RAND HRS Data Products
The Health and Retirement Study (HRS) is a longitudinal household survey conducted by the Institute for Social Research at the University of Michigan. The multidisciplinary data provide researchers the opportunity to investigate many different aspects related to population aging in the United States. The HRS is extraordinarily rich and complex. With the goal of making the data more accessible to researchers, the RAND Center for the Study of Aging, with funding and support from the National Institute on Aging (NIA) and the Social Security Administration (SSA), created the seven data products:
- RAND HRS Longitudinal File is a user-friendly file derived from all waves of the HRS. It contains cleaned and processed variables with consistent and intuitive naming conventions, model-based imputations, and spousal counterparts of most individual-level variables.
- RAND HRS Detailed Imputations File contains the component and ownership variables that are used to create the income, wealth, and medical expenditures summary measures found in the RAND HRS Longitudinal File. It is distributed as a companion file to the RAND HRS Longitudinal File.
- RAND HRS Family Data Files are user-friendly files derived from HRS other person files. The files include the characteristics of all children of HRS Respondents and spouses.
- RAND HRS CAMS Data File is a user-friendly version of the spending section (Part B) of the CAMS survey.
- RAND HRS Tax Calculations contain information about federal, state, and FICA taxes for Respondents to the HRS 2000 – 2014 surveys.
- RAND HRS Fat Files which contain most of the original HRS variables with household data merged to the Respondent level. There is one file for each survey year. Currently, there are files for 1992, 1993, 1994, 1995, and biennially 1996-2018.
- RAND HRS Exit/Post-Exit Interview and Finder Files combine the Exit and Post-Exit Interviews into one file per survey year. The Finder file allows users to identify which years Exit and Post-Exit interviews are available for each Respondent.
Before doing so, however, we kindly request that users first consult the documentation that accompanies our data products, as we have found that our responses often point users to specific sections of the documentation that provide further detail on the variables mentioned in the users’ queries.
In addition, we recommend that users become familiar with some of the information provided on the HRS website, such as the HRS questionnaires and codebooks for the key variables under study.
We have also found the concordance tool extremely useful to help find available variables across waves: