This study examines whether women politicians address violence against women (VAW) more effectively than their male counterparts at the local level in Mexico. Using a regression discontinuity design that leverages close mayoral elections, we find that women mayors reduce some of the most egregious violent crimes committed against women, with estimates suggesting a 64.7% reduction in homicides of women over their 3-year terms. As evidence of potential mechanisms, we find that women mayors actively work to combat VAW, appoint more women to leadership and support roles, and expand specialized services for crime victims. These findings suggest that women’s representation in local politics may be an important factor in advancing women’s safety.

How does a ruler implement state-building at the local level? This paper examines state-building in late 16th-century Japan by focusing on Toyotomi Hideyoshi’s land surveys, which were crucial for establishing a centralized regime. We argue that Hideyoshi strengthened control over the locality via land surveys as a strategic response to the perceived threats emanating from Catholic missionaries. Using various empirical strategies including spatial econometrics, sensitivity analysis, and an instrumental variable approach, we find that the presence of Catholic churches significantly increased the likelihood of a locality being surveyed. These results highlight the importance of information-gathering beyond fiscal purposes for security objectives and emphasize the role of threats from foreign religious institutions in state formation processes.

The increasing multimodality (e.g., images, videos, links) of social media data presents opportunities and challenges. But text-as-data methods continue to dominate as modes of classification, as multimodal social media data are costly to collect and label. Researchers who face a budget constraint may need to make informed decisions regarding whether to collect and label only the textual content of social media data or their full multimodal content. In this article, we develop five measures and an experimental framework to assist with these decisions. We propose five performance metrics to measure the costs and benefits of multimodal labeling: average time per post, average time per valid response, valid response rate, intercoder agreement, and classifier’s predictive power. To estimate these measures, we introduce an experimental framework to evaluate coders’ performance under text-only and multimodal labeling conditions. We illustrate the method with a tweet labeling experiment.

Despite widespread adoption of international anti-money laundering standards over the last 30 years, their effectiveness remains poorly understood due to persistent data limitations. I address this gap in the scholarship by leveraging cryptocurrency transaction data to assess how specific regulatory design features shape compliance. Using bunching estimation, I demonstrate that customers strategically adjust transaction sizes to avoid threshold-based screening requirements, while exchanges fail to adequately address this behavior through risk-based monitoring. Analysis of British Virgin Islands exchanges using difference-in-differences estimation before and after regulatory changes provides additional evidence supporting these conclusions. The findings reveal how regulatory design features shape behavior in cryptocurrency markets and suggest specific improvements for regulatory frameworks.

As the use of computational text analysis in the social sciences has increased, topic modeling has emerged as a popular method for identifying latent themes in textual data. Nevertheless, concerns have been raised regarding the validity of the results produced by this method, given that it is largely automated and inductive in nature, and the lack of clear guidelines for validating topic models has been identified by scholars as an area of concern. In response, we conducted a comprehensive systematic review of 789 studies that employ topic modeling. Our goal is to investigate whether the field is moving toward a common framework for validating these models. The findings of our review indicate a notable absence of standardized validation practices and a lack of convergence toward specific methods of validation. This gap may be attributed to the inherent incompatibility between the inductive, qualitative approach of topic modeling and the deductive, quantitative tradition that favors standardized validation. To address this, we advocate for incorporating qualitative validation approaches, emphasizing transparency and detailed reporting to improve the credibility of findings in computational social science research when using topic modeling.

We investigate whether election results are associated with emotional reactions among voters across democracies and under what conditions these responses are more intense. Building on recent work in comparative politics, we theorize that emotional intensity is stronger after elections involving populist candidates and highly polarized parties. We test these expectations with a big-data analysis of emotional reactions on parties’ Facebook posts during 29 presidential elections in 26 democracies. The results show that ideological polarization of political parties might intensify emotional reactions, but there is no clear relationship with the presence of populist candidates.

The boom in survey experiments in international relations has allowed researchers to make causal inferences on longstanding foreign policy debates such as democratic peace, and audience costs. However, most of these experiments rely on mass samples, whereas foreign policy is arguably more technocratically driven. We probe the validity of generalizing from mass to elite preferences by exploring preferences of ordinary U.S. citizens and foreign policy experts (employees of the U.S. Department of State) in two identical conjoint experiments on democratic peace. We find that experts are not only more opposed to military actions against other democracies than members of the public—but also that overall preferences about the matters of war and peace are stronger among foreign policy professionals.

