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Improved Wealth Measures in the Health and Retirement Study Asset Reconciliation and Cross-Wave Imputation

Author

Listed:
  • Michael D. Hurd
  • Erik Meijer
  • Michael B. Moldoff
  • Susann Rohwedder

Abstract

In this report, we present improved wealth measures for the Health and Retirement Study (HRS), which aim to reduce the effect of observation error on wealth levels and changes in wealth. The new wealth measures take account of the asset verification section in the HRS and use cross-wave information, most notably the value of the same asset in adjacent waves, in the imputation models, so imputed values better preserve serial correlation in the asset values. We document how we dealt with several methodological challenges in the implementation of these improvements. The corrections from the asset verification data reduce the standard deviations of wave-to-wave changes by substantial amounts (up to 57 percent for total wealth). The most important effect of the cross-wave imputations is a considerable reduction of the number of spikes and trenches (large changes in value followed by large changes back).

Suggested Citation

  • Michael D. Hurd & Erik Meijer & Michael B. Moldoff & Susann Rohwedder, 2016. "Improved Wealth Measures in the Health and Retirement Study Asset Reconciliation and Cross-Wave Imputation," Working Papers WR-1150, RAND Corporation.
    • Handle: RePEc:ran:wpaper:wr-1150
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    Citations

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    Cited by:

    1. Ghimire, Umesh, 2022. "The Impact of Health on Wealth: Empirical Evidence," MPRA Paper 113850, University Library of Munich, Germany.
    2. Fabrizio Mazzonna & Franco Peracchi, 2024. "Are Older People Aware of Their Cognitive Decline? Misperception and Financial Decision-Making," Journal of Political Economy, University of Chicago Press, vol. 132(6), pages 1793-1830.
    3. Fabrizio Mazzonna & Franco Peracchi, 2018. "Self-assessed cognitive ability and financial wealth: Are people aware of their cognitive decline?," EIEF Working Papers Series 1808, Einaudi Institute for Economics and Finance (EIEF), revised Sep 2018.
    4. Umesh Ghimire, 2020. "The Impact of Health on Wealth: Empirical Evidence," Working papers 2020-19, University of Connecticut, Department of Economics.
    5. Jiang, Nan & Kaushal, Neeraj, 2020. "How children's education affects caregiving: Evidence from parent’s last years of life," Economics & Human Biology, Elsevier, vol. 38(C).
    6. Daniel Barczyk & Sean Fahle & Matthias Kredler, 2023. "Save, Spend, or Give? A Model of Housing, Family Insurance, and Savings in Old Age," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(5), pages 2116-2187.
    7. Emily Joy Nicklett & Matthew C. Lohman & Matthew Lee Smith, 2017. "Neighborhood Environment and Falls among Community-Dwelling Older Adults," IJERPH, MDPI, vol. 14(2), pages 1-15, February.
    8. Michael D. Hurd & Erik Meijer & Philip Pantoja & Susann Rohwedder, 2018. "Addition to the RAND HRS Longitudinal Files: IRA Withdrawals in the HRS, 2000 to 2014," Working Papers wp388, University of Michigan, Michigan Retirement Research Center.
    9. Poterba, James & Venti, Steven & Wise, David A., 2018. "Longitudinal determinants of end-of-life wealth inequality," Journal of Public Economics, Elsevier, vol. 162(C), pages 78-88.

    More about this item

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D31 - Microeconomics - - Distribution - - - Personal Income and Wealth Distribution
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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