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dc.contributor.authorMcCue, Kristin
dc.contributor.authorAbowd, John
dc.contributor.authorLevenstein, Margaret
dc.contributor.authorPatki, Dhiren
dc.contributor.authorRodgers, Ann
dc.contributor.authorShapiro, Matthew
dc.contributor.authorWasi, Nada
dc.date.accessioned2016-05-13T17:22:37Z
dc.date.available2016-05-13T17:22:37Z
dc.date.issued2016-05-10
dc.identifier.urihttps://hdl.handle.net/1813/43895
dc.descriptionPresented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.info/event/ncrn-spring-2016-meetingen_US
dc.description.abstractThis paper documents work using probabilistic record linkage to create a crosswalk between jobs reported in the Health and Retirement Study (HRS) and the list of workplaces on Census Bureau’s Business Register. Matching job records provides an opportunity to join variables that occur uniquely in separate datasets, to validate responses, and to develop missing data imputation models. Identifying the respondent’s workplace (“establishment”) is valuable for HRS because it allows researchers to incorporate the effects of particular social, economic, and geospatial work environments in studies of respondent health and retirement behavior. The linkage makes use of name and address standardizing techniques tailored to business data that were recently developed in a collaboration between researchers at Census, Cornell, and the University of Michigan. The matching protocol makes no use of the identity of the HRS respondent and strictly protects the confidentiality of information about the respondent’s employer. The paper first describes the clerical review process used to create a set of human-reviewed candidate pairs, and use of that set to train matching models. It then describes and compares several linking strategies that make use of employer name, address, and phone number. Finally it discusses alternative ways of incorporating information on match uncertainty into estimates based on the linked data, and illustrates their use with a preliminary sample of matched HRS jobs.en_US
dc.description.sponsorshipNSF Grant 1507241 (NCRN Coordinating Office) and 1131500 (to University of Michigan Ann Arbor)en_US
dc.language.isoen_USen_US
dc.subjectprobabilistic record linkageen_US
dc.subjectjob recordsen_US
dc.subjectHealth and Retirement Studyen_US
dc.titleNCRN Meeting Spring 2016: Developing job linkages for the Health and Retirement Studyen_US
dc.typepresentationen_US


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