Does democratic satisfaction drive voter turnout, or does voting increase satisfaction with democracy? This paper explores the satisfaction-participation nexus in Central and Eastern Europe (CEE), where democratic dissatisfaction is prominent. It tests preregistered hypotheses using a five-wave panel survey from the Czech 2023 presidential election and a pooled dataset from five CEE countries. Unlike previous studies from Western Europe, it finds evidence for both mechanisms: pre-election satisfaction correlates with participation, but, simultaneously, voters experience a stronger election-related increase in satisfaction than abstainers. Further analyses reveal that the strong increases in satisfaction are driven by election winners and begin already during the election campaign. Our findings highlight the specificities of the satisfaction-participation link and elections’ legitimizing effects in newer democracies.

Christian evangelicals now represent a significant share of the global population. Notably, they are expected to soon outnumber Roman Catholics in several low- and middle-income countries. This paper examines whether such episodes of religious minority growth can reshape electoral politics. To address this, I combine novel data spanning over two decades (1994–2018) of Christian evangelicals’ expansion across Brazilian municipalities with indicators of structural changes in electoral politics: voter turnout, competition, polarization, and conservatism. Regression models with unit and year-fixed effects reveal no impact of the evangelical boom on electoral competition and polarization, suggestive evidence of increasing conservatism in recent years, and a clear and robust negative effect on turnout. Regression discontinuity design estimates, leveraging an exogenous and discontinuous growth of Christian evangelicals in Brazil’s rural areas, support these findings. The results suggest that the rise of religious minorities may drive gradual transformations in electoral politics.

Research on the political consequences of terrorism often finds a rally around the flag effect: terrorist attacks, as other types of threats, tend to produce spikes in popularity and support for the incumbent, as citizens turn to those in power seeking protection. Most research, however, is based on single case studies that analyze very salient attacks from international terrorist organizations. Even if these studies are well identified, the question of generalizability remains, as the studied attacks are often very idiosyncratic. In this paper, we explore the rally around the flag effect in an arguably difficult context: a sustained terrorist campaign held by domestic terrorist groups in a parliamentary democracy (Spain). To overcome the limitations of the single-attack studies, we use a multiple unexpected event approach: we developed a systematic process of matching the occurrence of terror attacks during the fieldwork of official public opinion surveys in Spain, through which we identified 142 valid attack-survey pairs. We find that in the attacked region support for the incumbent increases, on average, around 4 percentage points right after an attack, while support for the opposition decreases in a similar amount. These effects seem to occur mostly for the conservative incumbent and are especially relevant for the attacks that target civilians. We use a survey experiment to provide additional evidence in support for our interpretation of the findings.

In recent decades, activists and leaders of government and nongovernment organizations have increasingly and explicitly called for greater attention to human dignity in their efforts to promote pro-social relations. In this study, we investigate whether appeals to this core human value actually influence how individuals act with regard to those who might be otherwise ignored or neglected. Using the digital advertising platform on Facebook, we randomly assign ads to over 90,000 adult American users to estimate the effects of dignity appeals on their likelihood of engaging with content concerning people facing homelessness or incarceration. Consistent with preregistered hypotheses and specifications, we find that adding dignity appeals increases the likelihood of positive reactions to such ads, but only when the vulnerable are considered less “blameworthy” for their situation.

Protest event analysis (PEA) is the core method to understand spatial patterns and temporal dynamics of protest. We show how Large Language Models (LLM) can be used to automate the classification of protest events and of political event data more broadly with levels of accuracy comparable to humans, while reducing necessary annotation time by several orders of magnitude. We propose a modular pipeline for the automation of PEA (PAPEA) based on fine-tuned LLMs and provide publicly available models and tools which can be easily adapted and extended. PAPEA enables getting from newspaper articles to PEA datasets with high levels of precision without human intervention. A use case based on a large German news-corpus illustrates the potential of PAPEA.

Extensive research showcases that authoritarian propaganda can cultivate support and deter protest during “normal” times. This study examines authoritarian propaganda’s efficacy during crises and policy changes when the regime needs it most. We posit that various propaganda strategies, including “hard” and “soft” rhetoric, have significant limitations during crises. Hard propaganda’s heavy-handed slogans could signal regime strength but may also legitimize “rightful resistance” against local authorities, limiting its protest-deterrence effects. Soft propaganda may lose persuasiveness due to presenting contradictory arguments during policy changes. We leverage the turbulent period of China’s COVID policy reversal to conduct an original survey experiment in December 2022. Our findings reveal that pro-reopening hard propaganda weakens its protest-deterrence effects by reinforcing belief in protest righteousness. Moreover, inconsistent soft propaganda lowers public evaluations of China’s COVID response, diminishing its persuasive effects. Our study highlights significant limitations of authoritarian propaganda during crises and policy changes.

