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Measuring Income of the Aged in Household Surveys: Evidence from Linked Administrative Records

Author

Listed:
  • Adam Bee
  • Irena Dushi
  • Joshua Mitchell
  • Brad Trenkamp

Abstract

Research has shown that household survey estimates of retirement income (defined benefit pensions and defined contribution account withdrawals) suffer from substantial underreporting which biases downward measures of financial well-being among the aged. Using data from both the redesigned 2016 Current Population Survey Annual Social and Economic Supplement (CPS ASEC) and the Health and Retirement Study (HRS), each matched with administrative records, we examine to what extent underreporting of retirement income affects key statistics such as reliance on Social Security benefits and poverty among the aged. We find that underreporting of retirement income is still prevalent in the CPS ASEC. While the HRS does a better job than the CPS ASEC in terms of capturing retirement income, it still falls considerably short compared to administrative records. Consequently, the relative importance of Social Security income remains overstated in household surveys�53 percent of elderly beneficiaries in the CPS ASEC and 49 percent in the HRS rely on Social Security for the majority of their incomes compared to 42 percent in the linked administrative data. The poverty rate for those aged 65 and over is also overstated�8.8 percent in the CPS ASEC and 7.4 percent in the HRS compared to 6.4 percent in the linked administrative data. Our results illustrate the effects of using alternative data sources in producing key statistics from the Social Security Administration�s Income of the Aged publication.

Suggested Citation

  • Adam Bee & Irena Dushi & Joshua Mitchell & Brad Trenkamp, 2024. "Measuring Income of the Aged in Household Surveys: Evidence from Linked Administrative Records," Working Papers 24-32, Center for Economic Studies, U.S. Census Bureau.
    • Handle: RePEc:cen:wpaper:24-32
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    References listed on IDEAS

    as
    1. Celhay, Pablo & Meyer, Bruce D. & Mittag, Nikolas, 2024. "What leads to measurement errors? Evidence from reports of program participation in three surveys," Journal of Econometrics, Elsevier, vol. 238(2).
    2. Bruce D. Meyer & Nikolas Mittag, 2019. "Using Linked Survey and Administrative Data to Better Measure Income: Implications for Poverty, Program Effectiveness, and Holes in the Safety Net," American Economic Journal: Applied Economics, American Economic Association, vol. 11(2), pages 176-204, April.
    3. Robert Argento & Victoria L. Bryant & John Sabelhaus, 2015. "Early Withdrawals From Retirement Accounts During The Great Recession," Contemporary Economic Policy, Western Economic Association International, vol. 33(1), pages 1-16, January.
    4. Gustman, Alan L. & Steinmeier, Thomas L. & Tabatabai, Nahid, 2014. "Mismeasurement of pensions before and after retirement: the mystery of the disappearing pensions with implications for the importance of Social Security as a source of retirement support," Journal of Pension Economics and Finance, Cambridge University Press, vol. 13(1), pages 1-26, January.
    5. repec:mpr:mprres:6195 is not listed on IDEAS
    6. Gustman, Alan L. & Steinmeier, Thomas L. & Tabatabai, Nahid, 2010. "Pensions in the Health and Retirement Study," Economics Books, Harvard University Press, number 9780674048669, Spring.
    7. Anqi Chen & Alicia H. Munnell & Geoffrey T. Sanzenbacher, 2018. "How Much Income Do Retirees Actually Have? Evaluating the Evidence from Five National Datasets," Working Papers, Center for Retirement Research at Boston College wp2018-14, Center for Retirement Research.
    8. Dushi, Irena & Honig, Marjorie, 2015. "How much do respondents in the health and retirement study know about their contributions to tax-deferred contribution plans? A cross-cohort comparison," Journal of Pension Economics and Finance, Cambridge University Press, vol. 14(3), pages 203-239, July.
    9. Michael Hurd & F. Thomas Juster & James P. Smith, 2003. "Enhancing the Quality of Data on Income: Recent Innovations from the HRS," Journal of Human Resources, University of Wisconsin Press, vol. 38(3).
    10. Barry P. Bosworth & Gary Burtless & Sarah E. Anders, 2007. "Capital Income Flows and the Relative Well-Being of America's Aged Population," Working Papers, Center for Retirement Research at Boston College wp2007-21, Center for Retirement Research, revised Dec 2007.
    11. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    12. Meyer, Bruce D. & Mittag, Nikolas, 2021. "An empirical total survey error decomposition using data combination," Journal of Econometrics, Elsevier, vol. 224(2), pages 286-305.
    13. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    14. Jesse Bricker & Gary V. Engelhardt, 2007. "Measurement Error in Earnings Data in the Health and Retirement Study," Working Papers, Center for Retirement Research at Boston College wp2007-16, Center for Retirement Research, revised Oct 2007.
    15. Dushi, Irena & Iams, Howard, 2017. "Reporting accuracy of Social Security benefits and its implications in the Health and Retirement Study," Journal of Economic and Social Measurement, IOS Press, issue 3-4, pages 271-292.
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    More about this item

    Keywords

    measurement error; household surveys; retirement income; Social Security; poverty; aging;
    All these keywords.

    JEL classification:

    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household
    • H55 - Public Economics - - National Government Expenditures and Related Policies - - - Social Security and Public Pensions
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • J14 - Labor and Demographic Economics - - Demographic Economics - - - Economics of the Elderly; Economics of the Handicapped; Non-Labor Market Discrimination
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies
    • R29 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Other

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