Concerns over free riding in NATO are widespread. An intuitive approach to analyzing free riding is treating it as a systematic pattern of spatial interdependence between the allies: how does a NATO member's defense spending react to changes in other allies' military expenditures? While recent work has found statistically significant free riding (negative spatial interdependence in the outcomes), it suffers from important limitations. First, this research does not adequately account for temporal dependence. Second, it does not quantify the effect of interest. Accounting directly for temporal dependence provides a meaningfully distinct perspective on the within-alliance dynamics, demonstrating that the spatiotemporal effect of free riding is, in fact, more substantial than its short-run effect, challenging inferences of static spatial models. We discuss the relevant practical and theoretical implications.

Latin America is the most violent region in the world, with many countries also suffering from high levels of criminality and the presence of powerful criminal organizations. Identifying government responses that improve citizen security is imperative. Existing research argues that improving intergovernmental coordination helps the state combat criminality, but has limited its analysis to political factors that affect coordination. I study the impact of increasing intergovernmental coordination between law enforcement agencies. Using the generalized synthetic control method, original data on the staggered implementation of a police reform that increased intergovernmental police coordination and detailed data on criminal organizations and criminality in Guanajuato, Mexico, I find that the reform weakened criminal organizations and reduced violent crime, but increased violence.

A popular explanation for America's democratic ills is that Republicans and Democrats misperceive one another to hold extreme attitudes. However, Americans may also misperceive the diversity of partisans’ attitudes to ill effect. This paper uses surveys and pre-registered experiments with representative and convenience samples (n = 9405) to validate a measure of perceived attitude extremity and diversity and compare it to canonical measures of perceived polarization. We find that American partisans vastly under-estimate the diversity of each party's attitudes. Yet, contrary to existing research, we see little evidence that partisans over-estimate how extreme the “average” Republican or Democrat is. Finally, perceptions of both the “average” partisan and within-party attitude diversity predict partisan animosity and perceptions of out-party threat.

In multiethnic societies, domestic minority groups are frequently associated with external adversaries. In such situations, external confrontations may potentially exacerbate internal ethnic conflict by increasing intolerance toward the domestic minority. Alternatively, they may result in rallying the public around a common superordinate national identity. We examine the case of India, which has a long-running rivalry with Muslim-majority Pakistan. Through a large (n = 7052) survey experiment, we find little evidence that a hypothetical crisis with Pakistan worsens attitudes toward India's large Muslim minority. Instead, we find that such a crisis may improve intergroup cohesion within India, improving tolerance towards Muslim minorities.

Stance detection is identifying expressed beliefs in a document. While researchers widely use sentiment analysis for this, recent research demonstrates that sentiment and stance are distinct. This paper advances text analysis methods by precisely defining stance detection and outlining three approaches: supervised classification, natural language inference, and in-context learning. I discuss how document context and trade-offs between resources and workload should inform your methods. For all three approaches I provide guidance on application and validation techniques, as well as coding tutorials for implementation. Finally, I demonstrate how newer classification approaches can replicate supervised classifiers.

Many believe that weaker legal constraints make unilateral action easier to reverse than legislation. Yet in some cases, unilateralism survives a successor's determined opposition. I argue that legislative persistence and unilateral transience may arise not only from differences in legal status but also from a selection effect: unilateralism and subsequent rescission can both stem from weak underlying policy-demanding groups. But if unilateralism itself alters the landscape of group power through policy feedback effects, it can survive a purportedly opposed new president. An example is the Trump Administration's failure to depart from the Obama Justice Department's lax stance on cannabis. Weak constituents’ policy demands may thus need to rely on a sympathetic president's unilateral action to begin a feedback loop, subsequently softening opposition. This paper therefore identifies unilateral action as a key tool for shifting policy in an enduring way, explaining its success or failure in terms of interest group power.

