Since the end of World War II, the US government has spent nearly $4 trillion on humanitarian, economic, and military assistance to other countries. Despite the myriad benefits of such programs, mass support has long been lacking. Here, I argue that low citizen trust in government can help us to understand why. Using cross-sectional and panel survey data from the United States, I find a positive and substantively significant relationship between political trust and support for government spending on foreign aid. Overall, these findings underscore the relevance of political trust and further illustrate the drivers of U.S. public opinion toward foreign aid, something that has implications for whether America should turn inward or continue its long-standing role of global leadership.

We demonstrate how few-shot prompts to large language models (LLMs) can be effectively applied to a wide range of text-as-data tasks in political science—including sentiment analysis, document scaling, and topic modeling. In a series of pre-registered analyses, this approach outperforms conventional supervised learning methods without the need for extensive data pre-processing or large sets of labeled training data. Performance is comparable to expert and crowd-coding methods at a fraction of the cost. We propose a set of best practices for adapting these models to social science measurement tasks, and develop an open-source software package for researchers.

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.

Inverse probability weighting is a common remedy for missing data issues, notably in causal inference. Despite its prevalence, practical application is prone to bias from propensity score model misspecification. Recently proposed methods try to rectify this by balancing some moments of covariates between the target and weighted groups. Yet, bias persists without knowledge of the true outcome model. Drawing inspiration from the quasi maximum likelihood estimation with misspecified statistical models, I propose an estimation method minimizing a distance between true and estimated weights with possibly misspecified models. This novel approach mitigates bias and controls mean squared error by minimizing their upper bounds. As an empirical application, it gives new insights into the study of foreign occupation and insurgency in France.

How well do existing survey instruments differentiate between opinions that affect individual behavior and opinions that don't? To answer this question, we randomly assigned U.S. respondents to one of three survey instruments: Likert items (Likert), Likert items followed by personal importance items (Likert+) and Quadratic Voting for Survey Research (QVSR), which gives respondents a fixed budget to buy “favor” or “oppose” votes, with the price for each vote increasing quadratically. We find that, relative to Likert, both Likert+ and QVSR better identify people who care enough about an issue to act in opinion-congruent ways, with QVSR offering the most consistent improvement overall. Building on these results, we show how conclusions regarding the relationship between policy opinions and self-interest can differ across measurement strategies.

Disseminating data is a core mission of international organizations. The Bretton Woods Institutions (BWIs), in particular, have become a main data source for research and policy-making. Due to their extensive lending activities, the BWIs often find themselves in a position to assist and pressure governments to increase the amount of economic data that they provide. In this study, we explore the association between loans from the BWIs and an index of economic transparency derived from the data-reporting practices of governments to the World Bank. Using a matching method for causal inference with panel data complemented by a multilevel regression analysis, we examine, separately, loan commitments and disbursements from the IMF and the World Bank. The multilevel regression analysis finds a significant association between BWI loans and the improvement of economic transparency in all developing countries; the matching method identifies a causal effect in democracies.

Among the greatest challenges facing scholars of public opinion are the potential biases associated with survey item nonresponse and preference falsification. This difficulty has led researchers to utilize nonresponse rates to gauge the degree of preference falsification across regimes. This article addresses the use of survey nonresponse rates to proxy for preference falsification. A simulation analysis exploring the expression of preferences under varying degrees of repression was conducted to examine the viability of using nonresponse rates to regime assessment questions. The simulation demonstrates that nonresponse rates to regime assessment questions and indices based on nonresponse rates are not viable proxies for preference falsification. An empirical examination of survey data supports the results of the simulation analysis.

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.

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.

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.

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.

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.

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.

An extensive theoretical and practitioner literature addresses the drivers and consequences of transformation of violent rebel actors during conflicts. However, measurement challenges constrain large-N empirical study of the effects and consequences of such transformations. This Research Note introduces a strategy to identify periods of transformation and change in the operation of non-state armed militant groups via computational text analysis of trends in reporting on activities. It presents the measurement approach and demonstrates scalability to a corpus of more than 200 militant groups operating from 1989 to 2020. The study concludes by extending a recent analysis of the impacts of uncertainty on conflict termination. An online Appendix demonstrates the advantages and drawbacks of the measurement through a series of case studies.

Although influential models of public opinion hold that group sentiments play an important role in shaping political beliefs, they often assume that group attitudes stem from socialization and are thus exogenous to politics. We challenge this assumption, arguing that group attitudes may themselves be the consequence of political views. Across three survey experiments that each uses a unique social group–issue pair, we consistently demonstrate that attitudes toward groups are influenced by information about the groups' policy views. These findings persist even when accounting for potential partisan signaling. Altogether, these results show that group sentiments should not be regarded as wholly exogenous to policy concerns and suggest that the use of group-based heuristics can be consistent with instrumental models of public opinion.

Whether a topic is seen in a moral or moral-emotional light has significant political implications. Yet, we lack knowledge about the process of moralization: Who defines the way topics are communicated about? Where prior research has investigated the relative power of different actors to place a topic on the agenda or shape opinions, we study who sets the moral and moral-emotional tone of debate. To do so, we zoom in on immigration discourse in Germany and analyze fine-grained social media data from politicians, political parties, newspapers, and members of the public over a period of more than four years. After employing a transformer model to identify moral and moral-emotional appeals, we use structural vector autoregression models to demonstrate the important role of radical-right challengers in shaping public discourse in a negative moral-emotional direction. The results inform theories of moralization and political entrepreneurship.

Concerns over free riding in NATO are widespread. An intuitive approach to analyzing free riding is treating it as a systematic pattern of spatial interdependence between the allies: how does a NATO member's defense spending react to changes in other allies' military expenditures? While recent work has found statistically significant free riding (negative spatial interdependence in the outcomes), it suffers from important limitations. First, this research does not adequately account for temporal dependence. Second, it does not quantify the effect of interest. Accounting directly for temporal dependence provides a meaningfully distinct perspective on the within-alliance dynamics, demonstrating that the spatiotemporal effect of free riding is, in fact, more substantial than its short-run effect, challenging inferences of static spatial models. We discuss the relevant practical and theoretical implications.