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Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report

Author

Listed:
  • John M. Abowd
  • William R. Bell
  • J. David Brown
  • Michael B. Hawes
  • Misty L. Heggeness
  • Andrew D. Keller
  • Vincent T. Mule Jr.
  • Joseph L. Schafer
  • Matthew Spence
  • Lawrence Warren
  • Moises Yi

Abstract

This report documents the efforts of the Census Bureau’s Citizen Voting-Age Population (CVAP) Internal Expert Panel (IEP) and Technical Working Group (TWG) toward the use of multiple data sources to produce block-level statistics on the citizen voting-age population for use in enforcing the Voting Rights Act. It describes the administrative, survey, and census data sources used, and the four approaches developed for combining these data to produce CVAP estimates. It also discusses other aspects of the estimation process, including how records were linked across the multiple data sources, and the measures taken to protect the confidentiality of the data.

Suggested Citation

  • John M. Abowd & William R. Bell & J. David Brown & Michael B. Hawes & Misty L. Heggeness & Andrew D. Keller & Vincent T. Mule Jr. & Joseph L. Schafer & Matthew Spence & Lawrence Warren & Moises Yi, 2020. "Determination of the 2020 U.S. Citizen Voting Age Population (CVAP) Using Administrative Records and Statistical Methodology Technical Report," Working Papers 20-33, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:20-33
    as

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    File URL: https://www2.census.gov/ces/wp/2020/CES-WP-20-33.pdf
    File Function: First version, 2020
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    References listed on IDEAS

    as
    1. Bakk, Zsuzsa & Kuha, Jouni, 2018. "Two-step estimation of models between latent classes and external variables," LSE Research Online Documents on Economics 85161, London School of Economics and Political Science, LSE Library.
    2. J. David Brown & Misty L. Heggeness & Suzanne M. Dorinski & Lawrence Warren & Moises Yi, 2018. "Understanding the Quality of Alternative Citizenship Data Sources for the 2020 Census," Working Papers 18-38, Center for Economic Studies, U.S. Census Bureau.
    3. Chung H. & Loken E. & Schafer J.L., 2004. "Difficulties in Drawing Inferences With Finite-Mixture Models: A Simple Example With a Simple Solution," The American Statistician, American Statistical Association, vol. 58, pages 152-158, May.
    4. Zsuzsa Bakk & Jouni Kuha, 2018. "Two-Step Estimation of Models Between Latent Classes and External Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 871-892, December.
    5. Frauke Kreuter & Ting Yan & Roger Tourangeau, 2008. "Good item or bad—can latent class analysis tell?: the utility of latent class analysis for the evaluation of survey questions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(3), pages 723-738, June.
    6. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    7. Paul P. Biemer & Christopher Wiesen, 2002. "Measurement error evaluation of self‐reported drug use: a latent class analysis of the US National Household Survey on Drug Abuse," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 97-119, February.
    8. Mary Layne & Deborah Wagner & Cynthia Rothhaas, 2014. "Estimating Record Linkage False Match Rate for the Person Identification Validation System," CARRA Working Papers 2014-02, Center for Economic Studies, U.S. Census Bureau.
    9. 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.
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    Cited by:

    1. Meyer, Bruce D. & Wyse, Angela & Corinth, Kevin, 2023. "The size and Census coverage of the U.S. homeless population," Journal of Urban Economics, Elsevier, vol. 136(C).

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    More about this item

    Keywords

    citizenship; administrative records; voting-age population; big data;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs

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