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Estimating Record Linkage False Match Rate for the Person Identification Validation System

Author

Listed:
  • Mary Layne
  • Deborah Wagner
  • Cynthia Rothhaas

Abstract

The Census Bureau Person Identification Validation System (PVS) assigns unique person identifiers to federal, commercial, census, and survey data to facilitate linkages across files. PVS uses probabilistic matching to assign a unique Census Bureau identifier for each person. This paper presents a method to measure the false match rate in PVS following the approach of Belin and Rubin (1995). The Belin and Rubin methodology requires truth data to estimate a mixture model. The parameters from the mixture model are used to obtain point estimates of the false match rate for each of the PVS search modules. The truth data requirement is satisfied by the unique access the Census Bureau has to high quality name, date of birth, address and Social Security (SSN) data. Truth data are quickly created for the Belin and Rubin model and do not involve a clerical review process. These truth data are used to create estimates for the Belin and Rubin parameters, making the approach more feasible. Both observed and modeled false match rates are computed for all search modules in federal administrative records data and commercial data.

Suggested Citation

  • 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.
    • Handle: RePEc:cen:cpaper:2014-02
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    File URL: https://www.census.gov/content/dam/Census/library/working-papers/2014/adrm/carra-wp-2014-02.pdf
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    References listed on IDEAS

    as
    1. Deborah Wagner & Mary Lane, 2014. "The Person Identification Validation System (PVS): Applying the Center for Administrative Records Research and Applications’ (CARRA) Record Linkage Software," CARRA Working Papers 2014-01, Center for Economic Studies, U.S. Census Bureau.
    2. Larsen M. D & Rubin D. B, 2001. "Iterative Automated Record Linkage Using Mixture Models," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 32-41, March.
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    9. Catherine G. Massey, 2014. "Creating Linked Historical Data: An Assessment of the Census Bureau’s Ability to Assign Protected Identification Keys to the 1960 Census," CARRA Working Papers 2014-12, Center for Economic Studies, U.S. Census Bureau.
    10. Mulry Mary H. & Keller Andrew D., 2017. "Comparison of 2010 Census Nonresponse Follow-Up Proxy Responses with Administrative Records Using Census Coverage Measurement Results," Journal of Official Statistics, Sciendo, vol. 33(2), pages 455-475, June.
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    13. Misty Heggeness & Marta Murray-Close, 2019. "Manning Up and Womaning Down: How Husbands and Wives Report Earnings When She Earns More," Opportunity and Inclusive Growth Institute Working Papers 28, Federal Reserve Bank of Minneapolis.
    14. Carolyn A. Lieble & Sonya Rastogi & Leticia E. Fernandez & James M. Noon & Sharon R. Ennis, 2014. "America’s Churning Races: Race and Ethnic Response Changes between Census 2000 and the 2010 Census," CARRA Working Papers 2014-09, Center for Economic Studies, U.S. Census Bureau.
    15. Keller Andrew & Mule Vincent T. & Morris Darcy Steeg & Konicki Scott, 2018. "A Distance Metric for Modeling the Quality of Administrative Records for Use in the 2020 U.S. Census," Journal of Official Statistics, Sciendo, vol. 34(3), pages 599-624, September.
    16. Leticia Fernández & Sonya R. Porter & Sharon R. Ennis & Renuka Bhaskar, 2018. "Factors that Influence Change in Hispanic Identification: Evidence from Linked Decennial Census and American Community Survey Data," Working Papers 18-45, Center for Economic Studies, U.S. Census Bureau.
    17. Carolyn A. Liebler & Sonya R. Porter & Leticia E. Fernandez & James M. Noon & Sharon R. Ennis, 2017. "America’s Churning Races: Race and Ethnicity Response Changes Between Census 2000 and the 2010 Census," Demography, Springer;Population Association of America (PAA), vol. 54(1), pages 259-284, February.
    18. Bastian, Jacob E. & Jones, Maggie R., 2021. "Do EITC expansions pay for themselves? Effects on tax revenue and government transfers," Journal of Public Economics, Elsevier, vol. 196(C).

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    Keywords

    false match rate; PVS;

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