Replication Data for: Inferring-Roll Call Scores from Campaign Contributions Using Supervised Machine Learning

This paper 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 casts during a legislator’s first two 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.