Policy agendas of the American state legislatures
Ethan Dee, Alex Garlick
TL;DR
This paper uses machine learning to categorize millions of state bills into policy areas, helping researchers study U.S. state legislatures.
Contribution
A transformer-based model is introduced to classify state bills into policy areas with high coverage and accuracy.
Findings
The model successfully coded 1.36 million bills into 28 policy areas since 2009.
The method outperforms traditional dictionary-based approaches in coverage and accuracy.
Abstract
State legislatures in the United States handle a number of important policy issues, but pose a challenge for researchers to observe because they are not organized by any central agency. We use a machine learning model based on the “transformer” architecture and contextual word-piece embeddings to code the universe of bills introduced in the states since 2009 (about 1.36 million bills) into 28 policy areas. Validation exercises show our method compares favorably with hand-coded estimates of bill policy areas while offering far greater coverage than legacy human-supervised “dictionary” methods. We explain how researchers can use these estimates to investigate sub-national governance in the United States.
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Taxonomy
TopicsElectoral Systems and Political Participation · Computational and Text Analysis Methods · Judicial and Constitutional Studies