A central challenge in understanding public opinion shifts is identifying whose opinions change. Political economists try to uncover this by exploring voters’ economic vulnerability, particularly the relationship between labor-market risk and redistribution preferences. Predominantly, however, such work imputes risk from occupational or sectoral characteristics. Due to within-occupational inequality in exposure to risk, considerable variation remains unexplored. I propose an individual-level, dynamic account of risk inferred from job tenure, contract type, and expectations of job security. These aspects, importantly, account for individual variation in risk and the possibility that one's experience of risk may change across time. The results indicate the usefulness of this approach to risk in understanding changes in social spending preferences.

Many, especially low-skilled workers, blame globalization for their economic woes. Robots and machines, which have led to job market polarization, rising income inequality, and labor displacement, are often viewed much more forgivingly. This paper argues that citizens have a tendency to misattribute blame for economic dislocations toward immigrants and workers abroad, while discounting the effects of technology. Using the 2016 American National Elections Studies, a nationally representative survey, I show that workers facing higher risks of automation are more likely to oppose free trade agreements and favor immigration restrictions, even controlling for standard explanations for these attitudes. Although pocket-book concerns do influence attitudes toward globalization, this study calls into question the standard assumption that individuals understand and can correctly identify the sources of their economic anxieties. Accelerated automation may have intensified attempts to resist globalization.

Can data on government coercion and violence be trusted when the data are generated by state itself? In this paper, we investigate the extent to which data from the California Department of Corrections and Rehabilitation (CDCR) regarding the use of force by corrections officers against prison inmates between 2008 and 2017 conform to Benford's Law. Following a growing data forensics literature, we expect misreporting of the use-of-force in California state prisons to cause the observed data to deviate from Benford's distribution. Statistical hypothesis tests and further investigation of CDCR data—which show both temporal and cross-sectional variance in conformity with Benford's Law—are consistent with misreporting of the use-of-force by the CDCR. Our results suggest that data on government coercion generated by the state should be inspected carefully before being used to test hypotheses or make policy.

Twitter is a prominent communication tool for politicians with two potential uses: as a “substitute” channel to circumvent constraints from other political arenas, or as an “amplifier” that reinforces party messages. Using a novel dataset containing tweets and parliamentary speeches by members of parliament (MPs) in seven countries, we estimate politicians’ positions and intra-party dissent on European integration. We find that MPs’ sentiment about Europe on Twitter is a valid measure of their party's position, while also uncovering intra-party disagreements. Our results suggest that most MPs amplify the partisan message, but MPs who participate less in parliamentary debate tend to have larger differences with their party on Twitter. Social media thus can free politicians from their party's grip.

Security forces are one of the main targets of political violence. This paper examines the effect of their casualties on electoral outcomes. Between two general elections in 2015, Turkey experienced a series of attacks that killed 153 members of its security forces. Based on the as-if random assignment of their funerals across the country, I estimate that government vote share increases in the funeral places of security force terror victims. However, in the localities with recurring funerals, it decreases by a similar percentage. These non-linear changes provide strong evidence for the rally theory.

A robust economy is assumed to bolster leaders' standing. This ignores how benefits of growth are distributed. Extending the partisan models of economic voting, we theorize executives are more likely rewarded when gains from growth go to their constituents. Analyses of presidential approval in 18 Latin American countries support our pro-constituency model of accountability. When economic inequality is high, growth concentrates among the rich, and approval of right-of-center presidents is higher. Leftist presidents benefit from growth when gains are more equally distributed. Further analyses show growth and inequality inform perceptions of personal finances differently based on wealth, providing a micro-mechanism behind the aggregate findings. Study results imply that the economy is not purely a valence issue, but also a position issue.

Do agencies implement the president's particularistic goals uniformly? This paper clarifies the presidential particularism literature by explicitly considering the mechanism through which the president pursues their policy goals: executive agencies. The constellation of bureaucratic agencies responsible for allocating grants plays a key role in facilitating or frustrating presidential policy priorities. Using a dataset of 21 agencies over 14 years, I find that only agencies ideologically proximate to the president engage in particularism benefiting the president. I find no evidence that politicization influences agency implementation of particularism. Critically, the moderating effect of the bureaucracy on particularism only occurs for distributive programs over which agencies have discretion. When disbursing formula grants written by Congress but administered by the bureaucracy with little or no discretion, ideological distance between agencies and presidents has no effect on particularism.

