Predicting United States Policy Outcomes with Random Forests

Shawn McGuire () and Charles Delahunt ()
Additional contact information
Charles Delahunt: University of Washington, Seattle, WA

No inetwp138, Working Papers Series from Institute for New Economic Thinking

Abstract: Two decades of U.S. government legislative outcomes, as well as the policy preferences of rich people, the general population, and diverse interest groups, were captured in a detailed dataset curated and analyzed by Gilens, Page et al. (2014). They found that the preferences of the rich correlated strongly with policy outcomes, while the preferences of the general population did not, except via a linkage with rich people`s preferences. Their analysis applied the tools of classical statistical inference, in particular logistic regression. In this paper we analyze the Gilens dataset using the complementary tools of Random Forest classifiers (RFs), from Machine Learning. We present two primary findings, concerning respectively prediction and inference: (i) Holdout test sets can be predicted with approximately 70% balanced accuracy by models that consult only the preferences of rich people and a small number of powerful interest groups, as well as policy area labels. These results include retrodiction, where models trained on pre-1997 cases predicted ``future`` (post-1997) cases. The 20% gain in accuracy over baseline (chance), in this detailed but noisy dataset, indicates the high importance of a few wealthy players in U.S. policy outcomes, and aligns with a body of research indicating that the U.S. government has significant plutocratic tendencies. (ii) The feature selection methods of RF models identify especially salient subsets of interest groups (economic players). These can be used to further investigate the dynamics of governmental policy making, and also offer an example of the potential value of RF feature selection methods for inference on datasets such as this one.

Keywords: political economy; financial crisis; political parties; political money. (search for similar items in EconPapers)
JEL-codes: D72 G20 G38 K22 L5 N22 P16 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2020-10-02
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pol
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published

Downloads: (external link)
https://www.ineteconomics.org/uploads/papers/McGui ... y_INET_25oct2020.pdf (application/pdf)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3731837 First version, 2020 (text/html)

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:thk:wpaper:inetwp138

DOI: 10.36687/inetwp138

Access Statistics for this paper

More papers in Working Papers Series from Institute for New Economic Thinking Contact information at EDIRC.
Bibliographic data for series maintained by Pia Malaney ().