In a time of unprecedented displacement, hostility toward refugees is widespread. Two common strategies refugee advocates pursue to counter hostility and promote inclusion are perspective-getting exercises and providing information that corrects misperceptions. In this study, we evaluate whether these strategies are effective across four outcomes commonly used to measure outgroup inclusion: warmth toward refugees, policy preferences, behavior, and beliefs about a common misperception concerning refugees. Using three studies with nearly 15,000 Americans, we find that information and perspective-getting affect different outcomes. We show that combining both interventions produces an additive effect on all outcomes, that neither strategy enhances the other, but that bundling the strategies may prevent backfire effects. Our results underscore the promise and limits of both strategies for promoting inclusion.

Our understanding of politics often relies on the ideological placement of political actors—ranging from scaling legislative roll-call voting in the United States to text-based classifications of political parties in Europe. A particularly thorny problem remains estimating individual positions in legislatures with strong partisan discipline. We improve upon recently developed measurement strategies and propose a novel approach for estimating legislators’ ideological positions: an expert survey in which respondents compare pairs of representatives on a left-right dimension. The innovation of our approach lies in the combination of four particular features. First, we rely on political youth leaders who are insightful and easy to recruit. Second, the rating task does not involve numeric scaling and consists of simple pairwise comparisons. Third, we efficiently and automatically detect informative comparisons to reduce the cost and length of the survey without compromising our estimates. Fourth, we use a Bayesian Davidson model with random effects to generate an ideological position for each legislator. As an empirical illustration, we estimate the placement of the 709 members of the 19th German Bundestag. Several validity tests show that our model captures variation within and across political parties. Our estimates offer a thorough benchmark to validate alternative measurement strategies. The presented measurement strategy is flexible and easily extendable to diverse political settings because it can capture comparisons among political actors across time and space.

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.

Are people's priorities associated with their income and education levels? There is a long history in political science of claims that priorities are driven by economic interests, but also that low-income and low-education people fail to prioritize their economic interests. In this paper we use measures of revealed importance from [Sides J, Tausanovitch C and Vavreck L (2023) The Bitter End: The 2020 Presidential Campaign and the hallenge to American Democracy. Princeton University Press.] to evaluate the priorities of high- and low-income/education voters with respect to 44 different policies. It is well known that there are substantial differences in the preferences of people with lower incomes or education levels and people with higher incomes or education levels, but conditional on preferences we find very small differences among education and income groups in terms of priorities. Like high-income and high-education voters, lower-income and education voters care most about the major issues of the day. They do not care systematically more or less than other voters about policies that expand social welfare, redistribution, or labor rights.

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.

The remuneration of MPs affects who engages in politics. Even if average returns to office are positive, as found in all other studies, some officeholders’ returns are likely negative. Further, the timing of returns to office is crucial as politicians often enjoy delayed compensation like lucrative pensions. Utilizing administrative data for parliamentary candidates in Denmark from 1994 to 2015, we estimate first-time runners’ earnings and total income returns to office. Based on total income, practically all elected MPs experience economic gains during their first term. Computations of life-cycle returns reveal that the 25 percent highest earning candidates (pre-office) have no long-term economic gain from winning. Generally, considering the distribution and timing of returns to office improves studies of how office-holding is economically rewarded.

Do citizens evaluate quota and non-quota politicians similarly? Do voters perceive quota-elected politicians as less competent and autonomous? Despite the proliferation of gender quotas, it remains unclear how citizens view female quota politicians compared to their non-quota male and female counterparts. This is particularly important in electoral systems where competitive and reserved seats both exist. We conducted a vignette experiment in Morocco where we randomized information about how politicians were elected and their legislative behavior. We find no evidence that voters evaluate quota women as less competent than non-quota male and female politicians. Our data demonstrate partial support for the argument that quota women are perceived as pawns of their parties, although information about legislative behavior can mitigate this perception.

In this study, we examine the interactions among elections by considering two parties competing in simultaneous elections, where each party fields candidates in every election. We model these interactions through voters’ utility functions, influenced not just by the policies proposed in a specific election but also by those in concurrent elections. The benchmark result indicates that all candidates adopt positions more polarized than those predicted by the Calvert–Wittman model. This shift occurs because candidates leverage the policy stance of their counterparts in co-partisan elections to pursue policies more aligned with their preferences. In multidistrict elections, candidates from areas with strong ideological leanings deviate further from the median voter's stance at the national level. Moreover, this interaction effect may select candidates with more extreme ideology into the local elections.

