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Big Data for Policy Analysis: The Good, The Bad, and The Ugly

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  • Laurie A. Schintler
  • Rajendra Kulkarni

Abstract

Big data holds tremendous potential for public policy analysis. At the same time, its use prompts a number of issues related to statistical bias, privacy, equity, and governance, among others. Accordingly, there is a need to formulate, evaluate, and implement policies that not only mitigate the risks, but also maximize the benefits of using big data for policy analysis. This poses a number of challenges, which are highlighted in this essay.

Suggested Citation

  • Laurie A. Schintler & Rajendra Kulkarni, 2014. "Big Data for Policy Analysis: The Good, The Bad, and The Ugly," Review of Policy Research, Policy Studies Organization, vol. 31(4), pages 343-348, July.
    • Handle: RePEc:bla:revpol:v:31:y:2014:i:4:p:343-348
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    File URL: http://hdl.handle.net/10.1111/ropr.12079
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    Citations

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    Cited by:

    1. Prpić, John, 2017. "A Framework for Policy Crowdsourcing," SocArXiv pmfdx, Center for Open Science.
    2. Giacomo Caterini, 2018. "Classifying Firms with Text Mining," DEM Working Papers 2018/09, Department of Economics and Management.
    3. Certomà, Chiara & Corsini, Filippo & Frey, Marco, 2020. "Hyperconnected, receptive and do-it-yourself city. An investigation into the European “imaginary” of crowdsourcing for urban governance," Technology in Society, Elsevier, vol. 61(C).
    4. Jinwen Qiu & Wenjian Liu & Ning Ning, 2020. "Evolution of Regional Innovation with Spatial Knowledge Spillovers: Convergence or Divergence?," Networks and Spatial Economics, Springer, vol. 20(1), pages 179-208, March.
    5. Hui Zhang & Huiying Ding & Jianying Xiao, 2023. "How Organizational Agility Promotes Digital Transformation: An Empirical Study," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    6. Ulbricht, Lena, 2020. "Algorithmen und Politisierung [Algorithms and politicization]," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 0, pages 255-278.
    7. Kolkman, Daan, 2020. "The usefulness of algorithmic models in policy making," SocArXiv hpma8, Center for Open Science.
    8. Md Altab Hossin & Jie Du & Lei Mu & Isaac Owusu Asante, 2023. "Big Data-Driven Public Policy Decisions: Transformation Toward Smart Governance," SAGE Open, , vol. 13(4), pages 21582440231, December.
    9. Justin Longo & Alan Rodney Dobell, 2018. "The Limits of Policy Analytics: Early Examples and the Emerging Boundary of Possibilities," Politics and Governance, Cogitatio Press, vol. 6(4), pages 5-17.
    10. Luis Alberto Delgado-de-la-Garza & Gonzalo Adolfo Garza-Rodríguez & Daniel Alejandro Jacques-Osuna & Alejandro Múgica-Lara & Carlos Alberto Carrasco, 2021. "Does the use of a big data variable improve monetary policy estimates? Evidence from Mexico," Economics and Business Letters, Oviedo University Press, vol. 10(4), pages 383-393.
    11. Matteo Fontana & Massimo Tavoni & Simone Vantini, 2019. "Functional Data Analysis of high-frequency load curves reveals drivers of residential electricity consumption," PLOS ONE, Public Library of Science, vol. 14(6), pages 1-16, June.

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