Optimal Probabilistic Record Linkage: Best Practice for Linking Employers in Survey and Administrative Data
March 2019
Working Paper Number:
CES-19-08
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.
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census research,
survey data,
survey,
respondent,
linked census,
imputation,
census employment,
linkage
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Social Security Administration,
Longitudinal Research Database,
National Science Foundation,
National Bureau of Economic Research,
Longitudinal Business Database,
Employer Identification Numbers,
Survey of Income and Program Participation,
Cornell University,
Unemployment Insurance,
Health and Retirement Study,
Alfred P Sloan Foundation,
Longitudinal Employer Household Dynamics,
Census Bureau Business Register,
Business Register,
Quarterly Workforce Indicators,
Quarterly Census of Employment and Wages,
University of Michigan,
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