While presidents frequently create new policies through unilateral power, empirical scholarship generally focuses on executive orders and overlooks other categories of directives. We introduce data on more than 50,000 unilateral directives issued between 1877 and 2020 and use machine learning techniques to characterize their substantive importance and issue areas. Our measures reveal significant increases in unilateral activity over time, driven largely by increases in foreign affairs and through the substitution of memoranda for executive orders. We use our measures to formally evaluate the historical development of the unilateral presidency and reassess theoretical claims about public opinion and unilateral power. Our research provides new evidence about variation in the use of presidential authority and opens new avenues for empirical inquiry.

Prominent recent work argues that support for democracy behaves thermostatically—that democratic erosion boosts democratic support while deepening democracy yields public backlash—and further contends that there is no evidence for the classic argument that democracy itself increases democratic support over time. Here, we document how these conclusions depend on subtle choices in measurement coding that constitute “researcher degrees of freedom”: analyses employing alternative reasonable choices provide little or no support for the original conclusions. The fragility of the statistical results demonstrates that researcher degrees of freedom in measurement must be taken seriously and that the question of the relationship between democratic institutions and democratic support remains unsettled.

Under closed-list proportional representation, a party's electoral list determines the order in which legislative seats are allocated to candidates. When candidates differ in their ability, parties face a trade-off between competence and incentives. Ranking candidates in decreasing order of competence ensures that elected politicians are most competent. Yet, party lists create incentives for candidates that may lead parties not to place their best candidates at the top of the list. We examine this trade-off in a game-theoretical model in which parties rank their candidates on a list, candidates choose their campaign effort, and the election is a team contest for multiple prizes. We analyze how the candidates’ objectives, voters’ attention and media coverage, incumbency, the number of parties competing in the election, and the electoral environment influence how parties rank candidates.

Can democracies attract and elect politicians who are both high-ability and from diverse backgrounds? Using data covering the entire Danish population, including every candidate for local and national elections in 1990–2015, we explore the selection of political candidates. We show that Danish candidates and elected politicians have higher ability than the voters they represent, that selection on ability reflects individual skills rather than social background, that politicians are selected from economically diverse backgrounds, and that there is no substantial trade-off between ability and representation. Furthermore, we utilize a major structural reform, which significantly reduced the number of municipalities in Denmark, to show that increased political competition did not affect politicians’ ability, economic background, or the trade-off between the two.

We revisit Arunachalam and Watson's contention that a person's physical height may be used as instrument for income because it affects economic well-being solely by causing more conservative political preferences among people who are taller. To evaluate whether other early-life and genetic factors might serve as mechanisms connecting height and political preferences, we analyze a unique data source that includes political, economic, and demographic data on same-gender siblings. Models that include fixed effects for siblings provide a strong test of the Arunachalam and Watson thesis. We find that height is not a consistent predictor of political preferences once shared sibling characteristics are controlled in this way, raising doubt about whether height can in fact be used as an instrument for income.

Economic sanctions are a policy tool that great powers frequently use to interfere with domestic politics of another state. Regime change has been a primary goal of economic sanctions over the past decades. This article studies the relationship between leader-contingent sanctions—sanctions that are designed to impede the flow of revenue to a specific leader—and violent political conflict in target countries. I build a theoretical model to illuminate two mechanisms by which leader-contingent sanctions destabilize a regime—the Depletion Mechanism and the Instigation Mechanism. The Depletion Mechanism works when sanctions mechanically deplete the government's resources so that it becomes unable to buy off domestic opposition even by making the largest possible offer. The Instigation Mechanism implies that as sanctions decrease the benefit of negotiated settlement relative to war, the government may strategically choose to repress rather than buy off the opposition even when it is able to do so. Leader-contingent sanctions lead to bargaining failure by rewarding the opposition for revolt while reducing the government's ability and willingness to appease the opposition.

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.

Do policy priorities that candidates emphasize during election campaigns predict their subsequent legislative activities? We study this question by assembling novel data on legislative leadership posts held by Japanese politicians and using a fine-tuned transformer-based machine learning model to classify policy areas in over 46,900 statements from 1270 candidate manifestos across five elections. We find that a higher emphasis on a policy issue increases the probability of securing a legislative post in the same area. This relationship remains consistent across multiple elections and persists even when accounting for candidates' previous legislative leadership roles. We also discover greater congruence in distributive policy areas. Our findings indicate that campaigns provide meaningful signals of policy priorities.

Various robust communication effects have been identified, but evidence is overwhelmingly based on artificial survey treatments with limited real-world insight. I conducted a natural experiment on the impact of the European–Turkey statement closing the Balkan route during the 2015/16 European refugee crisis in Germany. This design tests the lasting effect of the statement's framing on public sentiment. I identify treatment and control groups based on timing to demonstrate its effect on perceptions of the crisis, asylum attitudes, and policy preferences. Effects are largest immediately following the announcement but decline rapidly. This shows political communication can significantly change opinion within a limited time frame. This study enhances our understanding of real-world communication effects and offers a broadly applicable methodology.

Technological change often increases demand for high-skilled jobs, with low-skilled losers turning to the populist right in response. The political effects of technological change that increases demand for low-skilled workers are largely unknown. The growth of the salmon fish-farming industry in rural Norway improved the labor-market situation for low-skilled workers, and we find that support for the populist right-wing party increased in municipalities that benefitted from the industry growth. The electoral change is due to a right-wing shift on the economic, but not the cultural dimension. Our results support political economy frameworks that point to lower demand for state interventions after positive labor market shocks, but raise the question of in what contexts support for populism will decline.

Deceiving citizens is typically considered the main political motive behind the spread of fake news. Accordingly, strategies to debunk fake news, such as fact-checking, have been suggested to combat it. However, the spread of fake news persists despite these debunking strategies. We propose an alternative but underexplored motive behind the spread of fake news: Fake news aims not only to deceive citizens but also to induce media skepticism. To support our claim, we present a stylized formal model of media skepticism and demonstrate that the incentive to spread fake news persists even if citizens are not deceived by disinformation coming from fake news. Our model highlights the dilemma embedded in fact-checking.

Policymakers and scholars have long proposed that willingness to participate in armed conflict is influenced by citizens' income-earning opportunities. Testing this opportunity cost mechanism has led to mixed results. One reason for this might be the fact that current proxies can also serve as indicators to test grievance-based theories. In this study, we construct a more suitable measure. We use crop calendars and crop location data to build an index of agricultural idle time for first administration units on the African continent from 1990 to 2017. We test the explanatory power of this measure by examining its relationship with armed conflict. Our results show that agricultural idle time increases the probability of observing armed conflict by more than 20 percent.

With the increasing nationalization of politics, federal politicians have interacted more and more with subnational actors. In particular, the president and governing party have provided selective policy and spending benefits to same-party jurisdictions in order to increase their influence in subnational politics. As a significant amount of federal grants is allocated directly to city governments, we analyze the effects of the federal-city relationship in the federal grant process. Specifically, we examine the effects of the president–mayor party alignment on the allocation of federal block and project grants to 568 medium and large cities from 2005 to 2020 using a two-way (city and year) fixed-effects model. We find that the president favors co-partisan mayors in the distribution of federal grants, specifically co-partisan mayors from (a) secure party cities, (b) cities in states where the governor is also a co-partisan, and (c) secure party cities in states where the governor is also a co-partisan. Digging deeper, we find this form of presidential particularism is almost exclusively a Democratic pursuit.

American politics scholarship has relied extensively on self-reported measures of ideology. We evaluate these widely used measures through an original national survey. Descriptively, we show that Americans’ understandings of “liberal” and “conservative” are weakly aligned with conventional definitions of these terms and that such understandings are heterogeneous across social groups, casting doubt on the construct validity and measurement equivalence of ideological self-placements. Experimentally, we randomly assign one of three measures of ideology to each respondent: (1) the standard ANES question, (2) a version that adds definitions of “liberal” and “conservative,” and (3) a version that keeps these definitions but removes ideological labels from the question. We find that the third measure, which helps to isolate symbolic ideology from operational ideology, shifts self-reported ideology in important ways: Democrats become more conservative, and Republicans more liberal. These findings offer first-cut experimental evidence on the limitations of self-reported ideology as a measure of operational ideology, and contribute to ongoing debates about the use of ideological self-placements in American politics.

