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Political Campaigns and Big Data

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  • 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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    4. Rogers, Todd & Nickerson, David W., 2013. "Can Inaccurate Beliefs about Incumbents be Changed? And Can Reframing Change Votes?," Working Paper Series rwp13-018, Harvard University, John F. Kennedy School of Government.
    5. 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. Enrico Cantoni & Vincent Pons, 2021. "Strict Id Laws Don’t Stop Voters: Evidence from a U.S. Nationwide Panel, 2008–2018," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 136(4), pages 2615-2660.
    2. Sayibu Ibrahim Nnindini & Kobby Mensah, 2021. "Political Relationship Marketing: An Examination of Internal Relationship Management in Ghanaian Political Parties," Information Management and Business Review, AMH International, vol. 13(3), pages 30-40.
    3. Mehta, Somya & Erickson, Kristofer, 2022. "Can online political targeting be rendered transparent? Prospects for campaign oversight using the Facebook Ad Library," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 11(1), pages 1-31.
    4. Markku Kaustia & Samuli Knüpfer & Sami Torstila, 2016. "Stock Ownership and Political Behavior: Evidence from Demutualizations," Management Science, INFORMS, vol. 62(4), pages 945-963, April.
    5. Braaksma, Barteld & Zeelenberg, Kees, 2015. "“Re-make/Re-model”: Should big data change the modelling paradigm in official statistics?," MPRA Paper 87741, University Library of Munich, Germany.
    6. Edward McFowland III & Sriram Somanchi & Daniel B. Neill, 2018. "Efficient Discovery of Heterogeneous Quantile Treatment Effects in Randomized Experiments via Anomalous Pattern Detection," Papers 1803.09159, arXiv.org, revised May 2023.
    7. Baldwin-Philippi, Jessica, 2019. "Data campaigning: between empirics and assumptions," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 8(4), pages 1-18.
    8. Balart, Pau & Casas, Agustin & Troumpounis, Orestis, 2022. "Technological change, campaign spending and polarization," Journal of Public Economics, Elsevier, vol. 211(C).
    9. Joseph Gerald Hirschberg & Jeanette Ngaire Lye, 2020. "Grading Journals In Economics: The Abcs Of The Abdc," Journal of Economic Surveys, Wiley Blackwell, vol. 34(4), pages 876-921, September.
    10. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    11. Exley, Christine L. & Petrie, Ragan, 2018. "The impact of a surprise donation ask," Journal of Public Economics, Elsevier, vol. 158(C), pages 152-167.
    12. Ceren Baysan, 2017. "Can More Information Lead to More Voter Polarization? Experimental Evidence from Turkey," 2017 Papers pba1551, Job Market Papers.

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    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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