IDEAS home Printed from https://ideas.repec.org/p/ran/wpaper/wr-668.html
   My bibliography  Save this paper

Mode and Context Effects in Measuring Household Assets

Author

Listed:
  • Arthur Van Soest
  • Arie Kapteyn

Abstract

Differences in answers in Internet and traditional surveys can be due to selection, mode, or context effects. The authors exploit unique experimental data to analyze mode and context effects controlling for arbitrary selection. The Health and Retirement Study (HRS) surveys a random sample of the US 50+ population, with CAPI or CATI core interviews once every two years. In 2003 and 2005, random samples were drawn from HRS respondents in 2002 and 2004 willing and able to participate in an Internet interview. Comparing core and Internet survey answers of the same people, the authors analyze mode and context effects, controlling for selection. They focus on household assets, for which mode effects in Internet surveys have rarely been studied. They find some large differences between the first Internet survey and the other three surveys which they interpret as a context and question wording effect rather than a pure mode effect.

Suggested Citation

  • Arthur Van Soest & Arie Kapteyn, 2009. "Mode and Context Effects in Measuring Household Assets," Working Papers WR-668, RAND Corporation.
  • Handle: RePEc:ran:wpaper:wr-668
    as

    Download full text from publisher

    File URL: https://www.rand.org/content/dam/rand/pubs/working_papers/2009/RAND_WR668.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Matthias Schonlau & Arthur van Soest & Arie Kapteyn & Mick Couper, 2009. "Selection Bias in Web Surveys and the Use of Propensity Scores," Sociological Methods & Research, , vol. 37(3), pages 291-318, February.
    2. Guiso, Luigi & Jappelli, Tullio, 2000. "Household Portfolios in Italy," CEPR Discussion Papers 2549, C.E.P.R. Discussion Papers.
    3. Berrens, Robert P. & Bohara, Alok K. & Jenkins-Smith, Hank & Silva, Carol & Weimer, David L., 2003. "The Advent of Internet Surveys for Political Research: A Comparison of Telephone and Internet Samples," Political Analysis, Cambridge University Press, vol. 11(1), pages 1-22, January.
    4. F. Thomas Juster & James P. Smith, 2004. "Improving the Quality of Economic Data: Lessons from the HRS and AHEAD," Labor and Demography 0402010, University Library of Munich, Germany.
    5. Couper, Mick P. & Kapteyn, Arie & Schonlau, Matthias & Winter, Joachim, 2007. "Noncoverage and nonresponse in an Internet survey," Munich Reprints in Economics 20093, University of Munich, Department of Economics.
    6. Matthias Schonlau & Arthur van Soest & Arie Kapteyn & Mick Couper, 2009. "Selection Bias in Web Surveys and the Use of Propensity Scores," Sociological Methods & Research, , vol. 37(3), pages 291-318, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Geary Working Paper: Van Soest and Kapteyn on Mode and Context Effects
      by Liam Delaney in Geary Behaviour Centre on 2009-12-22 06:37:00

    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. Arthur van Soest & Arie Kapteyn, 2009. "Mode and Context Effects in Measuring Household Assets," Working Papers 200949, Geary Institute, University College Dublin.
    2. Grewenig, Elisabeth & Lergetporer, Philipp & Simon, Lisa & Werner, Katharina & Woessmann, Ludger, 2023. "Can internet surveys represent the entire population? A practitioners’ analysis," European Journal of Political Economy, Elsevier, vol. 78(C).
    3. Crossley, Thomas F. & Fisher, Paul & Low, Hamish, 2021. "The heterogeneous and regressive consequences of COVID-19: Evidence from high quality panel data," Journal of Public Economics, Elsevier, vol. 193(C).
    4. Hildebrand Sean, 2015. "Coerced Confusion? Local Emergency Policy Implementation After September 11," Journal of Homeland Security and Emergency Management, De Gruyter, vol. 12(2), pages 273-298, June.
    5. Grewenig, Elisabeth & Lergetporer, Philipp & Simon, Lisa & Werner, Katharina & Woessmann, Ludger, 2018. "Can Online Surveys Represent the Entire Population?," IZA Discussion Papers 11799, Institute of Labor Economics (IZA).
    6. Matthias Schonlau & Arthur van Soest & Arie Kapteyn & Mick Couper, 2009. "Selection Bias in Web Surveys and the Use of Propensity Scores," Sociological Methods & Research, , vol. 37(3), pages 291-318, February.
    7. John Y. Campbell, 2006. "Household Finance," Journal of Finance, American Finance Association, vol. 61(4), pages 1553-1604, August.
    8. Joanne W. Hsu & Brooke H. McFall, 2015. "Mode effects in mixed-mode economic surveys: Insights from a randomized experiment," Finance and Economics Discussion Series 2015-8, Board of Governors of the Federal Reserve System (U.S.).
    9. Dimitrios Christelis & Tullio Jappelli & Mario Padula, 2005. "Wealth and Portfolio Composition," CSEF Working Papers 132, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy.
    10. repec:aia:aiaswp:wp76 is not listed on IDEAS
    11. Randall Alan Cantrell & Amanda Stafford, 2013. "The introduction and development of the community-flow measurement instrument," Community Development, Taylor & Francis Journals, vol. 44(3), pages 305-322, July.
    12. Murray Rudd, 2011. "An Exploratory Analysis of Societal Preferences for Research-Driven Quality of Life Improvements in Canada," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 101(1), pages 127-153, March.
    13. Grossmann, Volker, 2008. "Risky human capital investment, income distribution, and macroeconomic dynamics," Journal of Macroeconomics, Elsevier, vol. 30(1), pages 19-42, March.
    14. Andreas Fagereng & Luigi Guiso & Davide Malacrino & Luigi Pistaferri, 2020. "Heterogeneity and Persistence in Returns to Wealth," Econometrica, Econometric Society, vol. 88(1), pages 115-170, January.
    15. Bertocchi, Graziella & Brunetti, Marianna & Torricelli, Costanza, 2011. "Marriage and other risky assets: A portfolio approach," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2902-2915, November.
    16. Borsch-Supan, Axel & Reil-Held, Anette & Rodepeter, Ralf & Schnabel, Reinhold & Winter, Joachim, 2001. "The German Savings Puzzle," Research in Economics, Elsevier, vol. 55(1), pages 15-38, March.
    17. Rosen, H.S.Harvey S. & Wu, Stephen, 2004. "Portfolio choice and health status," Journal of Financial Economics, Elsevier, vol. 72(3), pages 457-484, June.
    18. Luigi Guiso & Monica Paiella, 2008. "Risk Aversion, Wealth, and Background Risk," Journal of the European Economic Association, MIT Press, vol. 6(6), pages 1109-1150, December.
    19. Pelizzon, Loriana & Weber, Guglielmo, 2009. "Efficient portfolios when housing needs change over the life cycle," Journal of Banking & Finance, Elsevier, vol. 33(11), pages 2110-2121, November.
    20. Joachim Inkmann & Alexander Michaelides, 2012. "Can the Life Insurance Market Provide Evidence for a Bequest Motive?," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 79(3), pages 671-695, September.
    21. Hui Li & Robert P. Berrens & Alok K. Bohara & Hank C. Jenkins-Smith & Carol L. Silva & David L. Weimer, 2005. "Exploring the Beta Model Using Proportional Budget Information in a Contingent Valuation Study," Economics Bulletin, AccessEcon, vol. 17(8), pages 1-9.

    More about this item

    JEL classification:

    • C42 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Survey Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

    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-668. 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: 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.