Suchergebnisse

Multilevel regression and post-stratification (MRP) is the current gold standard for extrapolating opinion data from nationally representative surveys to smaller geographic units. However, innovations in nonparametric regularization methods can further improve the researcher's ability to extrapolate opinion data to a geographic unit of interest. I test an ensemble of regularization algorithms and find that there is room for substantial improvement on the multilevel model via more flexible methods of regularization. I propose a modified version of MRP that replaces the multilevel model with a nonparametric approach called Bayesian additive regression trees (BART or, when combined with post-stratification, BARP). I compare both methods across a number of data contexts, demonstrating the benefits of applying more powerful regularization methods to extrapolate opinion data to target geographical units. I provide an R package that implements the BARP method.

AbstractThe link between objective facts and politically relevant beliefs is an essential mechanism for democratic accountability. Yet the bulk of empirical work on this topic measures objective facts at whatever geographic units are readily available. We investigate the implications of these largely arbitrary choices for predicting individual-level opinions. We show that varying the geographic resolution—namely aggregating economic data to different geographic units—influences the strength of the relationship between economic evaluations and local economic conditions. Finding that unemployment claims are the best predictor of economic evaluations, especially when aggregated at the commuting zone or media market level, we underscore the importance of the modifiable areal unit problem. Our methods provide an example of how applied scholars might investigate the importance of geography in their own research going forward.

The relationship between anxiety and investor behavior is well known enough to warrant its own aphorism: a "flight to safety." We posit that anxiety alters the intensity of voters' preference for the status quo, inducing a political flight to safety toward establishment candidates. Leveraging the outbreak of the novel coronavirus during the Democratic primary election of 2020, we identify a causal effect of the outbreak on voting, with Biden benefiting between 7 and 15 percentage points at Sanders's expense. A survey experiment in which participants exposed to an anxiety-inducing prompt choose the less disruptive hypothetical candidate provides further evidence of our theorized flight to safety among US-based respondents. Evidence from 2020 French municipal and US House primary elections suggests a COVID-induced flight to safety generalizes to benefit mainstream candidates across a variety of settings. Our findings suggest an as-yet underappreciated preference for "safe" candidates in times of anxiety.

AbstractIndividuals with heightened labor market insecurity express more protectionist, xenophobic, and isolationist sentiment. We construct a novel measure of labor market insecurity that combines an individual's industry‐based exposure to import competition with an occupation‐based measure of job immobility. Immobility captures the similarity of an individual's job to others in the economy, weighted by their prevalence. The holder of a job that is dissimilar to others in the industry or in the state experiences more anxiety regarding their labor market prospects in the face of a globalization shock, and is more likely to express antiglobalization sentiment.

To answer questions about the origins and outcomes of collective action, political scientists increasingly turn to datasets with social network information culled from online sources. However, a fundamental question of external validity remains untested: are the relationships measured between a person and her online peers informative of the kind of offline, "real-world" relationships to which network theories typically speak? This article offers the first direct comparison of the nature and consequences of online and offline social ties, using data collected via a novel network elicitation technique in an experimental setting. We document strong, robust similarity between online and offline relationships. This parity is not driven by sharedidentityof online and offline ties, but a shared nature of relationships in both domains. Our results affirm that online social tie data offer great promise for testing long-standing theories in the social sciences about the role of social networks.

Knowledge creation is a social enterprise, especially in political science. Sharing new findings widely and quickly is essential for progress. Scholars can now use Twitter to rapidly disseminate ideas, and many do. What are the implications of this new tool? Who uses it, how do they use it, and what are the implications for exacerbating or ameliorating existing inequalities in terms of research dissemination and attention? We construct a novel dataset of all 1,236 political science professors at PhD-granting institutions in the United States who have a Twitter account to answer these questions. We find that female scholars and those on the tenure track are more likely to use Twitter, especially for the dissemination of research. However, we consistently find that research by men shared on Twitter is more likely to be passed along further by men than research by women.

Abstract
Large language models (LLMs) offer new research possibilities for social scientists, but their potential as "synthetic data" is still largely unknown. In this paper, we investigate how accurately the popular LLM ChatGPT can recover public opinion, prompting the LLM to adopt different "personas" and then provide feeling thermometer scores for 11 sociopolitical groups. The average scores generated by ChatGPT correspond closely to the averages in our baseline survey, the 2016–2020 American National Election Study (ANES). Nevertheless, sampling by ChatGPT is not reliable for statistical inference: there is less variation in responses than in the real surveys, and regression coefficients often differ significantly from equivalent estimates obtained using ANES data. We also document how the distribution of synthetic responses varies with minor changes in prompt wording, and we show how the same prompt yields significantly different results over a 3-month period. Altogether, our findings raise serious concerns about the quality, reliability, and reproducibility of synthetic survey data generated by LLMs.