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Effects of Attrition and Non-Response in the Health and Retirement Study

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Author Info

  • Arie Kapteyn
  • Pierre-Carl Michaud
  • James P. Smith
  • Arthur van Soest

Abstract

The authors study the effect of attrition and other forms of non-response on the representativity over time of the Health and Retirement Study (HRS) sample born 1931-1941; the sample was initially drawn in 1992. Although some baseline characteristics of respondents do appear correlated with non-response over time, the 2002 sample of respondents does not appear to suffer significantly from selection on observables, except for race and ethnicity; for these two observables, longitudinal weights based on the Current Population Survey (CPS) can be used and are provided with the data set. They attribute this lack of selection to the fact that attritors who differ most eventually come back to the survey in waves prior to 2002. Although this allows cross-sections to remain fairly representative in later waves, it suggests that longitudinal analysis should use the unbalanced sample rather than the balanced sample of those interviewed in all waves. Individuals who attrit but who are recruited back into the survey are very different from those who are permanent attritors to the HRS. Finally, they investigate the selective nature of the decision of respondents to grant HRS permission to access their Social Security records and of the non-response introduced by employers of pension policyholders not providing HRS with worker's Summary Plan Descriptions. They find that subsamples for which such information is available are selective on a number of dimensions, such as education and other socioeconomic status (SES) outcomes.

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Bibliographic Info

Paper provided by RAND Corporation Publications Department in its series Working Papers with number 407.

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Length: 42 pages
Date of creation: Jul 2006
Date of revision:
Handle: RePEc:ran:wpaper:407

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References

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  1. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
  2. Keisuke Hirano & Guido W. Imbens & Geert Ridder & Donald B. Rubin, 2001. "Combining Panel Data Sets with Attrition and Refreshment Samples," Econometrica, Econometric Society, vol. 69(6), pages 1645-1659, November.
  3. Daniel H. Hill & Robert J. Willis, 2001. "Reducing Panel Attrition: A Search for Effective Policy Instruments," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 416-438.
  4. John Fitzgerald & Peter Gottschalk & Robert Moffitt, 1998. "An Analysis of Sample Attrition in Panel Data: The Michigan Panel Study of income Dynamics," Economics Working Paper Archive 379, The Johns Hopkins University,Department of Economics.
  5. Juster, F. Thomas & Smith, James P. & Stafford, Frank, 1999. "The measurement and structure of household wealth," Labour Economics, Elsevier, vol. 6(2), pages 253-275, June.
  6. O. Attanasio & H. W. Hoynes, . "Differential mortality and wealth accumulation," Institute for Research on Poverty Discussion Papers 1079-96, University of Wisconsin Institute for Research on Poverty.
  7. Hausman, Jerry A & Wise, David A, 1979. "Attrition Bias in Experimental and Panel Data: The Gary Income Maintenance Experiment," Econometrica, Econometric Society, vol. 47(2), pages 455-73, March.
  8. Steven Haider & Gary Solon, 2000. "Non Random Selection in the HRS Social Security Earnings Sample," Working Papers 00-01, RAND Corporation Publications Department.
  9. Lee A. Lillard & Constantijn W. A. Panis, 1998. "Panel Attrition from the Panel Study of Income Dynamics: Household Income, Marital Status, and Mortality," Journal of Human Resources, University of Wisconsin Press, vol. 33(2), pages 437-457.
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Cited by:
  1. John L. Czajka & Gabrielle Denmead, 2008. "Income Data for Policy Analysis: A Comparative Assessment of Eight Surveys," Mathematica Policy Research Reports 6195, Mathematica Policy Research.
  2. P.-C. Michaud & D. Goldman & D. Lakdawalla & Y. Zheng & A. Gailey, 2009. "Understanding the Economic Consequences of Shifting Trends in Population Health," Social and Economic Dimensions of an Aging Population Research Papers 255, McMaster University.
  3. Tobias Stucki, 2009. "How Long Do External Capital Constraints Matter?," KOF Working papers 09-241, KOF Swiss Economic Institute, ETH Zurich.
  4. Joachim R. Frick & Markus M. Grabka & Olaf Groh-Samberg, 2010. "Dealing with Incomplete Household Panel Data in Inequality Research," Discussion Papers of DIW Berlin 991, DIW Berlin, German Institute for Economic Research.
  5. Slade, Alexander N., 2012. "Health investment decisions in response to diabetes information in older Americans," Journal of Health Economics, Elsevier, vol. 31(3), pages 502-520.
  6. Dave, Dhaval & Saffer, Henry, 2008. "Alcohol demand and risk preference," Journal of Economic Psychology, Elsevier, vol. 29(6), pages 810-831, December.
  7. Xiaoyan Li & Nicole Maestas, 2008. "Does the Rise in the Full Retirement Age Encourage Disability Benefits Applications? Evidence from the Health and Retirement Study," Working Papers wp198, University of Michigan, Michigan Retirement Research Center.

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