Existing literature examines the effectiveness of civilian oversight in reducing police misconduct. However, little-to-no quantitative research explores possible adverse consequences of this accountability mechanism. Utilizing time series analysis of administrative data on aggregate monthly civilian complaints and police behavior in the largest American city, this article offers evidence of racial inequality in police response to civilian complaints. For White civilians, complaint against the police abates subsequent police stops. For Black civilians, complaint is associated with subsequent intensification of police stops. This intensification only follows complaints against White officers, is conditional upon officer knowledge of the complaint, is confined to stops involving greater officer discretion to perform the stop, and is only observed in police precincts with large Black populations.

We study how an intervention combining youth intergroup contact and sports affects intergroup relations in the context of an active conflict. We first conduct a randomized controlled trial (RCT) of one-year program exposure in Israel. To track effects of a multiyear exposure, we then use machine-learning techniques to fuse the RCT with the observational data gathered on multiyear participants. This analytical approach can help overcome frequent limitations of RCTs, such as modest sample sizes and short observation periods. Our evidence cannot affirm a one-year effect on outgroup regard and ingroup regulation, although we estimate benefits of multiyear exposure among Jewish-Israeli youth, particularly boys. We discuss implications for interventions in contexts of active conflict and group status asymmetry.

We experimentally explore the role of institutions imposing collective sanctions in sustaining cooperation. In our experiment, players only observe noisy signals about individual contributions in finitely repeated public goods game with imperfect monitoring, while total output is perfectly observed as it is often the case in collective action problems in society. We consider sanctioning mechanism that allows agents to commit to collective punishment in case the level of cooperation among members of society falls short of a target. We find that cooperation is higher with collective punishment compared to both no punishment or punishment targeting individuals. Importantly, our results indicate that it is the combination of making a commitment to be punished and the collective nature of punishment which induces cooperation. Our findings show that punishing a group collectively for misbehavior of some of its members induces cooperation when individuals participate in setting up the sanctioning institution. The study contributes to the literature on institutional legitimacy and how to ensure good government performance when dealing with collective action problems, and, by considering commitment, improves enforcement methods criticized for their detrimental effects on some societal groups.

Discussions of terrorism assume actual or threatened violence, but the term is regularly used to delegitimize rivals' nonviolent actions. Yet do ordinary citizens accept descriptions of nonviolence as terrorism? Using a preregistered survey-experiment in Israel, a salient conflictual context with diverse repertoires of contention, we find that audiences rate adversary nonviolence close to terrorism, consider it illegitimate, and justify its forceful repression. These perceptions vary by the action's threatened harm, its salience, and respondents' ideology. Explicitly labeling nonviolence as terrorism, moreover, particularly sways middle-of-the-road centrists. These relationships replicate in a lower-salience conflict, albeit with milder absolute judgments, indicating generalizability. Hence, popular perceptions of terrorism are more fluid and manipulable than assumed, potentially undermining the positive effects associated with nonviolent campaigns.

What allows autocratic political elites to coordinate with each other and check dictators? Earlier work assumes that elite coordination becomes easier as dictators share more power. I argue that, even when power-sharing is pervasive, a lack of cohesiveness can rob regime members of their influence over dictators. Conflicting interests can leave seemingly powerful elites unable to agree on when or how to challenge the dictator. I develop a measure of elite collective action based on this framework. It documents not only how much power elites have, but also the obstacles they would need to resolve to work together. In doing so, it better describes the relationship between autocrats and their ruling coalition, and its added nuance allows it to recover findings that current measures miss. By paying closer attention to the cohesion of autocratic elites, this paper explains why some dictators can rule tyrannically without retribution, and why weaker elites can still constrain powerful autocrats.

Even in the best-designed experiment, noncompliance can complicate analysis. While the intent-to-treat effect remains identified, randomization alone no longer identifies the complier average causal effect (CACE). Instrumental variables approaches, which rely on the exclusion restriction, can suffer from high variance, particularly when the experiment has a low compliance rate. We provide a framework which broadens the set of design and analysis techniques political science researchers can use when addressing noncompliance. Building on the growing literature about the advantages of ex-ante design decisions to improve precision, we show blocking on variables related to both compliance and the outcome can greatly improve all the estimators we propose. Drawing on work in statistics, we introduce the principal ignorability assumption and a class of principal score weighting estimators, which can exhibit large gains in precision in low compliance settings. We then combine principal ignorability and blocking with a simple estimation strategy to derive a more efficient estimation strategy for the CACE. In a re-evaluation of a study on the effect of GOTV on turnout, we find that the principal ignorability approaches result in confidence intervals roughly half the size of traditional instrumental variable approaches.

Economic development and accompanying structural changes of the economy create new opportunities, however not everywhere and not for everyone. Development increases the demand for skilled labor, improving their welfare perceptions, but low-skilled workers feel more insecure and worse off economically. This adverse effect results from a mismatch between local labor demand and individual skill sets. To measure the development levels of people's local environments, I combine geocoded Afrobarometer data and night lights. Bayesian multilevel analysis confirms that the highly skilled are economically most dissatisfied in lagging areas, while the low-skilled are less satisfied in highly developed environments. These findings emphasize the importance of local conditions for welfare perceptions and show the unequal effect of development leaving behind large parts of the population.