There are two prominent but seemingly contradictory symbols of how Asians are racialized domestically within the United States: “yellow peril” and “model minority.” How do these two racial tropes relate to each other? What effects do they have on the formation of support for race-targeted public policy? In this paper, we propose and empirically test that racialized resentment toward Asian Americans and the congratulatory framing of them as a model minority are both salient in the minds of the American public, reflecting the complexity of prejudices toward Asians in American society. Utilizing two original survey-based measures of anti-Asian resentment and the model minority stereotype, we empirically demonstrate the interconnection between the two racial tropes and highlight the key demographic and dispositional correlates of these multi-faceted contemporary racial attitudes toward Asian Americans. We then show that the two racial tropes, both independently and by interacting with each other, significantly shape racial public policy preferences in the United States.

The Russian invasion of Ukraine in February 2022 has had profound effects on the stability and security of Europe. This study examines the attitudes of Europeans toward the European Union (EU) in the aftermath of the invasion of Ukraine. Using Special Eurobarometer data collected between February and April 2022 with a representative sample of the EU (N = 26,502), it leverages the quasi-experimental setting with the coincidence between the timing of the invasion and the fieldwork period of the Eurobarometer. Our findings indicate a general increase in support for the EU in the aftermath of the invasion by 4 percentage-points (11 percent of a SD). While the amplitude of the effect remains similar, we see larger treatment effects as more days passed after the invasion. We also observe significant variation at the individual level in treatment effects, particularly by ideology, with left-leaning individuals being more critical of the EU following the invasion. In general, our research demonstrates the significant impact of regional conflicts on public attitudes toward supranational organizations such as the EU and highlights the role of the EU as a provider of security and stability in the face of such conflicts.

An unresolved aspect of women's underrepresentation in politics is the media portrayal of female candidates. This paper studies how advocacy campaigns may affect potential bias, leveraging the 2019 Swiss federal elections, which were shaped by two nation-wide, cross-party campaigns advocating for gender equality. The empirical analysis compares the 2015 and 2019 election campaigns, relying on an original dataset of the mentions that all candidates (over 3,700 respectively 4,600) received in over 2.2 million news articles. The analysis produces three main results. First, although in both elections male candidates received more media attention than female candidates did, the gender gap was significantly smaller in 2019 than in 2015. Second, in both elections, male and female candidates tended to be mentioned in conjunction with gender-stereotypical topics. Third, the gender gap in media attention before and after a key women's rights event was similar to that between the corresponding periods in 2015. These findings suggest that the differences observed between 2015 and 2019 are linked to the political campaign at large rather than to a specific event, despite its historical dimensions. The results contribute to the understanding of how advocacy campaigns can change bias in media coverage and, methodologically, to measuring and understanding gendered media coverage of politics.

Mounting U.S. research suggests many non-White individuals feel solidarity with, and identify as, people of color (PoC). Yet measurement limitations prevent scholars from testing whether these constructs are empirically different. We explain why these concepts diverge and evaluate our claims with an expanded battery of measures across U.S. Asian, Black, Latino, and Multiracial adults (N = 3402). Using multi-group confirmatory factor analysis, we show these items capture related but distinct concepts among PoC (configural invariance). We then establish that these items uniformly measure each construct across PoC groups (metric invariance), with mean level differences validly reflecting actual heterogeneity between groups, rather than measurement artifacts (scalar invariance). Finally, consistent with our conceptualization, we show that solidarity among PoC mediates the association between PoC identification and support for policies that implicate various communities of color. We end with practical advice for using these items in surveys of racially diverse populations.

How does a candidate's racial background affect the inferences voters make about them? Prior work finds that Black candidates are perceived to be more liberal. Using two survey experiments, we test whether this effect persists when candidate partisanship and issue positions are specified and also consider other consequential voter perceptions. We make two contributions. First, we show that while Black candidates are perceived to be more liberal than White candidates with the same policy positions, this difference is smaller for Black candidates who adopt more conservative positions on race-related issues. Second, we find that voters, both Black and White, believe Black candidates will prioritize the interests of Black constituents over those of White constituents, regardless of candidate positions.

