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Optimal Probabilistic Record Linkage: Best Practice for Linking Employers in Survey and Administrative Data

Author

Listed:
  • John M. Abowd
  • Joelle Abramowitz
  • Margaret C. Levenstein
  • Kristin McCue
  • Dhiren Patki
  • Trivellore Raghunathan
  • Ann M. Rodgers
  • Matthew D. Shapiro
  • Nada Wasi

Abstract

This paper illustrates an application of record linkage between a household-level survey and an establishment-level frame in the absence of unique identifiers. Linkage between frames in this setting is challenging because the distribution of employment across firms is highly asymmetric. To address these difficulties, this paper uses a supervised machine learning model to probabilistically link survey respondents in the Health and Retirement Study (HRS) with employers and establishments in the Census Business Register (BR) to create a new data source which we call the CenHRS. Multiple imputation is used to propagate uncertainty from the linkage step into subsequent analyses of the linked data. The linked data reveal new evidence that survey respondents’ misreporting and selective nonresponse about employer characteristics are systematically correlated with wages.

Suggested Citation

  • John M. Abowd & Joelle Abramowitz & Margaret C. Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann M. Rodgers & Matthew D. Shapiro & Nada Wasi, 2019. "Optimal Probabilistic Record Linkage: Best Practice for Linking Employers in Survey and Administrative Data," Working Papers 19-08, Center for Economic Studies, U.S. Census Bureau.
  • Handle: RePEc:cen:wpaper:19-08
    as

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    File URL: https://www2.census.gov/ces/wp/2019/CES-WP-19-08.pdf
    File Function: First version, 2019
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    References listed on IDEAS

    as
    1. Kevin L. McKinney & Andrew S. Green & Lars Vilhuber & John M. Abowd, 2017. "Total Error and Variability Measures with Integrated Disclosure Limitation for Quarterly Workforce Indicators and LEHD Origin Destination Employment Statistics in On The Map," Working Papers 17-71, Center for Economic Studies, U.S. Census Bureau.
    2. Martha Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2017. "How Well Do Automated Linking Methods Perform? Lessons from U.S. Historical Data," NBER Working Papers 24019, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Probabilistic record linkage; survey data; administrative data; multiple imputation; measurement error; nonresponse;

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