Most political systems consist of multiple layers. Yet datasets are predominantly situated at a single territorial tier, encouraging methodological nationalism, regionalism, and localism. We present three new integrated datasets that include electoral, institutional, ideological, and government composition data on the country and regional level (RD|CED, RED and RPSD). With this data, we cover 337 country elections on the regional level, 2,226 regional elections, and 2,825 regional cabinets in 365 regions of 21 countries from 1941 to 2019, accounting for 800 political parties and their ideological positions. Combined, these data complement and extend existing datasets and facilitate the study of political interaction across levels. Data are available at http://multi-level-cross-level-politics.eu/ or can be accessed through the Havard Dataverse repository. We conclude with an agenda for future cross-level studies.

Liberals experience more distress than conservatives. Why? We offer a novel explanation, the social support hypothesis. Maintaining social support and avoiding exclusion are basic human motivations, but people differ in their sensitivity to the threat of social exclusion. Among people high in the personality trait neuroticism, exclusion easily triggers feelings of vulnerability and neediness. The social support hypothesis translates this to politics. Concerned with their own vulnerability, we find that neurotic people prefer policies of care – social welfare and redistribution – but not other left-wing policies. Specifically, it is anxiety – the facet of neuroticism tapping sensitivity to social threats – that drives this link. And it is only for people experiencing exclusion that anxiety predicts support for social welfare. Our results come from two experiments and four representative surveys across two continents. They help to resolve the puzzle of liberal distress while providing a new template for research on personality and politics.

We use instruction-tuned large language models (LLMs) like GPT-4, Llama 3, MiXtral, or Aya to position political texts within policy and ideological spaces. We ask an LLM where a tweet or a sentence of a political text stands on the focal dimension and take the average of the LLM responses to position political actors such as US Senators, or longer texts such as UK party manifestos or EU policy speeches given in 10 different languages. The correlations between the position estimates obtained with the best LLMs and benchmarks based on text coding by experts, crowdworkers, or roll call votes exceed .90. This approach is generally more accurate than the positions obtained with supervised classifiers trained on large amounts of research data. Using instruction-tuned LLMs to position texts in policy and ideological spaces is fast, cost-efficient, reliable, and reproducible (in the case of open LLMs) even if the texts are short and written in different languages. We conclude with cautionary notes about the need for empirical validation.

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.

Anthony D. Smith, in one of his earlier, less debated, works – Nationalism in the 20th Century (1979) – examines phases of nationalism in the modern era, suggesting that nationalism has taken various forms before and during the 20th century. He argues that nationalism’s adaptability is at the core of its persistence, adapting to changing situations such as fascism and communism. As a result of this adaptability, nationalism still flourishes today. This article applies Smith’s theory to explore the interplay between cultural and material factors in the evolution of nationalism in Ireland. It identifies five ideological phases – revolutionary nationalist, protectionist, liberalising, neoliberal, and ecological – to which nationalism has adapted, and within which nationalism has influenced various aspects of Irish society. These phases are situated within a broader ideological and material context, analysing obliquely the Irish language (a core element of Irish nationalism), and related to changing processes of individualization.

I develop a survey method for estimating social influence over individual political expression, by combining the content-richness of document scaling with the flexibility of survey research. I introduce the “What Would You Say?” question, which measures self-reported usage of political catchphrases in a hypothetical social context, which I manipulate in a between-subjects experiment. Using Wordsticks, an ordinal item response theory model inspired by Wordfish, I estimate each respondent’s lexical ideology and outspokenness, scaling their political lexicon in a two-dimensional space. I then identify self-censorship and preference falsification as causal effects of social context on respondents’ outspokenness and lexical ideology, respectively. This improves upon existing survey measures of political expression: it avoids conflating expressive behavior with populist attitudes, it defines preference falsification in terms of code-switching, and it moves beyond trait measures of self-censorship, to characterize relative shifts in the content of expression between different contexts. I validate the method and present experiments demonstrating its application to contemporary concerns about self-censorship and polarization, and I conclude by discussing its interpretation and future uses.

Welfare state attitudes make up an interactive feedback loop of defining popular legitimacy and future policy trajectories. Understanding attitudinal drivers is thus essential political knowledge. However, as existing research is mainly based on the work-nexus of welfare, this article expands the literature to the welfare state’s care-nexus, examining drivers of family policy attitudes. We argue that conventional attitude predictors of self-interest and ideology are insufficient to explain the attitudinal cleavage in family policy. Instead, justice perceptions in the division of physical and cognitive household labour represent an important normative battleground. We test this with Norwegian survey data (N = 3500), using a unique vignette experiment to operationalise justice perceptions. Findings show that individuals who do not perceive a disproportional household labour division as unfair prefer optional familialism within family policy. Individuals who do perceive unfairness in a disproportional household labour division prefer de-familialism, which facilitates gender equality in public and private spheres. This is consistently found for the physical division of labour, while the cognitive dimension seems less politicised. We conclude that the battleground for different family policy approaches is fundamentally normative and linked to justice considerations on gender roles.

This article examines the information sharing behavior of U.S. politicians and the mass public by mapping the ideological sharing space of political news on social media. As data, we use the near-universal currency of online information exchange: web links. We introduce a methodological approach and software to unify the measurement of ideology across social media platforms by using sharing data to jointly estimate the ideology of news media organizations, politicians, and the mass public. Empirically, we show that (1) politicians who share ideologically polarized content share, by far, the most political news and commentary and (2) that the less competitive elections are, the more likely politicians are to share polarized information. These results demonstrate that news and commentary shared by politicians come from a highly unrepresentative set of ideologically extreme legislators and that decreases in election pressures (e.g., by gerrymandering) may encourage polarized sharing behavior.