Journal of Political Institutions and Political Economy > Vol 6 > Issue 3–4

Measuring the Policy Content of Congressional and American State Legislation Using Machine Learning

Ethan Dee, Independent Researcher, dr.ethandee@gmail.com , Alex Garlick, Department of Political Science, University of Vermont, USA, alex.garlick@uvm.edu
 
Suggested Citation
Ethan Dee and Alex Garlick (2025), "Measuring the Policy Content of Congressional and American State Legislation Using Machine Learning", Journal of Political Institutions and Political Economy: Vol. 6: No. 3–4, pp 455-498. http://dx.doi.org/10.1561/113.00000132

Publication Date: 01 Oct 2025
© 2025 E. Dee and A. Garlick
 
Subjects
Legislatures,  Political economy,  Public policy,  State politics
 
Keywords
State politicslegislaturespublic policymachine learning
 

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Open Access

This is published under the terms of CC-BY.

In this article:
Background and Summary 
Methods 
Technical Validation 
Data Records and Usage Notes 
References 

Abstract

We use a machine learning model based on the transformer architecture to replicate and expand the Comparative Agenda Project’s coverage of American legislatures. Our model is jointly trained on pre-coded Congressional and Pennsylvania legislation and it compares favorably to extant supervised machine learning models. Using Pennsylvania as a keystone allows us to bridge the national and state legislative contexts, and produce 1.687 million estimates of the leading policy in legislative documents from Congress and the 50 state legislatures since about 2009. Validations show the model agrees with human-coders on the vast majority of policy assignments, and the disagreements are based more on inconsistencies in the codebook’s logic than random error. We discuss the challenges with applying a model like this to the study of legislative institutions.

DOI:10.1561/113.00000132

Companion

Journal of Political Institutions and Political Economy, Volume 6, Issue 3-4 Special Issue: Artificial Intelligence and the Study of Political Institutions
See the other articles that are part of this special issue.