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Respondent Behavior in Panel Studies: A Case Study for Income-Nonresponse by Means of the German Socio-Economic Panel (GSOEP)

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  • Jörg-Peter Schräpler

Abstract

Many validation studies deal with item-nonresponse and measurement error in earnings data. In this paper we explore motives of respondents for the failure to reveal earnings using the German Socio-Economic Panel (GSOEP). GSOEP collects socio-economic information of private households in the Federal Republic of Germany. We explain the evolution of income-nonresponse in the GSOEP and demonstrate the importance of a discrimination between refusing the income-statement or don't know.

Suggested Citation

  • Jörg-Peter Schräpler, 2002. "Respondent Behavior in Panel Studies: A Case Study for Income-Nonresponse by Means of the German Socio-Economic Panel (GSOEP)," Discussion Papers of DIW Berlin 299, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp299
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    References listed on IDEAS

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    1. Bollinger, Christopher R & David, Martin H, 2001. "Estimation with Response Error and Nonresponse: Food-Stamp Participation in the SIPP," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 129-141, April.
    2. Harvey Goldstein & Jon Rasbash, 1996. "Improved Approximations for Multilevel Models with Binary Responses," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 159(3), pages 505-513, May.
    3. Pamela Campanelli & Colm O'Muircheartaigh, 1999. "Interviewers, Interviewer Continuity, and Panel Survey Nonresponse," Quality & Quantity: International Journal of Methodology, Springer, vol. 33(1), pages 59-76, February.
    4. Brownstone, David & Valletta, Robert G, 1996. "Modeling Earnings Measurement Error: A Multiple Imputation Approach," The Review of Economics and Statistics, MIT Press, vol. 78(4), pages 705-717, November.
    5. C. O'Muircheartaigh & P. Campanelli, 1999. "A multilevel exploration of the role of interviewers in survey non‐response," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 437-446.
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    Cited by:

    1. Michal Myck & Mateusz Najsztub, 2015. "Data and Model Cross-validation to Improve Accuracy of Microsimulation Results: Estimates for the Polish Household Budget Survey," International Journal of Microsimulation, International Microsimulation Association, vol. 8(1), pages 33-66.
    2. Stocké, Volker, 2004. "Attitudes Toward Surveys, Attitude Accessibility and the Effect on Respondents� Susceptibility to Nonresponse," Sonderforschungsbereich 504 Publications 04-30, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    3. Jörg-Peter Schräpler & Jürgen Schupp & Gert G. Wagner, 2010. "Individual and Neighborhood Determinants of Survey Nonresponse: An Analysis Based on a New Subsample of the German Socio-Economic Panel (SOEP), Microgeographic Characteristics and Survey-Based Intervi," SOEPpapers on Multidisciplinary Panel Data Research 288, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Stocké, Volker, 2004. "Attitudes toward surveys, attitude accessibility and the effect on respondents' susceptibility to nonresponse," Papers 04-30, Sonderforschungsbreich 504.
    5. Lynn, Peter & Kaminska, Olena & Goldstein, Harvey, 2011. "Panel attrition: how important is it to keep the same interviewer?," ISER Working Paper Series 2011-02, Institute for Social and Economic Research.
    6. Joachim R. Frick & Markus M. Grabka, 2003. "Missing Income Data in the German SOEP: Incidence, Imputation and its Impact on the Income Distribution," Discussion Papers of DIW Berlin 376, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    Respondent behavior; Interviewer effects; Item-Nonresponse; Panel analysis; Multilevel modeling;
    All these keywords.

    JEL classification:

    • 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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