Being popular makes it easier for dictators to govern. A growing body of scholarship therefore focuses on the factors that influence authoritarian popularity. However, it is possible that the perception of popularity itself affects incumbent approval in autocracies. We use framing experiments embedded in four surveys in Russia to examine this phenomenon. These experiments reveal that manipulating information—and thereby perceptions—about Russian President Vladimir Putin’s popularity can significantly affect respondents’ support for him. Additional analyses, which rely on a novel combination of framing and list experiments, indicate that these changes in support are not due to preference falsification, but are in fact genuine. This study has implications for research on support for authoritarian leaders and defection cascades in nondemocratic regimes.

Research shows that sexist attitudes are deeply ingrained, with adverse consequences in the socioeconomic and political sphere. We argue that parental leave for fathers—a policy reform that disrupts traditional gender roles and promotes less stereotypical ones—has the power to decrease attitudinal gender bias. Contrasting the attitudes of new parents who were (and were not) directly affected by a real-world policy reform that tripled the amount of fathers’ leave, we provide causal evidence that the reform increased gender-egalitarian views in the socioeconomic and political domains among mothers and fathers, and raised support for pro-female policies that potentially displace men among mothers. In contrast, informational, indirect exposure to the reform among the general public produced no attitudinal change. These results show that direct exposure to progressive social policy can weaken sexist attitudes, providing governments with a practical and effective tool to reduce harmful biases.

The literature on early modern state-building in Europe has focused on war as its main driver and therefore on states’ relationships with men. Feminist scholars have critiqued the Weberian conceptions this literature relies on as being gender biased. I suggest an alternative theoretical starting point for theories of early modern state-building: the political imperatives created by the demographic fluctuations of the Malthusian trap. Harnessing Foucault’s concept of biopower and its application to the construction of gender, I argue that population fluctuations incentivized demographic regulation, in particular of childbearing, in order to keep birth rates high and maternal and infant mortality low, implying that early modern European states were constituted through the construction and maintenance of gender regimes. I propose strategies for empirical investigation and argue that a more accurate account of early modern European state-building needs to incorporate demographic regulation and therefore requires gender to be at its center.

How does the gender composition of deliberative committees affect citizens’ evaluations of their decision-making processes? Do citizens perceive decisions made by gender-balanced, legislative bodies as more legitimate than those made by all-male bodies? Extant work on the link between women’s descriptive representation and perceptions of democratic legitimacy in advanced democracies finds the equal presence of women legitimizes decision-making. However, this relationship has not been tested in more patriarchal, less democratic settings. We employ survey experiments in Jordan, Morocco, and Tunisia to investigate how citizens respond to gender representation in committees. We find that women’s presence promotes citizens’ perceptions of the legitimacy of committee processes and outcomes and, moreover, that pro-women decisions are associated with higher levels of perceived legitimacy. Thus, this study demonstrates the robustness of findings from the West regarding gender representation and contributes to the burgeoning literature on women and politics.

We show that a common method of predicting individuals’ race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter’s neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%.

When choosing what cases to investigate, do the police discriminate on the basis of caste and class? We employ a conjoint design to evaluate biases in police officers’ preferences for investigation based on perpetrator attributes. Conducting a survey of law enforcement officers in Nepal, we find evidence of discriminatory investigation practices. Absent constraining protocols that reduce officer discretion, police officers are more likely to target offenders who are from caste-class subjugated communities. Additionally, police officers’ assessments of institutional investigatory preferences reveal caste-based considerations: officers believe the police, in general, prefer to investigate low-caste offenders over high-caste offenders. They do not, however, perceive their institution as having class-based biases. These findings add to the body of evidence on whether police discriminate, which has previously focused on use of lethal force and police stops, and further demonstrate that concerns over systemic bias in policing are warranted.

Do democratic regimes depend on public support to avoid backsliding? Does public support, in turn, respond thermostatically to changes in democracy? Two prominent recent studies (Claassen 2020a; 2020b) reinvigorated the classic hypothesis on the positive relationship between public support for democracy and regime survival—and challenged its reciprocal counterpart—by using a latent variable approach to measure mass democratic support from cross-national survey data. However, both studies used only the point estimates of democratic support. We show that incorporating the concomitant measurement uncertainty into these analyses reveals that there is no support for either study’s conclusion. Efforts to minimize the uncertainty by incorporating additional survey data still fail to yield evidence in support of either hypothesis. These results underscore the need for both more nuanced analyses of the relationships between public support and democracy and taking measurement uncertainty into account when working with latent variables.

