A Data-Driven Voter Guide for U.S. Elections: Adapting Quantitative Measures of the Preferences and Priorities of Political Elites to Help Voters Learn About Candidates

36 Pages Posted: 4 Mar 2016

Date Written: February 24, 2016

Abstract

Internet-based voter advice applications have experienced tremendous growth across Europe in recent years but have yet to be widely adopted in the United States. By comparison, the candidate-centered U.S. electoral system, which routinely requires voters to consider dozens of candidates across a dizzying array of local, state, and federal offices each time they cast a ballot, introduces challenges of scale to the systematic provision of information. Only recently have methodological advances combined with the rapid growth in publicly available data on candidates and their supporters brought a comprehensive data-driven voter guide within reach. This paper introduces a set of newly developed software tools for collecting, disambiguating, and merging large amounts on data on candidates and other political elites. It then demonstrates how statistical methods developed by political scientists to measure the preferences and expressed priorities of politicians can be adapted to help voters learn about candidates.

Suggested Citation

Bonica, Adam, A Data-Driven Voter Guide for U.S. Elections: Adapting Quantitative Measures of the Preferences and Priorities of Political Elites to Help Voters Learn About Candidates (February 24, 2016). Available at SSRN: https://ssrn.com/abstract=2742094 or http://dx.doi.org/10.2139/ssrn.2742094

Adam Bonica (Contact Author)

Stanford University ( email )

Stanford, CA 94305
United States

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