IDEAS home Printed from https://ideas.repec.org/p/ran/wpaper/wr-1150.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://www.rand.org/content/dam/rand/pubs/working_papers/WR1100/WR1150/RAND_WR1150.pdf
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Umesh Ghimire, 2020. "The Impact of Health on Wealth: Empirical Evidence," Working papers 2020-19, University of Connecticut, Department of Economics.
    2. 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.
    3. Ghimire, Umesh, 2022. "The Impact of Health on Wealth: Empirical Evidence," MPRA Paper 113850, University Library of Munich, Germany.
    4. 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.
    5. 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.
    6. Mazzonna, Fabrizio & Peracchi, Franco, 2020. "Are Older People Aware of Their Cognitive Decline? Misperception and Financial Decision Making," IZA Discussion Papers 13725, Institute of Labor Economics (IZA).
    7. 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.
    8. 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).
    9. 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.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ran:wpaper:wr-1150. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Benson Wong (email available below). General contact details of provider: https://edirc.repec.org/data/lpranus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.