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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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    Citations

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Binswanger, Johannes & Oechslin, Manuel, 2020. "Better statistics, better economic policies?," European Economic Review, Elsevier, vol. 130(C).
    5. 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.
    6. 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.
    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. 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.
    9. Katharine G. Abraham & Ron S. Jarmin & Brian Moyer & Matthew D. Shapiro, 2020. "Big Data for Twenty-First Century Economic Statistics: The Future Is Now," NBER Chapters, in: Big Data for Twenty-First Century Economic Statistics, National Bureau of Economic Research, Inc.
    10. 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.

    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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