Replication Data for: Inferring-Roll Call Scores from Campaign Contributions Using Supervised Machine Learning
<|begin_of_text|><|start_header_id|>system<|end_header_id|> You are a research archivist expert in dataset metadata. Answer the question truthfully.  <|eot_id|><|start_header_id|>user<|end_header_id|> Here is the Description of a dataset. 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 vo' . Please return only a json list of at most 3 Keywords corresponding to the Description:.<|eot_id|><|start_header_id|>assistant<|end_header_id|> {"Answer" : ["Congress","Ideal point estimation","Ideology","Machine learning","Measurement","Roll call scores"]} <|eot_id|>
 Here is the Description of a dataset. 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 vo' . Please return only a json list of at most 3 Keywords corresponding to the 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 vo
Congress; Ideal point estimation; Ideology; Machine learning; Measurement; Roll call scores