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Item nonresponse to financial questions in household surveys: An experimental study of interviewer and mode effects

Author

Listed:
  • Lothar Essig
  • Joachim Winter

    (Munich Center for the Economics of Aging (MEA))

Abstract
We analyze nonresponse to questions on financial items such as income and asset holdings in household surveys using data from a controlled field experiment. As part of the SAVE study, a representative survey conducted in Germany in 2001, questions on household income and financial assets were administered using different modes (personal interview vs. drop-off questionnaire). The data also allow to investigate the influence of interviewer characteristics on nonresponse. Our results are in line with predictions derived from models of survey response behavior that have been developed in survey research and social psychology.

Suggested Citation

  • Lothar Essig & Joachim Winter, 2003. "Item nonresponse to financial questions in household surveys: An experimental study of interviewer and mode effects," MEA discussion paper series 03039, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    • Handle: RePEc:mea:meawpa:03039
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    References listed on IDEAS

    as
    1. Axel Borsch-Supan & Lothar Essig, 2003. "Household Saving in Germany: Results of the first SAVE study," NBER Working Papers 9902, National Bureau of Economic Research, Inc.
    2. Regina Riphahn & Oliver Serfling, 2005. "Item non-response on income and wealth questions," Empirical Economics, Springer, vol. 30(2), pages 521-538, September.
    3. Philipson, Tomas, 2001. "Data Markets, Missing Data, and Incentive Pay," Econometrica, Econometric Society, vol. 69(4), pages 1099-1111, July.
    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. Winter, Joachim, 0000. "Bracketing effects in categorized survey questions and the measurement of economic quantities," Sonderforschungsbereich 504 Publications 02-35, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    6. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    7. Horowitz, Joel L & Manski, Charles F, 1995. "Identification and Robustness with Contaminated and Corrupted Data," Econometrica, Econometric Society, vol. 63(2), pages 281-302, March.
    8. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    9. Tomas Philipson, 1997. "Data Markets and the Production of Surveys," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 64(1), pages 47-72.
    10. Philipson, Tomas & Malani, Anup, 1999. "Measurement errors: A principal investigator-agent approach," Journal of Econometrics, Elsevier, vol. 91(2), pages 273-298, August.
    11. Essig, Lothar, 2004. "Household Saving in Germany:," Sonderforschungsbereich 504 Publications 05-23, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    12. Michael D. Hurd & Daniel McFadden & Harish Chand & Li Gan & Angela Menill & Michael Roberts, 1998. "Consumption and Savings Balances of the Elderly: Experimental Evidence on Survey Response Bias," NBER Chapters, in: Frontiers in the Economics of Aging, pages 353-392, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Michael Ziegelmeyer, 2013. "Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 49-76, January.
    2. Schunk, Daniel, 2007. "What determines the saving behavior of German households? : an examination of saving motives and saving decisions," Papers 07-10, Sonderforschungsbreich 504.
    3. Börsch-Supan, Axel & Reil-Held, Anette & Schunk, Daniel, 2007. "The savings behaviour of German households: First Experiences with state promoted private pensions," MEA discussion paper series 07136, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    4. 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.
    5. Stocké, Volker & Stark, Tobias, 2006. "Trust in surveys and the respondents' susceptibility to item nonresponse," Papers 06-06, Sonderforschungsbreich 504.
    6. Thomas F. Crossley & Joachim K. Winter, 2014. "Asking Households about Expenditures: What Have We Learned?," NBER Chapters, in: Improving the Measurement of Consumer Expenditures, pages 23-50, National Bureau of Economic Research, Inc.
    7. Pfarr, Christian & Schmid, Andreas, 2013. "The political economics of social health insurance: the tricky case of individuals’ preferences," MPRA Paper 44534, University Library of Munich, Germany.
    8. Essig, Lothar, 2005. "Methodological aspects of the SAVE data set," Papers 05-17, Sonderforschungsbreich 504.
    9. 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.
    10. Pfarr, Christian, 2012. "Meltzer-Richard and social mobility hypothesis: revisiting the income-redistribution nexus using German choice data," MPRA Paper 43325, University Library of Munich, Germany.
    11. Daniel Schunk, 2008. "A Markov chain Monte Carlo algorithm for multiple imputation in large surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 101-114, February.
    12. Lothar Essig, 2005. "Methodological aspects of the SAVE data set," MEA discussion paper series 05080, Munich Center for the Economics of Aging (MEA) at the Max Planck Institute for Social Law and Social Policy.
    13. Schunk, Daniel, 2007. "A Markov Chain Monte Carlo Multiple Imputation Procedure for Dealing with Item Nonresponse in the German SAVE Survey," Sonderforschungsbereich 504 Publications 07-06, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    14. Gabriela Fonseca & Semira Tagliabue & Carla Crespo & Ana Paula Relvas, 2021. "Missingness Amount and Mechanisms in Family Economic Stress Research: Mapping (Non)answers to Economic-Related Variables of Fathers, Mothers, and Emerging Adult Children," Journal of Family and Economic Issues, Springer, vol. 42(3), pages 397-412, September.
    15. Bruckmeier, Kerstin & Müller, Gerrit & Riphahn, Regina T., 2015. "Survey misreporting of welfare receipt—Respondent, interviewer, and interview characteristics," Economics Letters, Elsevier, vol. 129(C), pages 103-107.
    16. Jörg-Peter Schräpler, 2004. "Respondent Behavior in Panel Studies," Sociological Methods & Research, , vol. 33(1), pages 118-156, August.
    17. Beatrice Scheubel & Joachim Winter, 2008. "Rente mit 67: Wie lange die Deutschen arbeiten können und wollen," ifo Schnelldienst, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 61(01), pages 26-32, January.
    18. repec:mea:meawpa:12258 is not listed on IDEAS
    19. Schunk Daniel, 2009. "What Determines Household Saving Behavior: An Examination of Saving Motives and Saving Decisions 06.01.2009," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 229(4), pages 467-491, August.

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    • 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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