Grants

University of Michigan

To create and analyze datasets that combine the Health and Retirement Study (HRS) with data from the Census Bureau from the firms where HRS respondents have worked

  • Amount $4,398,616
  • City Ann Arbor, MI
  • Investigator Margaret Levenstein
  • Year 2011
  • Program Research
  • Sub-program Working Longer

Funded by the National Institute on Aging, the Health and Retirement Study (HRS) is a nationally representative panel study of Americans over the age of 50 and their spouses. Respondents are interviewed every two years. The core survey collects information on income and wealth, employment, pension plans and health insurance, physical health and functioning, cognition, expectations, preferences, demographics, family structure, and some biomarkers. Supplemental surveys of subsets of the respondents cover more extensive biological, cognitive, and genetic measures; consumption, education, and human capital; information technology use; prescription drug use; happiness and well being; and education and human capital expenditures. This grant will fund a project by a team of researchers led by Maggie Levenstein of the Michigan Census Research Data Center to link HRS data to the U.S. Business Register, a list of business establishments in the U.S. compiled and maintained by the U.S. Census Bureau. Successfully linking these two datasets will greatly increase the potential usefulness of the HRS, allowing researchers to measure how various health and wellness markers of older workers vary and correlate with the characteristics of the firms that employ them and opening new research possibilities in economics, psychology, organizational behavior, sociology, and demography. In addition to the work required to link the two datasets, funds will support the creation and dissemination of a publicly-available version of the new, linked dataset (suitably anonymized to protect the privacy of survey respondents), a series of papers conducting preliminary analysis of the data, and a conference to promote the new dataset and its use.

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