Presidential elections are arguably the most consequential recurring political event in the United States. Understanding the factors that determine their outcomes, therefore, is of substantial importance. One proposed factor pertains to presidential candidates' incumbency status, yet its nature is complex and difficult to study with observational data. In particular, the individual-level mechanisms underlying incumbency effects remain unclear. This study proposes many citizens generally believe that, ceteris paribus, presidents should be afforded two terms. Crucially, such a norm implies that incumbency status possesses an inherent effect, operating independent of other mechanisms that may stem from incumbency. A large, pre-registered survey experiment was therefore employed to isolate the effect of incumbency status on presidential vote choice. The experiment finds strong evidence that one-term incumbent candidates have an inherent advantage against their non-incumbent opponents. The results also clarify that the two-term disadvantage present in observational data is not inherent, and is perhaps better understood simply as the absence of the one-term advantage. The study thus points toward a micro-foundational mechanism underlying incumbent-party performance in presidential elections. Finally, analyses of panel data explore which voters may be systematically inclined to vote based upon the incumbency status of presidential candidates.

This study contributes to understanding mainstream parties' room for maneuver in the immigration domain by exploring the impact of moralization as a rhetorical strategy. Research has found anti-immigrant messages to be more effective than pro-immigrant messages in shaping public opinion. However, a pre-registered survey experiment with Danish respondents reveals that moral rhetoric enhances policy support for pro-immigrant communication, rendering it as effective as anti-immigrant messages. Partisan-motivated reasoning influences reactions to all messages except the non-moral anti-immigrant message, suggesting that when appealing to out-party supporters, mainstream parties still meet least resistance with an exclusive platform.

In recent years, anti-refugee hate crimes have soared across Europe. We know this violence has spread fear among refugees, but we know less about its effects on the non-refugee population. This is an oversight, as research suggests political violence often has effects on the broader population. Those effects can range from increased solidarity with the targets of the violence to reduced pro-social behavior and less support for the targets of the violence. In this research note, we examine the effects of exposure to anti-refugee hate crimes in Germany. Our results suggest no direct effect of exposure to anti-refugee hate crimes on support for refugees. These results have several implications for our understanding of political divides over refugees in Europe.

Increasing partisan polarization has characterized American politics for decades. On January 6, 2021, both Republicans and Democrats in Congress expressed their horror at the violent invasion of the US Capitol, leading to the popular perception—emphasized by media accounts—that the attack generated a rare moment of bipartisan unity. We argue that while members of both parties condemned the attack, a marked partisan divide characterized their messaging even as events unfolded. We analyze all 1861 tweets by members of Congress on January 6th and find that Republicans were significantly more likely to characterize the invasion as a protest grown out of hand, while Democrats described it as an attack on democracy. The results strongly indicate that partisan polarization was alive and well on January 6th and may help to account for Republicans’ shift toward normatively positive portrayals of the day in subsequent months.

Although influential models of public opinion hold that group sentiments play an important role in shaping political beliefs, they often assume that group attitudes stem from socialization and are thus exogenous to politics. We challenge this assumption, arguing that group attitudes may themselves be the consequence of political views. Across three survey experiments that each uses a unique social group–issue pair, we consistently demonstrate that attitudes toward groups are influenced by information about the groups' policy views. These findings persist even when accounting for potential partisan signaling. Altogether, these results show that group sentiments should not be regarded as wholly exogenous to policy concerns and suggest that the use of group-based heuristics can be consistent with instrumental models of public opinion.

Among the greatest challenges facing scholars of public opinion are the potential biases associated with survey item nonresponse and preference falsification. This difficulty has led researchers to utilize nonresponse rates to gauge the degree of preference falsification across regimes. This article addresses the use of survey nonresponse rates to proxy for preference falsification. A simulation analysis exploring the expression of preferences under varying degrees of repression was conducted to examine the viability of using nonresponse rates to regime assessment questions. The simulation demonstrates that nonresponse rates to regime assessment questions and indices based on nonresponse rates are not viable proxies for preference falsification. An empirical examination of survey data supports the results of the simulation analysis.

Whether a topic is seen in a moral or moral-emotional light has significant political implications. Yet, we lack knowledge about the process of moralization: Who defines the way topics are communicated about? Where prior research has investigated the relative power of different actors to place a topic on the agenda or shape opinions, we study who sets the moral and moral-emotional tone of debate. To do so, we zoom in on immigration discourse in Germany and analyze fine-grained social media data from politicians, political parties, newspapers, and members of the public over a period of more than four years. After employing a transformer model to identify moral and moral-emotional appeals, we use structural vector autoregression models to demonstrate the important role of radical-right challengers in shaping public discourse in a negative moral-emotional direction. The results inform theories of moralization and political entrepreneurship.

