IDEAS home Printed from https://ideas.repec.org/p/nbr/nberte/0219.html
   My bibliography  Save this paper

Anchoring Effects in the HRS: Experimental and Nonexperimental Evidence

Author

Listed:
  • Michael D. Hurd

Abstract

The Health and Retirement Study (HRS) and a number of other major household surveys use unfolding brackets to reduce item nonresponse. However, the initial entry point into a bracketing sequence is likely to act as an anchor or point of reference to the respondent: The distribution of responses among those bracketed would be influenced by the entry point. For example, when the initial entry point is high the distribution will be shifted to the right one to believe that holdings of the particular asset are greater than they truly are. This paper has two goals. The first is to analyze some experimental data on housing value from HRS wave 3 for anchoring effects. The second is to compare the distributions of assets in HRS waves 1 and 2 for evidence about any anchoring effects that may have been caused by changes in the entry points between the waves. Both the experimental data on housing values and the nonexperimental data from HRS waves 1 and 2 on assets show anchoring effects. The conclusion is that to estimate accurately wealth change in panel data sets, we need a method of correcting for anchoring effects such as random entry into the bracketing sequence.

Suggested Citation

  • Michael D. Hurd, 1998. "Anchoring Effects in the HRS: Experimental and Nonexperimental Evidence," NBER Technical Working Papers 0219, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberte:0219
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/t0219.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    2. Hilary W. Hoynes & Michael D. Hurd & Harish Chand, 1998. "Household Wealth of the Elderly under Alternative Imputation Procedures," NBER Chapters, in: Inquiries in the Economics of Aging, pages 229-257, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Hendrik Jürges, 2007. "Does ill health affect savings intentions? Evidence from SHARE," MEA discussion paper series 07139, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Brañas-Garza, Pablo & Ciacci, Riccardo & Ramírez, Ericka G. Rascón, 2022. "Anchors matter: Eliciting maternal expectations on educational outcomes," Journal of Economic Psychology, Elsevier, vol. 90(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Schunk, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," MEA discussion paper series 07121, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    2. Daniel Schunk, 2006. "The German SAVE Survey: Documentation and Methodology," MEA discussion paper series 06109, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    3. Michael Hurd & Elaine Reardon, 2003. "Real Wealth Changes from 1982 to 1991 Among the Newly Retired," Working Papers 03-15, RAND Corporation.
    4. Raymundo M. Campos-Vázquez, 2013. "Efectos de los ingresos no reportados en el nivel y tendencia de la pobreza laboral en México," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(2), pages 23-54, November.
    5. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    6. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.
    7. Michael D. Hurd & Daniel McFadden & Angela Merrill, 2001. "Predictors of Mortality among the Elderly," NBER Chapters, in: Themes in the Economics of Aging, pages 171-198, National Bureau of Economic Research, Inc.
    8. Hendrik Jürges & Lars Thiel & Tabea Bucher-Koenen & Johannes Rausch & Morten Schuth & Axel Börsch-Supan, 2014. "Health, Financial Incentives, and Early Retirement: Microsimulation Evidence for Germany," NBER Chapters, in: Social Security Programs and Retirement Around the World: Disability Insurance Programs and Retirement, pages 285-330, National Bureau of Economic Research, Inc.
    9. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    10. Timothy F. Harris & Aaron Yelowitz, 2017. "Nudging Life Insurance Holdings In The Workplace," Economic Inquiry, Western Economic Association International, vol. 55(2), pages 951-981, April.
    11. Christopher R. Bollinger & Barry T. Hirsch, 2010. "GDP & Beyond – die europäische Perspektive," RatSWD Working Papers 165, German Data Forum (RatSWD).
    12. Martin, Eisele & Zhu, Junyi, 2013. "Multiple imputation in a complex household survey - the German Panel on Household Finances (PHF): challenges and solutions," MPRA Paper 57666, University Library of Munich, Germany.
    13. Dang, Hai-Anh & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    14. Börsch-Supan, Axel & Coppola, Michela & Reil-Held, Anette, 1970. "Riester Pensions in Germany: Design, Dynamics, Targetting Success and Crowding-In," MEA discussion paper series 201220, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    15. Brownstone, David, 1997. "Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition," University of California Transportation Center, Working Papers qt2zd6w6hh, University of California Transportation Center.
    16. Zachary H. Seeskin, 2016. "Evaluating the Use of Commercial Data to Improve Survey Estimates of Property Taxes," CARRA Working Papers 2016-06, Center for Economic Studies, U.S. Census Bureau.
    17. Eric French & John Bailey Jones, 2011. "The Effects of Health Insurance and Self‐Insurance on Retirement Behavior," Econometrica, Econometric Society, vol. 79(3), pages 693-732, May.
    18. F. Di Lascio & Simone Giannerini & Alessandra Reale, 2015. "Exploring copulas for the imputation of complex dependent data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(1), pages 159-175, March.
    19. Ankita Patnaik & Jeffrey Hemmeter & Arif Mamun, "undated". "Promoting Readiness of Minors with Autism Spectrum Disorder: Evidence from a Randomized Controlled Trial," Mathematica Policy Research Reports a74c93d9bdce40709ad81cdbc, Mathematica Policy Research.
    20. Luci Ellis & Jeremy Lawson & Laura Roberts-Thomson, 2003. "Housing Leverage in Australia," RBA Research Discussion Papers rdp2003-09, Reserve Bank of Australia.

    More about this item

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • J26 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Retirement; Retirement Policies

    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:nbr:nberte:0219. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.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.