TY - RPRT T1 - NCRN Meeting Spring 2016: Developing job linkages for the Health and Retirement Study Y1 - 2016 A1 - Mccue, Kristin A1 - Abowd, John A1 - Levenstein, Margaret A1 - Patki, Dhiren A1 - Rodgers, Ann A1 - Shapiro, Matthew A1 - Wasi, Nada AB - NCRN Meeting Spring 2016: Developing job linkages for the Health and Retirement Study McCue, Kristin; Abowd, John; Levenstein, Margaret; Patki, Dhiren; Rodgers, Ann; Shapiro, Matthew; Wasi, Nada This 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. Presented at the NCRN Meeting Spring 2016 in Washington DC on May 9-10, 2016; see http://www.ncrn.info/event/ncrn-spring-2016-meeting PB - University of Michigan UR - http://hdl.handle.net/1813/43895 ER - TY - JOUR T1 - Record Linkage using STATA: Pre-processing, Linking and Reviewing Utilities JF - The Stata Journal Y1 - 2015 A1 - Wasi, Nada A1 - Flaaen, Aaron AB - In this article, we describe Stata utilities that facilitate probabilistic record linkage—the technique typically used for merging two datasets with no common record identifier. While the preprocessing tools are developed specifically for linking two company databases, the other tools can be used for many different types of linkage. Specifically, the stnd_compname and stnd_address commands parse and standardize company names and addresses to improve the match quality when linking. The reclink2 command is a generalized version of Blasnik's reclink (2010, Statistical Software Components S456876, Department of Economics, Boston College) that allows for many-to-one matching. Finally, clrevmatch is an interactive tool that allows the user to review matched results in an efficient and seamless manner. Rather than exporting results to another file format (for example, Excel), inputting clerical reviews, and importing back into Stata, one can use the clrevmatch tool to conduct all of these steps within Stata. This helps improve the speed and flexibility of matching, which often involves multiple runs. VL - 15 UR - http://www.stata-journal.com/article.html?article=dm0082 IS - 3 ER - TY - RPRT T1 - NCRN Meeting Spring 2014: Summer Working Group for Employer List Linking (SWELL) Y1 - 2014 A1 - Gathright, Graton A1 - Kutzbach, Mark A1 - Mccue, Kristin A1 - McEntarfer, Erika A1 - Monti, Holly A1 - Trageser, Kelly A1 - Vilhuber, Lars A1 - Wasi, Nada A1 - Wignall, Christopher AB - NCRN Meeting Spring 2014: Summer Working Group for Employer List Linking (SWELL) Gathright, Graton; Kutzbach, Mark; Mccue, Kristin; McEntarfer, Erika; Monti, Holly; Trageser, Kelly; Vilhuber, Lars; Wasi, Nada; Wignall, Christopher Presentation for NCRN Spring 2014 meeting PB - NCRN Coordinating Office UR - http://hdl.handle.net/1813/36396 ER -