Scholars disagree as to whether Americans’ attitudes toward local issues are structured ideologically and whether these are related to national policy ideology. We use two surveys of American adults to assess whether and to what extent Americans' local policy attitudes exhibit a similar structure as do national policy attitudes. We find that items asking about local policy are just as likely to reflect a latent dimension of policy preferences as those asking about national policy. Additionally, when local and national items are scaled separately, those scales are highly correlated. Our findings indicate that attitudes toward many local issues are aligned with national ideology. A smaller subset of attitudes about local issues appears distinctively local and possibly structured by non-ideological cleavages.

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.

Networks among legislators shape politics and policymaking within legislative institutions. In past work on legislative networks, the ties between legislators have been defined on those who serve in the same legislature or chamber. Online information networks, which have been found to play important roles in legislative communication at the national level, are not bounded by individual legislative bodies. We collect original data for over four thousand US state legislators and study patterns of connection among them on Twitter. We look at three types of Twitter networks—follower, retweets, and mentions. We describe these networks and estimate the relationships between ties and salient attributes of legislators. We find that networks are organized largely along geographic and partisan lines and that identity attributes—namely gender and race—exhibit strong associations with the formation of ties.

Scholars studying organizations often work with multiple datasets lacking shared identifiers or covariates. In such situations, researchers usually use approximate string (“fuzzy”) matching methods to combine datasets. String matching, although useful, faces fundamental challenges. Even where two strings appear similar to humans, fuzzy matching often struggles because it fails to adapt to the informativeness of the character combinations. In response, a number of machine learning methods have been developed to refine string matching. Yet, the effectiveness of these methods is limited by the size and diversity of training data. This paper introduces data from a prominent employment networking site (LinkedIn) as a massive training corpus to address these limitations. By leveraging information from the LinkedIn corpus regarding organizational name-to-name links, we incorporate trillions of name pair examples into various methods to enhance existing matching benchmarks and performance by explicitly maximizing match probabilities. We also show how relationships between organization names can be modeled using a network representation of the LinkedIn data. In illustrative merging tasks involving lobbying firms, we document improvements when using the LinkedIn corpus in matching calibration and make all data and methods open source.

Ecological inference is a statistical technique used to infer individual behavior from aggregate data. A particularly relevant instance of ecological inference involves the estimation of the inner cells of a set of R × C related contingency tables when only their aggregate margins are known. This problem spans multiple disciplines, including quantitative history, epidemiology, political science, marketing, and sociology. This paper proposes new models for solving the problem using the latent structure theory, and presents the ecolRxC package, an R implementation of this methodology. This article exemplifies, explains, and statistically documents the new extensions and, using real inner cell election data, shows how the new models in ecolRxC lead to significantly more accurate solutions than ecol and VTR, two Stata routines suggested within this framework. ecolRxC also holds its own against ei.MD.bayes and nslphom, the two algorithms currently identified in the literature as the most accurate to solve this problem. ecolRxC records accuracies as good as those reported for ei.MD.bayes and nslphom. Besides, from a theoretical perspective, ecolRxC stands up for modeling a causal theory of political behavior to build its algorithm. This distinguishes it from other procedures proposed from different frameworks (such as ei.MD.bayes and nslphom) which model expected behaviors, instead of modeling how voters make choices based on their underlying preferences as ecolRxC does.

In this article, we explore the rhetorical space structuring the debates in the United Nations General Assembly's (UNGA) Committee on Disarmament and International Security. To this end, we unfold states' speeches by combining three established methods. First, we estimate terms' relevance for latent topics structuring the debates with structural topic modeling. Second, we estimate topic-specific positions based on the Wordfish algorithm. Third, we map these positions onto a lower-dimensional rhetorical space using principal component analysis. We identify two latent conflicts. First, a debate over conventional weapons with states emphasizing security interests on the one end and humanitarian interests on the other. Second, a conflict over weapons of mass destruction that divides defenders and challengers of the status quo.

Lobbying expenditures are widely used as a proxy variable for measuring lobbying activity. However, the validity of this approach has rarely been examined and existing justifications do not account for heterogeneity in expenditure formats across types of lobbyists. I address the question using unique lobbying disclosure data from Wisconsin, where lobbying organizations report both expenditures on lobbying-related activities and lobbyists’ hours worked. Strong overall correlations between changes in expenditures and hours worked within organization–lobbyist dyads indicate that lobbying expenditures can serve as a reasonable proxy. However, caution is warranted due to substantial heterogeneity, with contract lobbyist relationships exhibiting weaker correlations than in-house relationships. I conclude by providing suggestions to improve empirical analyses that rely on lobbying expenditures.