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Assessing the bias due to non-coverage of residential movers in the German microcensus panel: an evaluation using data from the socio-economic panel

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  • Rendtel, Ulrich
  • Basic, Edin
Abstract
The German Microcensus (MC) is a large scale rotating panel survey over three years. The MC is attractive for longitudinal analysis over the entire participation duration because of the mandatory participation and the very high case numbers (about 200 thousand respondents). However, as a consequence of the area sampling that is used for the MC , residential mobility is not covered and consequently statistical information at the new residence is lacking in theMCsample. This raises the question whether longitudinal analyses, like transitions between labour market states, are biased and how different methods perform that promise to reduce such a bias. Based on data of the German Socio-Economic Panel (SOEP), which covers residential mobility, we analysed the effects of missing data of residential movers by the estimation of labour force flows. By comparing the results from the complete SOEP sample and the results from the SOEP, restricted to the non-movers, we concluded that the non-coverage of the residential movers can not be ignored in Rubin's sense. With respect to correction methods we analysed weighting by inverse mobility scores and loglinear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60 percent whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80 percent. The estimation of loglinear models for nonignorable nonresponse leads to very unstable results.

Suggested Citation

  • Rendtel, Ulrich & Basic, Edin, 2007. "Assessing the bias due to non-coverage of residential movers in the German microcensus panel: an evaluation using data from the socio-economic panel," Discussion Papers 2007/6, Free University Berlin, School of Business & Economics.
    • Handle: RePEc:zbw:fubsbe:20076
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    References listed on IDEAS

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    1. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 38(2), pages 112-134.
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    3. Miller M. E. & Ten Have T. R. & Reboussin B. A. & Lohman K. K. & Rejeski W.J., 2001. "A Marginal Model for Analyzing Discrete Outcomes From Longitudinal Surveys With Outcomes Subject to Multiple-Cause Nonresponse," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 844-857, September.
    4. Andrew J. Copas & Vern T. Farewell & Catherine H. Mercer & Guiqing Yao, 2004. "The sensitivity of estimates of the change in population behaviour to realistic changes in bias in repeated surveys," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(4), pages 579-595, November.
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    Cited by:

    1. Sören Pannier & Ulrich Rendtel & Hartmut Gerks, 2020. "Die Prognose von Studienerfolg und Studienabbruch auf Basis von Umfrage- und administrativen Prüfungsdaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 14(3), pages 225-266, December.
    2. Rendtel, Ulrich & Alho, Juha M., 2022. "On the fade-away of an initial bias in longitudinal surveys," Discussion Papers 2022/4, Free University Berlin, School of Business & Economics.
    3. Walter Krämer, 2019. "Interview mit Ulrich Rendtel," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 13(2), pages 179-187, September.

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    More about this item

    Keywords

    Panel survey; labour market analysis; residential mobility; non-coverage bias; log-linear modelling; inverse probability weighting;
    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
    • J69 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Other

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