The remuneration of MPs affects who engages in politics. Even if average returns to office are positive, as found in all other studies, some officeholders’ returns are likely negative. Further, the timing of returns to office is crucial as politicians often enjoy delayed compensation like lucrative pensions. Utilizing administrative data for parliamentary candidates in Denmark from 1994 to 2015, we estimate first-time runners’ earnings and total income returns to office. Based on total income, practically all elected MPs experience economic gains during their first term. Computations of life-cycle returns reveal that the 25 percent highest earning candidates (pre-office) have no long-term economic gain from winning. Generally, considering the distribution and timing of returns to office improves studies of how office-holding is economically rewarded.

When and why do wealthy individuals support redistribution? Under standard political economy models, preferences for redistribution are a function of material conditions. The partisanship literature, on the contrary, argues that partisan identification determines redistributive preferences. We move beyond this dichotomy to argue that the ideology of the government enacting redistribution is a key factor explaining support for redistribution among the wealthy. Through survey experiments during the 2022 Colombian election, we find that the wealthy are more likely to support redistribution under a right-wing government and expect redistribution under the Right to be more efficient and less economically disruptive. We find heterogeneous treatment effects across ideological groups. However, regardless of ideology, the wealthy do not expect macroeconomic instability from right-wing redistribution.

Supervised learning is increasingly used in social science research to quantify abstract concepts in textual data. However, a review of recent studies reveals inconsistencies in reporting practices and validation standards. To address this issue, we propose a framework that systematically outlines the process of transforming text into a quantitative measure, emphasizing key reporting decisions at each stage. Clear and comprehensive validation is crucial, enabling readers to critically evaluate both the methodology and the resulting measure. To illustrate our framework, we develop and validate a measure assessing the tone of questions posed to nominees during U.S. Senate confirmation hearings. This study contributes to the growing literature advocating for transparency and rigor in applying machine learning methods within computational social sciences.

Covariance structure analysis or structural equation modeling is critical for political scientists measuring latent structural relationships, allowing for the simultaneous assessment of both latent and observed variables, alongside measurement error. Well-specified models are essential for theoretical support, balancing simplicity with optimal model fit. However, current approaches to improving model specification searches remain limited, making it challenging to capture all meaningful parameters and leaving models vulnerable to chance-based specification risks. To address this, we propose an improved Lagrange multiplier (LM) test incorporating stepwise bootstrapping in LM and Wald tests to detect omitted parameters. Monte Carlo simulations and empirical applications underscore its effectiveness, particularly in small samples and models with high degrees of freedom, thereby enhancing statistical fit.

Voters’ issue preferences are key determinants of vote choice, making it essential to reduce measurement error in responses to issue questions in surveys. This study uses a MultiTrait MultiError approach to assess the data quality of issue questions by separating four sources of variation: trait, acquiescence, method, and random error. The questions generally achieved moderate data quality, with 76% on average representing valid variance. Random error made up the largest proportion of error (23%). Error due to method and acquiescence was small. We found that 5-point scales are generally better than 11-point scales, while answers by respondents with lower political sophistication achieved lower data quality. The findings indicate a need to focus on decreasing random error when studying issue positions.

In multiparty systems, parties signal conflict through communication, yet standard approaches to measuring partisan conflict in communication consider only the verbal dimension. We expand the study of partisan conflict to the nonverbal dimension by developing a measure of conflict signaling based on variation in a speaker’s expressed emotional arousal, as indicated by changes in vocal pitch. We demonstrate our approach using comprehensive audio data from parliamentary debates in Denmark spanning more than two decades. We find that arousal reflects prevailing patterns of partisan polarization and predicts subsequent legislative behavior. Moreover, we show that consistent with a strategic model of behavior, arousal tracks the electoral and policy incentives faced by legislators. All results persist when we account for the verbal content of speech. By documenting a novel dimension of elite communication of partisan conflict and providing evidence for the strategic use of nonverbal signals, our findings deepen our understanding of the nature of elite partisan communication.

