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

Adam Bonica

American Journal of Political Science, 2018, vol. 62, issue 4, 830-848

Abstract: 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.

Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://doi.org/10.1111/ajps.12376

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:amposc:v:62:y:2018:i:4:p:830-848

Access Statistics for this article

More articles in American Journal of Political Science from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().