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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. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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).
    3. 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.
    4. 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.
    5. Stocké, Volker, 2004. "Attitudes toward surveys, attitude accessibility and the effect on respondents' susceptibility to nonresponse," Papers 04-30, Sonderforschungsbreich 504.
    6. 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.

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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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