Does providing information about the costs and benefits of automation affect the perceived fairness of a firm's decision to automate or support for government policies addressing automation's labor market consequences? To answer these questions, we use data from vignette and conjoint experiments across four advanced economies (Australia, Canada, the UK, and the US). Our results show that despite people's relatively fixed policy preferences, their evaluation of the fairness of automation—and therefore potentially the issue's political salience—is sensitive to information about its trade-offs, especially information about price changes attributable to automated labor. This suggests that the political impact of automation may depend on how it is framed by the media and political actors.

Hyperparameters critically influence how well machine learning models perform on unseen, out-of-sample data. Systematically comparing the performance of different hyperparameter settings will often go a long way in building confidence about a model's performance. However, analyzing 64 machine learning related manuscripts published in three leading political science journals (APSR, PA, and PSRM) between 2016 and 2021, we find that only 13 publications (20.31 percent) report the hyperparameters and also how they tuned them in either the paper or the appendix. We illustrate the dangers of cursory attention to model and tuning transparency in comparing machine learning models’ capability to predict electoral violence from tweets. The tuning of hyperparameters and their documentation should become a standard component of robustness checks for machine learning models.

When surveyed, clear majorities express concern about inequality and view the government as responsible for addressing it. Scholars often interpret this view as popular support for redistribution. We question this interpretation, contending that many people have little grasp of what reducing inequality actually entails, and that this disconnect masks important variation in preferences over concrete policies. Using original survey and experimental US data, we provide systematic evidence in line with these conjectures. Furthermore, when asked about more concrete redistributive measures, support for government action changes significantly and aligns more closely with people's self-interest. These findings have implications for how egalitarian policies can be effectively communicated to the public, as well as methodological implications for the study of preferences on redistribution.

This research note builds upon a number of important articles published in a variety of outlets concerning the population ecology of interest groups. Importantly, Lowery and Gray (1995), Nownes (2004), and Nownes and Lipinski (2005) empirically demonstrated the dependence on the density of pre-existing, similar groups when predicting new group formations. In this letter, I add to this research by modeling the density of ideologically divergent reproductive rights groups as well as offer supporting evidence for the popular Energy-Stability-Area model. The former is a novel consideration in the field of population ecology which primarily examines ideologically similar groups. I show that density dependence is at play among these polarized groups. I also provide insight into counter-mobilization movements of group formation by empirically demonstrating which groups are initial movers versus reactionary formers. In doing so, I raise important questions for researchers concerned with the emergence, longevity, and impact of interest groups over long periods of time. Finally, this research provides some insight into the expectations of group formation behavior in light of the landmark Dobbs decision.

The influence of congressional primary elections on candidate positioning remains disputed and poorly understood. We test whether candidates communicate artificially “extreme” positions during the nomination, as revealed by moderation following a primary defeat. We apply a scaling method based on candidates language on Twitter to estimate positions of 988 candidates in contested US House of Representatives primaries in 2020 over time, demonstrating validity against NOMINATE (r > 0.93) where possible. Losing Democratic candidates moderated significantly after their primary defeat, indicating strategic position-taking for perceived electoral benefit, where the nomination contest induced artificially “extreme” communication. We find no such effect among Republicans. These findings have implications for candidate strategy in two-stage elections and provide further evidence of elite partisan asymmetry.

Anemic demand for local news has contributed to an industry crisis. We consider whether local elections, which highlight the ability of local television stations and newspapers to provide information that is unavailable from national news outlets, increase local media use. While we show these elections are a time of increased attention to local politics in the news and among the public, we also find local media outlets do not benefit from this when considering behavioral news use measures. Relative to news outlets in cities without an election, local television remains substantively unchanged during local elections. Newspaper website traffic is largely stable, although it falls slightly the month after an election. In both cases these differences are small, even when considering close races and those happening off the federal election cycle. This shows limits on the ability of salient local political events to motivate local news use.