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.