In this letter, we assess whether the contributions of judges from underrepresented groups are undervalued or overlooked, thereby reducing these judges’ influence on legal policy. Drawing on an original dataset of discretionary citations to over 2,000 published federal appellate decisions, we find that the majority of opinions written by female judges receive less attention from other courts than those by similarly situated men and that this is largely attributable to disparities in citing Black women and Latinas. We also find that additional efforts by Black and Latinx judges to ground their opinions in precedent yield a much lower rate of return in subsequent citations by outside circuits than comparable work by white men and women judges. This suggests that, despite gains in diversification in the federal judiciary, stereotypes about social identities will play a powerful role in determining whose ideas receive recognition.

Why are some constitutions amended more frequently than others? The literature provides few clear answers, as some scholars focus on institutional factors, whereas others emphasize amendment culture. We bridge this divide with new theoretical and empirical insights. Using data from democratic constitutions worldwide and U.S. state constitutions, we examine how social capital reduces the transaction costs imposed by amendment rules. The results indicate that constitutional rigidity decreases amendment frequency, but group membership, civic activism, and political trust can offset the effect of amendment rules. Our findings have important implications for scholars in public law, constitutional and democratic theory, and social movements.

A wealth of recent research in comparative politics examines how spatial variation in historical conditions shapes modern political outcomes. In an article in the American Political Science Review, Homola, Pereira, and Tavits argue that Germans who live nearer to former Nazi concentration camps are more likely to display out-group intolerance. Clarifying the conceptual foundations of posttreatment bias and reviewing the historical record on postwar state creation in Germany, we argue that state-level differences confound the relationship between distance to camps and out-group intolerance. Using publicly available European Values Survey data and electoral results from 2017, we find no consistent evidence that distance to camps is related to contemporary values. Our findings have implications for literatures on historical persistence, causal inference with spatial data, Holocaust studies, and outgroup tolerance.

The Historical Social Conflict Database (HiSCoD) is an ongoing project designed to provide to scholars and society at large with a set of resources for analyzing social conflict from the Middle Ages to the second half of the nineteenth century (c. 1000–c. 1870). Based on original archival research and existing repositories, the aim is to provide a global database of social conflict in past societies by collecting, aggregating, documenting, and harmonizing instances of conflict. As of today, the database contains data on more than twenty thousand events, from fiscal scuffles to urban revolts involving thousands of individuals. For every event, we provide information on the date, location, type of conflict, and, when possible, number of participants, participation of women, and a summary of events.

Multilevel regression and post-stratification (MRP) is a popular use of hierarchical models in political science. Multiple papers have suggested that relying on machine learning methods can provide substantially better performance than traditional approaches that use hierarchical models. However, these comparisons are often unfair to traditional techniques as they omit possibly important interactions or nonlinear effects. I show that complex (“deep”) hierarchical models that include interactions can nearly match or outperform state-of-the-art machine learning methods. Combining multiple models into an ensemble can improve performance, although deep hierarchical models are themselves given considerable weight in these ensembles. The main limitation to using deep hierarchical models is speed. This paper derives new techniques to further accelerate estimation using variational approximations. I provide software that uses weakly informative priors and can estimate nonlinear effects using splines. This allows flexible and complex hierarchical models to be fit as quickly as many comparable machine learning techniques.

Pepinsky, Goodman, and Ziller (2024, American Political Science Review, PGZ) reassess a recent study on the long-term consequences of concentration camps in Germany. The authors conclude that accounting for contemporary (i.e., post-treatment) state heterogeneity in the models provides unbiased estimates of the effects of camps on current-day outgroup intolerance. In this note, we show that PGZ’s empirical strategy rests on (a) a mischaracterization of what regional fixed effects capture and (b) two unrealistic assumptions that can be avoided with pre-treatment state fixed effects. We further demonstrate that results from the original article remain substantively the same when we incorporate regional fixed effects correctly. Finally, simulations reveal that camp proximity consistently outperforms spatially correlated noise in this specific study. The note contributes to the growing literature on legacy studies by advancing the discussion about the correct modeling choices in this challenging field.