A flurry of current interest in time series has focused on clarifying equation balance, fractional integration, and cointegration testing. Despite this, a number of recent suggestions may continue to lead scholars toward incorrect inferences. In this comment, I investigate the likelihood of drawing both correct and incorrect inferences under a variety of stationary and non-stationary data-generating processes. I extend previous work in this area by focusing on both short- and long-run effects using several popular model specifications. Given these findings, I conclude by offering a variety of recommendations to practitioners about how they can best specify their model.

The literature on autocracies has argued that repression of protest is either a result of the political environment in which protest occurs, or depends on particular characteristics of the protest events themselves. We argue that the interaction of both matters. Authoritarian regimes vary in how they legitimize their rule, and they should be particularly thin-skinned if protesters challenge the basis of their legitimacy. Using event-level data on mass mobilization in autocracies between 2003 and 2015, we use text classification methods to extract protest issues from newspaper reports. Our analysis shows that dictators are more likely to repress protest against incumbents when they claim legitimacy based on the person of the leader. Overall, our study shows that protest issues are not universal in triggering repression; rather, they need to be considered together with the political context in which they are raised.

Individuals' attitudes about gender roles have been shown to be associated with a wide range of political outcomes. It is therefore crucial to better understand what shapes these attitudes. This note takes advantage of a randomized survey experiment embedded in the 2018 wave of the European Social Survey (ESS) to investigate how differences in education levels between partners influence the “gender childcare bias”—the extent to which individuals disapprove more of women working full time with children under three than men. Although male and female respondents exhibit an equally strong gender childcare bias on average, we find clear-cut evidence that the bias varies asymmetrically across the household education gap for women and men. In particular, positive household education gaps lead to a smaller gender childcare bias for female respondents, whereas the opposite holds for male respondents. Our findings are more in line with a resource-bargaining approach than a gender identity approach to the formation of gender role attitudes.

In the absence of a complete voting record, decision records are an important data source to analyze committee decision-making in various institutions. Despite the ubiquity of decision records, we know surprisingly little about how to analyze them. This paper highlights the costs in terms of bias, inefficiency, or inestimable effects when using decision instead of voting records and introduces a Bayesian structural model for the analysis of decision-record data. I construct an exact likelihood function that can be tailored to many institutional contexts, discuss identification, and present a Gibbs sampler on the data-augmented posterior density. I illustrate the application of the model using data from US state supreme court abortion decisions and UN Security Council deployment decisions.

In small samples, maximum likelihood (ML) estimates of logit model coefficients have substantial bias away from zero. As a solution, we remind political scientists of Firth's (1993, Biometrika, 80, 27–38) penalized maximum likelihood (PML) estimator. Prior research has described and used PML, especially in the context of separation, but its small sample properties remain under-appreciated. The PML estimator eliminates most of the bias and, perhaps more importantly, greatly reduces the variance of the usual ML estimator. Thus, researchers do not face a bias-variance tradeoff when choosing between the ML and PML estimators—the PML estimator has a smaller bias and a smaller variance. We use Monte Carlo simulations and a re-analysis of George and Epstein (1992, American Political Science Review, 86, 323–337) to show that the PML estimator offers a substantial improvement in small samples (e.g., 50 observations) and noticeable improvement even in larger samples (e.g., 1000 observations).

The COVID-19 pandemic is an unparalleled global crisis. Yet, despite the grave adversity faced by citizens, incumbents around the world experienced a boost in popularity during the onset of the outbreak. In this study, we examine how the response to the COVID-19 outbreak in one country affected incumbent support in other countries. Specifically, we leverage the fact that the first country-wide lockdown on European soil, in Italy on 9 March 2020, happened during the fieldwork of surveys conducted in four other European countries, France, Germany, Poland and Spain. This allows us to examine how an event abroad that alerted citizens to an imminent crisis—prior to a similar domestic government response—influenced incumbent support. Our results indicate a crisis signal effect of Italy's COVID-19 lockdown, as support for the incumbent increased domestically in other European countries after the lockdown. Importantly, these findings suggest that incumbents can benefit from a crisis unfolding in other countries, even when their own performance in response to the same crisis is not yet fully clear. They illustrate the importance of developments abroad for incumbent approval and the difficulty facing citizens seeking to disentangle performance signals from exogenous shocks.

