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

John Abowd (), Joelle Abramowitz, Margaret Levenstein, Kristin McCue, Dhiren Patki, Trivellore Raghunathan, Ann M. Rodgers, Matthew Shapiro and Nada Wasi

Working Papers from U.S. Census Bureau, Center for Economic Studies

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.

Keywords: Probabilistic record linkage; survey data; administrative data; multiple imputation; measurement error; nonresponse (search for similar items in EconPapers)
Pages: 41 pages
Date: 2019-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Citations: View citations in EconPapers (2)

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https://www2.census.gov/ces/wp/2019/CES-WP-19-08.pdf First version, 2019 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:cen:wpaper:19-08

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