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Political Campaigns and Big Data

Author

Listed:
  • David W. Nickerson
  • Todd Rogers

Abstract

Modern campaigns develop databases of detailed information about citizens to inform electoral strategy and to guide tactical efforts. Despite sensational reports about the value of individual consumer data, the most valuable information campaigns acquire comes from the behaviors and direct responses provided by citizens themselves. Campaign data analysts develop models using this information to produce individual-level predictions about citizens' likelihoods of performing certain political behaviors, of supporting candidates and issues, and of changing their support conditional on being targeted with specific campaign interventions. The use of these predictive scores has increased dramatically since 2004, and their use could yield sizable gains to campaigns that harness them. At the same time, their widespread use effectively creates a coordination game with incomplete information between allied organizations. As such, organizations would benefit from partitioning the electorate to not duplicate efforts, but legal and political constraints preclude that possibility.

Suggested Citation

  • David W. Nickerson & Todd Rogers, 2014. "Political Campaigns and Big Data," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 51-74, Spring.
  • Handle: RePEc:aea:jecper:v:28:y:2014:i:2:p:51-74
    Note: DOI: 10.1257/jep.28.2.51
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    References listed on IDEAS

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    1. Ansolabehere, Stephen & Hersh, Eitan, 2012. "Validation: What Big Data Reveal About Survey Misreporting and the Real Electorate," Political Analysis, Cambridge University Press, vol. 20(04), pages 437-459, September.
    2. Alan Gerber & Donald Green, 2000. "The effects of canvassing, direct mail, and telephone contact on voter turnout: A field experiment," Natural Field Experiments 00248, The Field Experiments Website.
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    Citations

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    Cited by:

    1. Edward McFowland III & Sriram Somanchi & Daniel B. Neill, 2018. "Efficient Discovery of Heterogeneous Treatment Effects in Randomized Experiments via Anomalous Pattern Detection," Papers 1803.09159, arXiv.org, revised Jun 2018.
    2. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    3. repec:eee:pubeco:v:158:y:2018:i:c:p:152-167 is not listed on IDEAS
    4. Ceren Baysan, 2017. "Can More Information Lead to More Voter Polarization? Experimental Evidence from Turkey," 2017 Papers pba1551, Job Market Papers.
    5. Exley, Christine L. & Petrie, Ragan, 2018. "The impact of a surprise donation ask," Journal of Public Economics, Elsevier, vol. 158(C), pages 152-167.

    More about this item

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • D72 - Microeconomics - - Analysis of Collective Decision-Making - - - Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior

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