We use an online experiment to study the relative effect on voter behavior of a candidate’s voice pitch and policy stance. We demonstrate a strong voice-pitch bias: between candidates who are identical in every other aspect, voters are more likely to choose the one with the lower voice-pitch, and more so in elections between men than women candidates. We then introduce a novel phenomenon: persistence of voice-pitch bias is the amount of policy difference needed to compensate for voice-pitch bias. While persistence is also gender-dependent, the effect is now reversed: voice-pitch bias is more persistent in elections between women than men candidates. As a possible mechanism, we show that voters perceive candidates with lower voice-pitch as more competent and trustworthy.

Stereotypes of the two parties play an important role in political cognition, and a range of recent studies have examined the content and effects of partisan stereotypes. However, little work has studied change in partisan stereotypes over time. We address this question by comparing data on stereotypes of partisans collected before and after the Trump presidency, a time when we might expect individuals' images of the two parties to undergo significant change. Using a structural topic model, we compare responses to open-ended questions asking respondents to list words describing members of the two parties from 2016 and 2021. We find that partisan stereotypes in the 2021 sample are less group- and issue-based and focused more on personal traits. These results suggest that, during the Trump era, members of the mass public came to see the parties in more personalized, character-focused terms, potentially contributing to affective polarization.

Some work in political methodology recommends that applied researchers obtain point estimates of quantities of interest by simulating model coefficients, transforming these simulated coefficients into simulated quantities of interest, and then averaging the simulated quantities of interest (e.g., CLARIFY). But other work advises applied researchers to directly transform coefficient estimates to estimate quantities of interest. I point out that these two approaches are not interchangeable and examine their properties. I show that the simulation approach compounds the transformation-induced bias identified by Rainey (2017), adding bias with direction and magnitude similar to the transformation-induced bias. I refer to this easily avoided additional bias as “simulation-induced bias.” Even if researchers use simulation to estimate standard errors, they should directly transform maximum likelihood estimates of coefficient estimates to obtain point estimates of quantities of interest.

Political divisions in the lead-up to the 2020 US presidential election were large, leading many to worry that heighted partisan conflict was so stark that partisans were living in different worlds, divided even in their understanding of basic facts. Moreover, the nationalization of American politics is thought to weaken attention to state political concerns. 2020 therefore provides an excellent, if difficult, test case for the claim that individuals understand their state political environment in a meaningful way. Were individuals able to look beyond national rhetoric and the national environment to understand state-level electoral dynamics? We present new data showing that, in the aggregate, despite partisan differences in electoral expectations, Americans are aware of their state's likely political outcome, including whether it will be close. At the same time, because forecasting the overall election outcome is more difficult, Electoral College forecasts are much noisier and display persistent partisan difference in expectations that do not differ much with state of residence.

While a number of high-level figures around the world have been prosecuted and even jailed for corruption in recent years, we know little about how such anticorruption efforts shape public opinion and patterns of political engagement. To address this question, we examine evidence from Argentina and Costa Rica involving the unprecedented sentencing of two former Presidents on corruption charges. Exploiting the coincidence in timing between these cases and fieldwork on nationally representative surveys, we find that citizens interviewed in the aftermath of these events expressed lower trust in institutions and were less willing to vote or join in collective demonstrations. Overall, these findings suggest that high-profile efforts to punish corrupt actors may have similar effects as political scandals in shaping citizens’ relationship to the political system.

In latent scaling applications, such as the positioning of political parties, differential item functioning (DIF) may occur because of measurement issues or because of substantive differences in the association between latent and manifest variables. While the first source of DIF has received considerable attention, the second has not, although it is of potential interest to comparative scholars. In this research note, we introduce a novel hierarchical Bayesian item response model that allows us to disentangle different sources of DIF. Drawing on the 2019 Chapel Hill Expert Survey (CHES), we highlight how the same issues are unequally politicized across Western Europe, and how some issues are less ideologically determined than others. Our model can be adapted to alternate settings, allowing researchers to shine a light on variation in, e.g., ideology, issue politicization, or party competition.

Despite a renewed interest in the health of the US administrative state, the absence of meaningful time-series measures of bureaucratic capacity hinders the testing of core theories of bureaucratic and executive politics. Using over 190 million personnel records, I estimate 5590 yearly policymaking-capacity scores for 261 unique agencies from 1998 to 2021. These measures provide an invaluable tool as either an independent or dependent variable in studies of administrative policymaking. To illustrate the value of these measures, I test longstanding theories about the relationship between bureaucratic autonomy and capacity. In contrast with emerging survey research, this study demonstrates that agencies with higher levels of structural independence have higher levels of policymaking capacity.

Survey experiments that investigate how voting procedures affect voting behavior and election outcomes use hypothetical questions and non-representative samples. We present here the results of a novel survey experiment that addresses both concerns. First, the winning party in our experiment receives a donation to its campaign funds inducing real consequences for voting. Second, we run an online experiment with a Dutch national representative sample (N = 1240). Our results validate previous findings using a representative sample, in particular that approval voting leads to a higher concentration in votes for smaller parties and strengthens centrist parties in comparison to plurality voting. Importantly, our results suggest that voting behavior is not affected by voting incentives and can be equally reliably elicited with hypothetical questions.

Rural residents are typically construed as being more conservative than urban residents. Is this true, or do rural residents carry unique interests from Republicans or conservatives? Using the 2020 ANES, we compare responses from 22 national issues by interacting urban and rural residency with Republican and Democrat identification. We find that issue preferences are partisan, not place-based: rural Democrats resemble their urban counterparts and urban Republicans resemble their rural counterparts, rather than rural areas specifically being more Republican. However, rural Democrats are more conservative than urban Democrats on issues relating to immigration, transgendered people in the military, and income inequality. These results point to partisan nationalization in issue stances, with exceptions, and that rural is not always Republican.