netherlands in the late 1990s, and ask whether the relative primacy of cultural compared with economic and safety threats in explaining ethnic prejudice remains true under markedly different national, economic and political contexts. Using two national British surveys conducted in 2011 and 2016, we examine the impact of threat on hostility toward three minority groups. Our results confirm the primacy of cultural threat as the strongest and most consistent predictor of hostility, while demonstrating the more context-specific effects of safety and economic concerns, with safety threats playing an overall more prominent role and increased economic concerns being related to less hostility post-Brexit.

I develop a theory of the emergence of minority and majority governments in multiparty parliamentary systems. I study a general bargaining environment with a policy space of arbitrary finite dimension, any number of political parties, and a general class of preferences over the government agreement space. I find that only majority governments form in the absence of significant political disagreement. However, I show that, except for knife-edge situations, minority government are formed with positive probability when parties represented in parliament are sufficiently ideologically polarized.

Measuring networks in the field—usually by asking individuals systematically about their networks–entails complex design choices, with large consequences for the resulting data. Because observations in a network are interconnected, well-established practices from non-network survey settings can lead researchers astray. Despite the increasing focus on networks in political science, little guidance is available for researchers facing high-stakes decisions when designing a study to elicit networks. This paper serves as a practical guide. It offers a simple framework for constructing a network theory, illuminates tradeoffs like measuring more nodes versus more ties per node or asking for names versus selections from a list, and proposes a new technique for cleaning relational data.

Why do opposing partisans sometimes disagree about the facts and processes that are relevant to understanding political issues? One explanation is that citizens may have a psychological tendency toward adopting beliefs about the political world that rationalize their partisan preferences. Previous quantitative evidence for rationalization playing a role in explaining partisan factual disagreement has come from cross-sectional covariation and from correction experiments. In this paper, I argue that these rationalizations can occur as side effects when citizens change their attitudes in response to partisan cues and substantively relevant facts about a political issue. Following this logic, I motivate and report the results of a survey experiment that provides US Republicans and Democrats with information that they will be inclined to rationalize in different ways, because they have different beliefs about which political actors they should agree with. The results are a novel experimental demonstration that partisan disagreements about the political world can arise from rationalization.

Improving geolocation accuracy in text data has long been a goal of automated text processing. We depart from the conventional method and introduce a two-stage supervised machine-learning algorithm that evaluates each location mention to be either correct or incorrect. We extract contextual information from texts, i.e., N-gram patterns for location words, mention frequency, and the context of sentences containing location words. We then estimate model parameters using a training data set and use this model to predict whether a location word in the test data set accurately represents the location of an event. We demonstrate these steps by constructing customized geolocation event data at the subnational level using news articles collected from around the world. The results show that the proposed algorithm outperforms existing geocoders even in a case added post hoc to test the generality of the developed algorithm.

How responsive is the US’ Trade Adjustment Assistance (TAA) to the labor dislocation that results from trade integration? Recent findings suggest that the world's most ambitious trade adjustment program barely responds to import shocks, and that the shortfall is made up by disability insurance and early retirement. This holds considerable implications: TAA offers a lens onto the central question of whether developed democracies can effectively redistribute the gains from international economic integration. We take a closer look at these results. Using petition-level data over a 20-year period, we find that TAA is between 1.7 and 3.3 times more responsive than current estimates suggest. Yet the news is not all good. As we show, the responsiveness of TAA has decreased considerably since the 1990s, just as developed democracies started facing increasing pushback against liberalization. This shortfall, in turn, has political consequences: areas where TAA has been least responsive were also more likely to shift toward voting for Trump in the 2016 Presidential election. Our findings speak to the considerable challenge governments face in aiding workers “left behind” by liberalization.

A method for predicting conflict zones in civil wars based on point process models is presented in this paper. Instead of testing the validity of specific theoretical conjectures about the determinants of violence in a causal framework, this paper builds on classic literature and a wide body of recent studies to predict conflict zones based on a series of geographic conditions. Using an innovative cross-validation design, the study shows that the quantitative research program on the micro-foundations of violence in civil conflict has crafted generalizable insights permitting out-of-sample predictions of conflict zones. The study region is delimited to ten countries in Sub-Saharan Africa that experienced full-blown insurgencies in the post-Cold War era.

Anti-black prejudice affects how some citizens evaluate black candidates. What does it take to reduce the role of prejudice in these evaluations? Using logical implications of relevant psychological phenomena, this article shows that repeated exposure to counter-stereotypical information is insufficient to reduce evaluative prejudice. Instead, citizens must associate this prejudice with adverse effects for themselves in contexts that induce them to rethink their existing racial beliefs. These findings explain important disagreements in empirical prejudice research, as only some empirical research designs supply the conditions for prejudice reduction predicted here. This study also clarifies why similarly situated citizens react so differently to counter-stereotypical information. In sum, we find that prejudice change is possible, but in a far narrower set of circumstances than many scholars claim.