Are people's priorities associated with their income and education levels? There is a long history in political science of claims that priorities are driven by economic interests, but also that low-income and low-education people fail to prioritize their economic interests. In this paper we use measures of revealed importance from [Sides J, Tausanovitch C and Vavreck L (2023) The Bitter End: The 2020 Presidential Campaign and the hallenge to American Democracy. Princeton University Press.] to evaluate the priorities of high- and low-income/education voters with respect to 44 different policies. It is well known that there are substantial differences in the preferences of people with lower incomes or education levels and people with higher incomes or education levels, but conditional on preferences we find very small differences among education and income groups in terms of priorities. Like high-income and high-education voters, lower-income and education voters care most about the major issues of the day. They do not care systematically more or less than other voters about policies that expand social welfare, redistribution, or labor rights.

In this article, I center substantial improvement in subnational democracy in the U.S. states as an object of inquiry and seek to explain it. I theorize that strong unions, high Democratic Party control of state government, an especially liberal Democratic Party, a large population of people of color, and a particularly liberal public mood may each contribute to substantial improvement in democratic quality. Using Coincidence Analysis (CNA), a configurational causal method, I assess the evidence for my hypotheses. The CNA identifies three alternative paths to substantial improvement in electoral democracy in the states. The results of my analysis highlight that substantial improvement in electoral democracy is the product of political struggle centrally involving unions and the Democratic Party.

Governments shape policy outcomes using two distinct mechanisms: rules and discretion. A simple decomposition strategy is proposed for distinguishing between these policymaking mechanisms on income inequality in the American states from 1986 to 2020. This analytical strategy is easily applicable to other policy settings. The statistical evidence, for the most part, that income inequality observed in the American states is generally unaffected by both TELs and partisan control of state governments—the lone exception being unified Republican state governments operating under a TEL. The decomposition evidence, however, shows that this is primarily the result of discretionary policymaking differences among partisan governments. This study underscores the importance of disentangling policy mechanisms that jointly occur when evaluating the consequences of government policymaking authority.

Does group-based tribal thinking against ethnic out-groups condition support for both liberal and illiberal policies? Our thesis is that, irrespective of the direction of the policy (progressive or conservative), nativists express selective support for policies based on different signals of group-identity: descriptive markers, group-based substantive representation, in- and out-group norms, and group-based reasoning. We test this theoretical expectation using a novel AI-powered visual conjoint experiment in the Netherlands and Germany that asked individuals to select between hypothetical educational reform proposals presented by civic actors during a public consultation. Empirically, our results demonstrate that citizens, on average, are indeed selectively (il)liberal and that this instrumental policy support is greater among those with higher levels of underlying nativism. Specifically, we show that—among our multidimensional markers of group-based identities, norms, and reasoning—group-based substantive representation and in-group norms are the strongest determinants of support for diverse reform proposals. These findings have key implications on the malleable nature of citizens’ support for the backsliding of the liberal tenets of democracy as well as the persuasive power of out-group disidentification.

A successful energy transition requires the reallocation of private capital away from fossil fuel assets to greener alternatives. This transition is typically hindered by investors’ focus on today’s returns. In times of crisis, however, credible and unambiguous political signals about the future profitability of green industries can steer investments toward low-carbon assets. Drawing on European Union interventions during the onset of the Russian invasion of Ukraine, we present an event study of daily stock market returns following the most salient policy announcements by the European Commission in 2022. Our analysis shows that markets for shares of EU-based energy firms were initially prepared to move capital to cleaner companies, suggesting support for the clean energy transition. However, the short-lived distributional effects materialized only for announcements that could unmistakably be understood as unwavering commitments to the EU’s green renewal, while more ambiguous announcements did not have the same distributional implications. Our findings emphasize that repeated and unambiguous political signals during crisis episodes can create favorable market conditions, at least in the short term, to support capital reallocation toward greener stocks.

Do external threats increase American bipartisanship? We subject this question to an experimental test. Leveraging the Biden and Trump administrations’ similar characterization of the China threat, we exposed American respondents to real-world primes about security threats from China, while randomizing the messenger of such primes. We find that the threat primes—regardless of the partisan identity of their messenger—boosted Democrats’ and Republicans’ support for assertive foreign policy in a largely parallel manner, thereby failing to reduce preference polarization. Importantly, there were no measurable changes across multiple indicators of affective polarization. These findings clarify the limits of external threats in uniting Americans, while also challenging recent perspectives that external threats—often colored by elite rhetoric—will further polarize the American public.

