title: The impact of retirement on health: quasi-experimental methods using administrative data.
abstract: BACKGROUND
  Is retirement good or bad for health? Disentangling causality is difficult. Much of the previous quasi-experimental research on the effect of health on retirement used self-reported health and relied upon discontinuities in public retirement incentives across Europe. The current study investigated the effect of retirement on health by exploiting discontinuities in private retirement incentives to test the effect of retirement on health using a quasi-experimental study design.
  
  
  METHODS
  Secondary data (1997-2009) on a cohort of male manufacturing workers in a United States setting. Health status was determined using claims data from private insurance and Medicare. Analyses used employer-based administrative and claims data and claim data from Medicare.
  
  
  RESULTS
  Widely used selection on observables models overstate the negative impact of retirement due to the endogeneity of the decision to retire. In addition, health status as measured by administrative claims data provide some advantages over the more commonly used survey items. Using an instrument and administrative health records, we find null to positive effects from retirement on all fronts, with a possible exception of increased risk for diabetes.
  
  
  CONCLUSIONS
  This study provides evidence that retirement is not detrimental and may be beneficial to health for a sample of manufacturing workers. In addition, it supports previous research indicating that quasi-experimental methodologies are necessary to evaluate the relationship between retirement and health, as any selection on observable model will overstate the negative relationship of retirement on health. Further, it provides a model for how such research could be implemented in countries like the United States that do not have a strong public pension program. Finally, it demonstrates that such research need-not rely upon survey data, which has certain shortcomings and is not always available for homogenous samples.
authors.affiliation: American Institutes for Research, 2800 Campus Dr. Suite 200, San Mateo, CA, 94330, USA. elli.horner@gmail.com.
authors.forename: Elizabeth Mokyr
authors.initials: EM
authors.lastname: Horner
authors.affiliation: Stanford University School of Medicine, Population Health Sciences, MSOB 1265 Welch Road, Stanford, CA, 94305, USA. mrcullen@stanford.edu.
authors.forename: Mark R
authors.initials: MR
authors.lastname: Cullen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D003430:Cross-Sectional Studies; D006304:Health Status; D006801:Humans; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D008875:Middle Aged; D009042:Motivation; D010415:Pensions / Q000706:statistics & numerical data; D012179:Retirement / Q000706:statistics & numerical data*; D011795:Surveys and Questionnaires; D014481:United States