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Activity Number: 465 - Probabilistic Record Linkage: Better Assumptions, Scalable Inference, and Accounting for Uncertainty
Type: Topic Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Social Statistics Section
Abstract #329148
Title: Multiple Imputation of Probabilistic Linkage of Employers in Survey and Administrative Data: Creating CenHRS
Author(s): Dhiren Patki*
Companies: University of Michigan
Keywords: Probabilistic record linkage; Multiple imputation; Wages ; Employer size

CenHRS is a new data source that enhances the Health and Retirement Study (HRS) with administrative data from the Census Business Register (BR). The dataset is constructed by probabilistically linking records on employed HRS respondents with employer characteristics from the BR. An important aspect of the CenHRS is that employer information is derived by multiple imputation (MI) to account for uncertainty in record linkage. In addition to documenting the record linkage procedure used to construct CenHRS, this paper re-examines the relationship between employer size and worker wages to illustrate the trade off between bias and variance implied by the MI linkage method in comparison to alternative methods.

Authors who are presenting talks have a * after their name.

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