Are policy arguments more or less persuasive when they are made by female politicians? Using a diverse sample of American respondents, we conduct a survey experiment which randomly varies the gender associated with two co-partisan candidates across four policy debates. We find strong effects contingent on respondent partisanship and gender, most notably on the issue of access to birth control. On this issue, regardless of the candidate's stance, Democratic respondents, particularly Democratic men, are much more likely to agree with the female candidate. Conversely, Republican respondents, particularly Republican women, are much more likely to agree with the male candidate. We discuss the implications of our findings for the meaning of gender as a heuristic in a highly partisan environment.

When the United States intervenes in foreign countries, the lives of both foreign combatants and foreign civilians are put at risk. I examine two rhetorical strategies, the use of sanitized and dehumanizing language that can influence the public's support of foreign intervention. In the context of foreign policy, sanitized language operates by obscuring casualties of war, while dehumanizing language operates by devaluing the lives of groups of individuals. Drawing on data from two experiments, I find that sanitized language operates through creating less of an emotional reaction toward casualties of war, which causes individuals to adopt more hawkish foreign policy attitudes. I find that dehumanizing language also leads to more hawkish foreign policy attitudes, but, contrary to expectations, does not lead to increased disgust or anger toward dehumanized groups.

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.

When separation is a problem in binary dependent variable models, many researchers use Firth's penalized maximum likelihood in order to obtain finite estimates (Firth, 1993; Zorn, 2005; Rainey, 2016). In this paper, I show that this approach can lead to inferences in the opposite direction of the separation when the number of observations are sufficiently large and both the dependent and independent variables are rare events. As large datasets with rare events are frequently used in political science, such as dyadic data measuring interstate relations, a lack of awareness of this problem may lead to inferential issues. Simulations and an empirical illustration show that the use of independent “weakly-informative” prior distributions centered at zero, for example, the Cauchy prior suggested by Gelman et al. (2008), can avoid this issue. More generally, the results caution researchers to be aware of how the choice of prior interacts with the structure of their data, when estimating models in the presence of separation.

Focusing on one specific aspect of immigrant political integration—how authorities deal with their political right to demonstrate—we show in a large-scale survey experiment that liberal policy decisions permitting demonstrations lead to a polarization in attitudes: citizens who agree with a permission become more sympathetic, while those in favor of banning become more critical of immigrants. This notion of opinion backlash to policy decisions adds a new perspective to the literature on immigration attitudes which has either assumed a congruence between public opinion and policy or ignored political sources of anti-immigrant sentiment altogether. By exploring the unintended consequences of policy decisions, we provide an alternative view and demonstrate the inherent dilemma of balancing citizen opinion and minority rights.

Electoral violence is conceived of as violence that occurs contemporaneously with elections, and as violence that would not have occurred in the absence of an election. While measuring the temporal aspect of this phenomenon is straightforward, measuring whether occurrences of violence are truly related to elections is more difficult. Using machine learning, we measure electoral violence across three elections using disaggregated reporting in social media. We demonstrate that our methodology is more than 30 percent more accurate in measuring electoral violence than previously utilized models. Additionally, we show that our measures of electoral violence conform to theoretical expectations of this conflict more so than those that exist in event datasets commonly utilized to measure electoral violence including ACLED, ICEWS, and SCAD. Finally, we demonstrate the validity of our data by developing a qualitative coding ontology.

People are commonly expected not to waste their vote on parties with small probabilities of being elected. Yet, many end up voting for underdogs. We argue that voters gauge the popular support for their preferred party from their social networks. When social networks function as echo chambers, a feature observed in real-life networks, voters overestimate underdogs’ chances of winning. We conduct voting experiments in which some treatment groups receive signals from a simulated network. We compare the effect of networks with a high degree of homogeneity against random networks. We find that homophilic networks increase the level of support for underdogs, which provides evidence to back up anecdotal claims that echo chambers foster the development of fringe parties.