What are the effects of campaigns of coercive social mobilization on political attitudes? We show that such policies can strengthen authoritarian regimes by altering citizens’ patterns of trust. From 1968 to 1978, 16–17 million Chinese teenagers were “sent-down” to labor in rural areas, where they lived without their families under difficult conditions. Using a regression discontinuity design to account for selection into being sent-down, we show that former sent-down students are more critical of local government performance compared to their counterparts, yet they are less critical of the national government and generally more supportive of the regime. We see no significant differences in political participation, though there is some suggestive evidence that the sent-down students are more likely to favor officially sanctioned political activities. These results appear to stem from the close social control and isolation from family associated with the sent-down experience.

Can autocratic governments influence foreign media? This study finds that political pressures from autocrats can motivate the media in democracies to tone down their negativity. Exploiting China's sudden expulsion of American journalists in March 2020, I show that US news outlets targeted by the expulsion adopted a more positive tone toward China in their subsequent coverage, compared to outlets that were not targeted. Further analyses confirm that the observed pattern is not due to unexpelled outlets presenting more negative coverage of China, and that the expulsion has similar chilling effects on media outlets that could have been affected. The findings highlight the overt threats autocracies pose to media freedom, a fundamental pillar of democratic societies.

This paper introduces a simple approach for assessing which survey questions are more likely to elicit political identity-influenced responses. We use daily data from Gallup to test which survey self-reports exhibit more or less susceptibility to politicization, finding the highest likelihood of politicization for societal-level questions. Conversely, we show that self-reported assessments of personal finances are less sensitive to partisan motivated responding. We also show how egotropic economic evaluations are influenced by the presence of other items on the same survey. Taken together, our results uncover scope conditions for how to interpret self-reported views of the economy, and we argue that measures of public opinion which have not yet been strongly politicized are better proxies for capturing the underlying welfare of the public.

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.

Mexico's war on drugs is increasingly characterized by small, local groups rather than large cartels. This research note introduces new data developed from a narcoblog—a citizen journalism website—on more than 450 criminal organizations operating in Mexico between 2009 and 2020. I use the data to test prominent theories of fragmentation, providing suggestive evidence that drug war policies contributed to a more complex conflict: kingpin removals were correlated with the emergence of smaller groups; profit opportunities (in this case, fuel theft) then attracted these organizations to new territories. This research contributes to our understanding of criminal control and informs debates over violence reduction policies.

Public perceptions of immigration are often inaccurate, yet research lacks conceptual clarity and valid measurement of these misperceptions. Prior work focuses mainly on population innumeracy (misestimating immigrant shares) and cannot distinguish genuine misperceptions from mere guessing. We introduce a survey module that captures multiple dimensions of immigration-related perceptions alongside respondents’ confidence in their estimates. Using population survey data from Switzerland, we develop confidence-weighted indicators that separate misperception from guessing. Although inaccurate perceptions are widespread across several immigration domains, they are less prevalent than often assumed; guessing accounts for a substantial share of observed inaccuracy. This measurement strategy enables more precise empirical tests of theories linking perceptions to political attitudes and behavior.

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.

A growing literature explores the effect of economic inequality in citizens’ surrounding environment on their political attitudes and behavior. This literature typically relies on measures of income concentration or gap-size, which reflect under-tested presumptions about how citizens perceive the economic conditions surrounding them. Utilizing survey data to explore perception of economic inequality in Americans’ residential environment, this note finds that measures capturing income concentration or gap-size perform poorly relative to a measure capturing the joint prevalence of “haves” and “have-nots.” These results suggest that commonly used measures of economic inequality may not fully capture the features of people’s daily environment used to perceive the existence or magnitude of inequality. The results guide future research toward using contextual indicators that treat inequality as a compound phenomenon involving manifestations of poverty and affluence.