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Reconstruction of age distributions from differentially private census data

Author

Listed:
  • Sigurd Dyrting

    (Northern Institute, Charles Darwin University)

  • Abraham Flaxman

    (University of Washington)

  • Ethan Sharygin

    (Portland State University)

Abstract

The age distribution of a population is important for understanding the demand and provision of labor and services, and as a denominator for calculating key age-specific rates such as fertility and mortality. In the US, the most important source of information on age distributions is the decennial census, but a new disclosure avoidance system (DAS) based on differential privacy will inject noise into the data, potentially compromising its utility for small areas and minority populations. In this paper, we explore the question whether there are statistical methods that can be applied to noisy age distributions to enhance the research uses of census data without compromising privacy. We apply a non-parametric method for smoothing with naive or informative priors to age distributions from the 2010 Census via demonstration data which have had the US Census Bureau’s implementation of differential privacy applied. We find that smoothing age distributions can increase the fidelity of the demonstration data to previously published population counts by age. We discuss implications for uses of data from the 2020 US Census and potential consequences for the measurement of population dynamics, health, and disparities.

Suggested Citation

  • Sigurd Dyrting & Abraham Flaxman & Ethan Sharygin, 2022. "Reconstruction of age distributions from differentially private census data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 41(6), pages 2311-2329, December.
    • Handle: RePEc:kap:poprpr:v:41:y:2022:i:6:d:10.1007_s11113-022-09734-2
    DOI: 10.1007/s11113-022-09734-2
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    References listed on IDEAS

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    1. Steven Ruggles & Catherine Fitch & Diana Magnuson & Jonathan Schroeder, 2019. "Differential Privacy and Census Data: Implications for Social and Economic Research," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 403-408, May.
    2. Laura McKenna, 2018. "Disclosure Avoidance Techniques Used for the 1970 through 2010 Decennial Censuses of Population and Housing," Working Papers 18-47, Center for Economic Studies, U.S. Census Bureau.
    3. Joop de Beer, 2011. "A new relational method for smoothing and projecting age-specific fertility rates: TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 24(18), pages 409-454.
    4. Santos-Lozada, Alexis R & Perez-Rivera, Danilo T & Bhat, Aarti C., 2020. "How differential privacy will affect our understanding of population growth in the United States," SocArXiv pmux7, Center for Open Science.
    5. Sigurd Dyrting, 2020. "Smoothing migration intensities with P-TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 43(55), pages 1607-1650.
    6. Alexis R. Santos-Lozada & Jeffrey T. Howard & Ashton M. Verdery, 2020. "How differential privacy will affect our understanding of health disparities in the United States," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 117(24), pages 13405-13412, June.
    7. Joop de Beer, 2012. "Smoothing and projecting age-specific probabilities of death by TOPALS," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 27(20), pages 543-592.
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    Cited by:

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