Abstract Annotated text data are central to many empirical applications in political science, yet many annotation tasks involve categories that are sparsely populated. These low- N categories pose problems for both encoder-only and decoder-only (generative) Transformer large language models (LLMs), leading to low performance, biased evaluation and measurement error in downstream analyses. This article introduces the synthetic imputation approach, a targeted method for expanding sparse categories while remaining consistent with the category-defining content of the human-coded examples. The approach uses a generative LLM to produce additional texts for underrepresented categories by conditioning on small, rotating subsets of human-coded examples. Rather than paraphrasing or replacing the original data, synthetic imputation adds informative variation only where data are thin. I evaluate the approach using both encoder-only and decoder-only LLM classifiers across multiple political science applications. The results show that synthetic imputation substantially improves performance and evaluation stability in sparse settings, recovering full-sample performance once a modest number of true observations is available, with limited and predictable overfitting at very low levels of N .
Abstract Political parties in advanced democracies face a dual challenge: fewer citizens are willing to volunteer, and those who remain active are disproportionately older and male. This is troubling given the central role that volunteers play in campaigns and party processes. Whether party leaders can recruit a larger and more diverse activist base remains an open question. We partnered with a major party in the Netherlands and conducted two field experiments before the 2021 general and 2022 municipal elections, targeting over 35,000 party members. The party randomized whether members received a personal call, an email or both. Results show that calls—where members could voice their opinions—increased volunteer sign-up rates (83% in Study 1 and 361% in Study 2), especially among younger members. By contrast, emails had no effect. These findings clarify mixed evidence in the literature, suggesting that voice opportunities are key to expanding and diversifying the activist base.
Abstract Elections are central to the study of politics. When studying parties’ vote shares across districts, scholars are encouraged to use compositional-outcome models in order to test their theories about what factors shape the dynamics of electoral support. Existing compositional modeling approaches incorrectly deal with scenarios in which not all parties compete in every electoral district. Because unobserved factors that affect a party’s decisions to contest a district are likely correlated with its performance in districts where the party fielded candidates, failing to account for partial contestation is likely to result in sample selection bias. Addressing sample selection in a compositional setting is challenging because the outcomes are in log-ratio form, and thus the errors often deviate from normality. To deal with these issues, we introduce a novel maximum likelihood approach which accounts for this type of sample selection and demonstrate through simulations that our method outperforms commonly used solutions, including the conventional Heckman correction. We illustrate the utility of our approach by analyzing the 2017 and 2019 U.K. parliamentary elections in English constituencies.
Abstract Scholars frequently use covariate balance tests to test the validity of natural experiments and related designs. Unfortunately, when measured covariates are unrelated to potential outcomes, balance is uninformative about key identification conditions. We show that balance tests can then lead to erroneous conclusions. To build stronger tests, researchers should identify covariates that are jointly predictive of potential outcomes; formally measure and report covariate prognosis; and prioritize the most individually informative variables in tests. Building on prior research on “prognostic scores,” we develop bootstrap balance tests that upweight covariates associated with the outcome. We adapt this approach for regression-discontinuity designs and use simulations to compare weighting methods based on linear regression and more flexible methods, including machine learning. The results show how prognosis weighting can avoid both false negatives and false positives. To illustrate key points, we study empirical examples from a sample of published studies, including an important debate over close elections.