I study the effects of direct democracy on economic policy in a novel setting. In Spain, national law determines that municipalities follow either direct or representative democracy, depending on their population size. Using a fixed-effect regression discontinuity design, I find that direct democracy leads to a smaller government, reducing public spending by around 8 percent. Revenues decrease by a similar amount and, therefore, there is no effect on budget deficits. These findings can be explained by a model in which direct democracy allows voters to enforce lower special-interest spending. I provide several additional results and discuss alternative mechanisms.

Previous studies identified several domestic factors that may influence a country’s level of structural coup-proofing, i.e., counterbalancing strategies that shall prevent internal groups from seizing power via a coup d’état. We suggest that a country’s level of counterbalancing is also affected by such policies in what we term countries’ “peer groups.” When deciding the appropriate level of counterbalancing, rulers may be affected by external information flows from a “peer group” with similar structural coup-risk characteristics (institutions) or a similar coup-risk experience (coup history). Using maximum likelihood spatial lag models and data in 1976–2005, we find that leaders learn from and emulate counterbalancing in other states, but rather only through an “experiential peer group.”

This study examines whether acts of anti-government violence exhibit spatial dependence across state boundaries. In other words, to what extent can acts of anti-government violence in one country be attributed to violence in neighboring countries? Past research, which has largely focused on civil war or large-scale conflict contagion, finds that geographically proximate states are more likely to experience the cross-boundary diffusion of conflict due to action emulation. However, this assumes that actors are fully aware of conflicts occurring in neighboring countries. To address this, the article argues that the proliferation of communication technology increases access to information about events in neighboring states, thereby allowing emulation to occur and subsequently conditioning the potential for violence to spread. It tests this expectation by modeling the effects of a unique spatial connectivity matrix that incorporates both state contiguity and access to communication technology. An analysis of all acts of anti-government violence in 44 African countries from 2000 to 2011 supports the argument.

The study of international relations (IR), and political science more broadly, has derived great benefits from the recent growth of conceptualizing and modeling political phenomena within their broader network contexts. More than just a novel approach to evaluating old puzzles, network analysis provides a whole new way of theoretical thinking. Challenging the traditional dyad-driven approach to the study of IR, networks highlight actor interdependence that goes beyond dyads and emphasizes that many traditional IR variables, such as conflict, trade, alliances, or international organization memberships must be treated and studied as networks. Properties of these networks (e.g., polarization, density), and of actor positions within them (e.g., similarity, centrality), will then reveal important insights about international events. Network analysis, however, is not yet fully adapted to account for important methodological issues common to IR research, specifically the issue of endogeneity or possible nonindependence between actors’ position within international networks and the outcomes of interest: for example, alliance network may be nonindependent from the conflict or trade network. We adopt an instrumental variable approach to explore and address the issue of endogeneity in network context. We illustrate the issue and the advantages of our approach with Monte Carlo analysis, as well as with several empirical examples from IR literature.

What is the effect of violence on political mobilization? Taking the repression-mobilization nexus debate as a starting point, we study the effects of police interventions on political participation, focusing on the Spanish police crackdown on Catalonia's independence referendum on 1 October 2017. We analyze the effect of police actions on turnout using detailed aggregate data, as well as a survey conducted a few days after the referendum. The two empirical approaches show that police interventions had both deterrent and inverse spatial spillover effects. Although police raids had a local negative impact on turnout, they induced positive spillover effects in the surrounding areas. Our findings also indicate heterogeneity in the spatial dynamics, with police actions encouraging people to go to vote in nearby areas, but also mobilizing residents in neighboring areas to participate, especially those individuals with fewer incentives to turn out to vote.

Researchers in political science often estimate linear models of continuous outcomes using least squares. While it is well known that least-squares estimates are sensitive to single, unusual data points, this knowledge has not led to careful practices when using least-squares estimators. Using statistical theory and Monte Carlo simulations, we highlight the importance of using more robust estimators along with variable transformations. We also discuss several approaches to detect, summarize, and communicate the influence of particular data points.

There is abundant empirical evidence that the plurality rule constrains party competition and favors two-party systems. This reduction of party system fragmentation may be due to parties deciding not to enter elections for which they are not viable and/or voters voting strategically. Yet, no prior research has attempted to estimate the respective role of parties and voters in this process. To fill this gap, we conducted a unique laboratory experiment where some subjects played the role of parties and others played the role of voters, and where the two were able to respond to each other just as in real-life elections. We find that the reduction due to party strategic exit is higher than that due to strategic voting. We conclude that parties play a key role in the effect of the plurality rule on party system fragmentation.