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The Computational Social Science Workshop at the University of Chicago cordially invites you to attend this week's talk:
Forecasting Congressional Election Outcomes and Legislative Voting Behavior using Campaign Finance Data
Summary: 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.
A light lunch will be provided by Cedars Mediterranean Kitchen.
Adam Bonica is an Associate Professor of Political Science at Stanford University. His research is at the intersection of data science and politics, with a focus on American Politics. Among his research contributions is the development of quantitative methods for measuring ideological preferences using campaign contributions. This provides a unified approach to measuring political preferences for a wide array of political actors, which are made available as part of the Database on Ideology, Money in Politics, and Elections (DIME). His current research examines how the challenges of early fundraising, and the advantages provided by a candidate's professional network, have been instrumental in sustaining deep representational imbalances in Congress. His work has appeared in the American Journal of Political Science, Political Analysis, Journal of Economic Perspectives, Journal of Law, Economics, and Organization, and JAMA Internal Medicine.
The 2018-2019 Computational Social Science Workshop meets Thursdays from 11 a.m. to 12:20 p.m. in Kent 120. All interested faculty and graduate students are welcome.
Students in the Masters of Computational Social Science program are expected to attend and join the discussion by posting a comment on the issues page of the workshop's public repository on GitHub. Further instructions are documented in the Computational Social Science Workshop's README on Github.