In survey experiments, should all covariates be administered before the experimental treatment? Some scholars argue that post-treatment items should never be used as covariates because the treatment could bias the measurement of those items and disrupt experimental randomization. Other scholars argue certain items—specifically sensitive questions measuring prejudice—should not be administered pre-treatment. They argue if asked pre-treatment, these items may prime respondents in ways that will influence how they engage with the experiment treatment, thereby affecting the overall outcome of the experiment. Using evidence from four studies (two original collections) that vary the placement of sensitive items—pre-treatment, post-treatment, or in a separate wave—we find little evidence that the placement of sensitive items influences the measurement of those items, the experimental outcomes, nor heterogeneously affects the outcome conditional on the treatment. However, we find the placement of sensitive items inconsistently affects the experimental outcome by interacting with both the measurement of the items and the experimental treatment condition. Overall, we find these measures to be robust to where they are administered. It may be best to place items pre-treatment to preserve randomization. If researchers have reason to include sensitive moderators post-treatment, they should transparently discuss this choice and the anticipated trade-offs.

Researchers applying evolutionary theory to political psychology discover that in democracies, most citizens struggle to select political leaders based on their ideologies. Rather, they tend to concentrate on procedural fairness during public decision-making when evaluating their leaders. We re-examine such evolutionary propositions in China using eight Wason selection experiments. In autocracies, where accountability systems are weak or absent, little is known about how citizens judge politicians’ ideologies and their cheating behaviors. Our findings show that Chinese citizens are incapable of identifying political leaders’ ideological orientations; instead, they rely on a cheater-detection mechanism, evaluating leaders based on their adherence to procedural fairness. These results contribute to our understanding of democratic competence and the cognitive mechanisms of political judgment in autocratic contexts.

Can the “us versus them” dynamic in politics undermine support for democracy even in the absence of strong party identification? While much is known about affective polarization in the USA, its impact on democratic commitment in other contexts remains understudied. We examine Chile’s 2022 plebiscite, where voters decided whether to approve or reject a new constitution amid low levels of party trust and identification. Through an experiment using an unobtrusive primer, we successfully induced short-term affective polarization, heightening animosity across multiple dimensions. Our findings show that individuals primed to this polarization significantly reduced their support for democracy, mirroring patterns observed in the USA. These results emphasize the importance of studying affective polarization, especially in regions with fragile democratic histories.

Presidential primary elections arguably represent the most dynamic campaigns in American politics. Television advertising is a key aspect of strategy that candidates can marshal throughout the campaign. We develop a methodology for measuring the impact of advertising in primary elections that accounts for endogeneity and apply it to the 2000 through 2016 elections. We find that advertisements—both positive and negative—improve the favorability and the vote share of the candidate running the ads. We find that negative advertising is more effective than positive advertising, but that only high polling candidates lose support when attacked.

Industrial concentration has increased in recent years with large companies consolidating their dominant positions. Concentrated markets are thought to benefit large firms as they earn elevated profits and gain political influence. Antitrust law is the main policy tool to reduce concentration. Calls to strengthen antitrust have come from the political left and the right, yet we know little about public support for such policies. We test how economic, moral, and democratic concerns influence support for antitrust. We find that the public does not respond to the consumer price benefits of antitrust but is moved by arguments invoking concerns for fairness and the importance of maintaining democratic institutions. We find that Republicans and Democrats often respond in divergent ways, with Republicans being less supportive of antitrust when informed that it could punish successful companies, whereas Democrats are more concerned about using antitrust to curb corporate influence. The findings accord with a general concern on the left for limiting business influence in politics and a concern on the right for maintaining business growth.

Emotions and their sociopolitical impact have received increasing scholarly attention. However, it remains largely unclear whether emotional expression within surveys is subject to social desirability bias. By drawing on impression management theory and the disclosure decision model, I argue that emotional expression is likely prone to social desirability bias in interviewer-administered survey modes and test my hypotheses on mixed-mode ANES data. The findings demonstrate that respondents significantly underreport negative emotions—anger and fear—when interviewed face-to-face as compared to online. Furthermore, positive emotions, such as hope and pride, are not exempt from biased reporting related to interview mode. These results highlight the risks of estimating emotions and their salience by either relying on interviewer administration or combining survey modes.

Despite growing interest in the political consequences of the urban–rural divide, we know little about whether urban and rural populations differ in the policy problems they view as most important. This study explores urban–rural differences in policy priorities over an extended period (1939–2020), using data from 850 U.S. surveys. The analysis reveals modest but persistent gaps between urban and rural residents in several key policy areas. However, while urban–rural differences remain stable across diverse economic and political contexts, partisan affiliation significantly outweighs place-based identities in shaping these priorities. The findings suggest that despite geographic distinctions, urban and rural populations predominantly rely on partisan cues when forming policy agendas, which contributes to a nuanced understanding of political representation.