What are the consequences of including a “don't know” (DK) response option to attitudinal survey questions? Existing research, based on traditional survey modes, argues that it reduces the effective sample size without improving the quality of responses. We contend that it can have important effects not only on estimates of aggregate public opinion, but also on estimates of opinion differences between subgroups of the population who have different levels of political information. Through a pre-registered online survey experiment conducted in the United States, we find that the DK response option has consequences for opinion estimates in the present day, where most organizations rely on online panels, but mainly for respondents with low levels of political information and on low salience issues. These findings imply that the exclusion of a DK option can matter, with implications for assessments of preference differences and our understanding of their impacts on politics and policy.

Several disciplines, such as economics, law, and political science, emphasize the importance of legislative quality, namely well-written legislation. Low-quality legislation cannot be easily implemented because the texts create interpretation problems. To measure the quality of legal texts, we use information from the syntactic and lexical features of their language and apply these measures to a dataset of European Union legislation that contains detailed information on its transposition and decision-making process. We find that syntactic complexity and vagueness are negatively related to member states’ compliance with legislation. The finding on vagueness is robust to controlling for member states’ preferences, administrative resources, length of texts, and discretion. However, the results for syntactic complexity are less robust.

Inverse probability weighting is a common remedy for missing data issues, notably in causal inference. Despite its prevalence, practical application is prone to bias from propensity score model misspecification. Recently proposed methods try to rectify this by balancing some moments of covariates between the target and weighted groups. Yet, bias persists without knowledge of the true outcome model. Drawing inspiration from the quasi maximum likelihood estimation with misspecified statistical models, I propose an estimation method minimizing a distance between true and estimated weights with possibly misspecified models. This novel approach mitigates bias and controls mean squared error by minimizing their upper bounds. As an empirical application, it gives new insights into the study of foreign occupation and insurgency in France.

How do international crises unfold? We conceptualize international relations as a strategic chess game between adversaries and develop a systematic way to measure pieces, moves, and gambits accurately and consistently over a hundred years of history. We introduce a new ontology and dataset of international events called ICBe based on a very high-quality corpus of narratives from the International Crisis Behavior (ICB) Project. We demonstrate that ICBe has higher coverage, recall, and precision than existing state of the art datasets and conduct two detailed case studies of the Cuban Missile Crisis (1962) and the Crimea-Donbas Crisis (2014). We further introduce two new event visualizations (event iconography and crisis maps), an automated benchmark for measuring event recall using natural language processing (synthetic narratives), and an ontology reconstruction task for objectively measuring event precision. We make the data, supplementary appendix, replication material, and visualizations of every historical episode available at a companion website crisisevents.org.

In this paper we test how much party labels influence vote choices between candidates when voters have access to realistically distributed information about candidate positions and demographics. We do not seek to adjudicate a long-running debate about the role of party labels, but present some nuances on the two archetypal theoretical views on vote choices. We use data from the Representative Audit of Britain (RAB) and the British Elections Study (BES) to generate electoral match-ups between randomly selected Conservative versus Labour candidates, with only half of respondents seeing party labels in addition to candidates’ positions and demographics. For our experiment fielded in October 2021, we find negligible to moderate effects of party labels on vote choices. Our results suggest the information on candidate positions and party labels largely act as substitutes for one another, with only modest changes when party labels are made explicit.

Civic associations are expected to foster civic, pro-social behavior, but this optimistic view is increasingly contested. We argue that populist radical right parties can strategically target and infiltrate associations to diffuse anti-establishment rhetoric and anti-democratic attitudes. We illustrate this phenomenon by examining the relationship between civic associations and compliance with government rules during Germany's first Covid-19 lockdown with a difference-in-differences design. Results show that areas with denser sport, nature, and culture clubs recorded higher mobility under lockdown. We document the infiltration mechanism and the spreading of anti-democratic attitudes within associations, using survey and election data and qualitative evidence including interviews. In doing so, we shed light on a negative effect of social networks and an understudied strategy of challenger populist parties.