While Amazon's Mechanical Turk (MTurk) has reduced the cost of collecting original data, in 2018, researchers noted the potential existence of a large number of bad actors on the platform. To evaluate data quality on MTurk, we fielded three surveys between 2018 and 2020. While we find no evidence of a “bot epidemic,” significant portions of the data—between 25 and 35 percent—are of dubious quality. While the number of IP addresses that completed the survey multiple times or circumvented location requirements fell almost 50 percent over time, suspicious IP addresses are more prevalent on MTurk than on other platforms. Furthermore, many respondents appear to respond humorously or insincerely, and this behavior increased over 200 percent from 2018 to 2020. Importantly, these low-quality responses attenuate observed treatment effects by magnitudes ranging from approximately 10 to 30 percent.

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.

What impact do party leaflets and canvass visits have on voter turnout? Get Out The Vote (GOTV) experiments consistently find that campaigning needs to be personal in order to be effective. However, the imbalance between United States and European-based studies has led to recent calls for further European GOTV experiments. There are also comparatively few partisan experiments. I report the findings of a United Kingdom-based field experiment conducted with the Liberal Democrats in 2017. Results show that party leaflets boost turnout by 4.3 percentage points, while canvassing has a small additional effect (0.6 percentage points). The study also represents the first individual level experiment to compare GOTV effects between postal voters and in-person voters outside the United States.

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.

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.

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.

The revolving door is a potential mechanism of private influence over policy. Recent work primarily examines the revolving of legislators and their staff, with little focus on the federal bureaucracy. To analyze decisions to turnover into lobbying, we develop an argument emphasizing the (1) policy expertise acquired from federal employment; (2) the proximity of employees to political decision-making; and (3) the agency policymaking environment. Leveraging federal personnel and lobbying data, we find the first two factors predict revolving whereas the policymaking environment has an inconsistent impact. We highlight the importance of studying selection into lobbying for estimating casual effects of lobbyist characteristics on revenue and contribute to the literature on bureaucratic careers and the nature of private influence in policymaking.

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.

Social connections between individuals can profoundly impact their political behavior. A growing body of research on legislative politics examines how spatial proximity to fellow legislators affects voting behavior within the institution. However, studies that examine this question often suffer from a fundamental identification problem in which proximity effects may reflect actual behavioral diffusion between members or, instead, homophily, in which legislators of a similar political feather flock together. We overcome this observational equivalence by exploiting a unique random seating lottery for seating assignments in the world's oldest existing parliament, Iceland's national legislature, Alþingi. Utilizing this naturally occurring randomization, we employ spatial analyses of more than 20,000 estimates of spatial dependence and find little evidence that seating proximity leads to similar voting behavior by members in this legislative context.

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.

We estimate how asset prices respond to a range of political shocks, including changes in a country's economic stewardship, national elections, coup d'états, wars, and terrorist attacks. Using an event study approach and daily prices from the Buenos Aires exchange (Argentina) between 1967 and 2020, we find that stock-market volatility increases in the days immediately following major policy-shifting events. These results hold irrespective of whether returns are measured in nominal terms, in local consumption units, or in US dollars. The most significant increase in post-event risk is associated with irregular government turnovers. Volatility also increases in the days immediately following a defeat in an international war, national elections, and changes in the country's economic stewardship. No changes in stock-market volatility occur, however, after terrorist attacks or when the date of a new administration's inauguration is publicly known and determined sufficiently far in advance.

Across a series of experiments, I show that racial threat from a stereotypically nonthreatening racial minority group, Asian Americans, has a direct impact on white Americans' views of discrimination toward the group. When white Americans learn the group is growing, they feel a distinct racial threat which decreases support for the idea that Asian Americans experience discrimination while simultaneously increasing support for policy which actively discriminates against Asian Americans. I show this concept to be portable over context, examining support for discriminatory policy toward Asians in education policy and COVID-19 policy. I conclude by discussing the implications for how racial threat can drive more racial conflict while simultaneously decreasing the perception that this discrimination is occurring.

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.

Studies of political persuasion often use an exogenous encouragement as an instrument for persuasive messaging. However, for some people, such encouragement is insufficient, while for others, it is unnecessary. These individuals are excluded from methods that only estimate a treatment effect among compliers. Using the marginal treatment effect framework, we extend research finding that exposure to West German television increases support for communism. We find that, because of self-selection, for those who watch West German TV regardless of signal quality, i.e. always-takers, cutting off West German television would have increased support for communism. Our extrapolation shows that media choices reinforce, rather than mollify, political preferences.

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.

Civic associations are expected to foster civic, pro-social behavior, but this optimistic view is increasingly contested. We argue that populist radical right parties can strategically target and infiltrate associations to diffuse anti-establishment rhetoric and anti-democratic attitudes. We illustrate this phenomenon by examining the relationship between civic associations and compliance with government rules during Germany's first Covid-19 lockdown with a difference-in-differences design. Results show that areas with denser sport, nature, and culture clubs recorded higher mobility under lockdown. We document the infiltration mechanism and the spreading of anti-democratic attitudes within associations, using survey and election data and qualitative evidence including interviews. In doing so, we shed light on a negative effect of social networks and an understudied strategy of challenger populist parties.

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.

We investigate the American public's attitudes over an integral component of judicial behavior: the legal principles judges employ when making decisions. Our theoretical perspective argues that political preferences shape individuals’ attitudes over how judges apply legal principles, mirroring ideological divisions expressed by political elites and judges. Using an original battery of questions, we find high support across all Americans for the use of certain, well-established legal principles, but stark differences in how liberals and conservatives evaluate the use of more controversial principles. In a survey experiment, we find that agreement (disagreement) between an individual's attitudes over the use of legal principles and the reasoning contained in a Supreme Court opinion is associated with increased (decreased) support for the Court decision.