ICAR Research Data Repository for Knowledge Management
Replication Data for: Inferring-Roll Call Scores from Campaign Contributions Using Supervised Machine Learning
Harvard Dataverse (Africa Rice Center, Bioversity International, CCAFS, CIAT, IFPRI, IRRI and WorldFish)
View Archive Info| Field | Value | |
| Title |
Replication Data for: Inferring-Roll Call Scores from Campaign Contributions Using Supervised Machine Learning
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| Identifier |
https://doi.org/10.7910/DVN/7EWDRF
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| Creator |
Bonica, Adam
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| Publisher |
Harvard Dataverse
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| Description |
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.
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| Subject |
Social Sciences
Ideology Measurement Machine learning Ideal point estimation Roll call scores Congress |
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| Contributor |
Bonica, Adam
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