Inferring Roll‐Call Scores from Campaign Contributions Using Supervised Machine Learning

This article develops a generalized supervised learning methodology for inferring roll‐call scores from campaign contribution data. Rather than use unsupervised methods to recover a latent dimension that best explains patterns in giving, donation patterns are instead mapped onto a target measure of legislative voting behavior. Supervised models significantly outperform alternative measures of ideology in predicting legislative voting behavior. Fundraising prior to entering office provides a highly informative signal about future voting behavior. Impressively, forecasts based on fundraising as a nonincumbent predict future voting behavior as accurately as in‐sample forecasts based on votes cast during a legislator's first 2 years in Congress. The combined results demonstrate campaign contributions are powerful predictors of roll‐call voting behavior and resolve an ongoing debate as to whether contribution data successfully distinguish between members of the same party.