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An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices

Author

Listed:
  • John M. Abowd
  • Ian M. Schmutte

Abstract

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.

Suggested Citation

  • John M. Abowd & Ian M. Schmutte, 2018. "An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices," Working Papers 18-35, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:18-35
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    File URL: https://www2.census.gov/ces/wp/2018/CES-WP-18-35.pdf
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    Cited by:

    1. Ian M. Schmutte & Nathan Yoder, 2022. "Information Design for Differential Privacy," Papers 2202.05452, arXiv.org, revised Apr 2022.
    2. E. Mark Curtis & Daniel G. Garrett & Eric C. Ohrn & Kevin A. Roberts & Juan Carlos Suárez Serrato, 2021. "Capital Investment and Labor Demand," NBER Working Papers 29485, National Bureau of Economic Research, Inc.
    3. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.
    4. Ronen Gradwohl & Rann Smorodinsky, 2021. "Privacy, Patience, and Protection," Dynamic Games and Applications, Springer, vol. 11(4), pages 759-784, December.
    5. John Mullahy, 2022. "Investigating health-related time use with partially observed data," Review of Economics of the Household, Springer, vol. 20(1), pages 103-121, March.
    6. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    7. John M. Abowd & Ian M. Schmutte & William Sexton & Lars Vilhuber, 2019. "Suboptimal Provision of Privacy and Statistical Accuracy When They are Public Goods," Papers 1906.09353, arXiv.org.
    8. Raj Chetty & John N. Friedman, 2019. "A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 414-420, May.
    9. Jeffrey D. Michler & Anna Josephson & Talip Kilic & Siobhan Murray, 2022. "Privacy Protection, Measurement Error, and the Integration of Remote Sensing and Socioeconomic Survey Data," Papers 2202.05220, arXiv.org.
    10. John M. Abowd & Ian M. Schmutte & William N. Sexton & Lars Vilhuber, 2019. "Why the Economics Profession Must Actively Participate in the Privacy Protection Debate," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 397-402, May.
    11. Katharine G. Abraham & Ron S. Jarmin & Brian C. Moyer & Matthew D. Shapiro, 2020. "Introduction: Big Data for Twenty-First-Century Economic Statistics: The Future Is Now," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 1-22, National Bureau of Economic Research, Inc.
    12. Yosuke Uno & Akira Sonoda & Masaki Bessho, 2021. "The Economics of Privacy: A Primer Especially for Policymakers," Bank of Japan Working Paper Series 21-E-11, Bank of Japan.
    13. John M. Abowd & Robert Ashmead & Ryan Cumings-Menon & Simson Garfinkel & Micah Heineck & Christine Heiss & Robert Johns & Daniel Kifer & Philip Leclerc & Ashwin Machanavajjhala & Brett Moran & William, 2022. "The 2020 Census Disclosure Avoidance System TopDown Algorithm," Papers 2204.08986, arXiv.org.
    14. Guy Aridor & Yeon-Koo Che & Tobias Salz, 2020. "The Effect of Privacy Regulation on the Data Industry: Empirical Evidence from GDPR," NBER Working Papers 26900, National Bureau of Economic Research, Inc.
    15. Rod Garratt & Maarten van Oordt, 2019. "Systemic Privacy as a Public Good: A Case for Electronic Cash," Staff Working Papers 19-24, Bank of Canada.
    16. Binswanger, Johannes & Oechslin, Manuel, 2020. "Better statistics, better economic policies?," European Economic Review, Elsevier, vol. 130(C).
    17. Rehse, Dominik & Tremöhlen, Felix, 2020. "Fostering participation in digital public health interventions: The case of digital contact tracing," ZEW Discussion Papers 20-076, ZEW - Leibniz Centre for European Economic Research.
    18. John Mullahy, 2019. "Identification of a Class of Health-Outcome Distributions under a Common Form of Partial Data Observability," NBER Working Papers 26011, National Bureau of Economic Research, Inc.
    19. Guha, Abhijit & Grewal, Dhruv & Kopalle, Praveen K. & Haenlein, Michael & Schneider, Matthew J. & Jung, Hyunseok & Moustafa, Rida & Hegde, Dinesh R. & Hawkins, Gary, 2021. "How artificial intelligence will affect the future of retailing," Journal of Retailing, Elsevier, vol. 97(1), pages 28-41.

    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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