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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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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 200000 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 the MC sample. 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. Similar problems occur also for other national Labour Force Surveys (LFS) which are rotating panels and do not cover residential mobility, see Clarke and Tate (2002).

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 log-linear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60% whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80%. The estimation of log-linear models for non-ignorable non-response leads to very unstable results.

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References

  1. Baker, S.G., Laird, N.M. (1988) Regression analysis for categorical variables with outcome subject to non-ignorable non-response. Journal of the American Statistical Association 83, 62–69

    Article  MathSciNet  Google Scholar 

  2. Basic, E., Marek, I., Rendtel, U. (2005) The German microcensus as a tool for longitudinal data analysis: An evaluation using SOEP data. Schmoller’s Jahrbuch – Journal of Applied Social Science Studies 125(1), 1–16

    Google Scholar 

  3. Behr, A., Bellgardt, E., Rendtel, U. (2005) Extent and determinants of panel attrition in the European Community household panel. European Sociological Review 21(5), 489–512

    Article  Google Scholar 

  4. Chambers, R.L., Welsh, A.H. (1993) Log-linear models for survey data with non-ignorable non-response. Journal of the Royal Statistical Society B 53, 157–170

    MathSciNet  Google Scholar 

  5. Clarke, P.S., Chambers, R. (1998) Estimating labour force gross flows from surveys subject to household-level nonignorable nonresponse. Survey Methodology 24, 123–129

    Google Scholar 

  6. Clarke, P.S., Tate, P.F. (1999) Methodological issues in the production and analysis of longitudinal data from the Labour Force Survey. GSS Methodology Series No 17. http://www.statistics.gov.uk/downloads/theme_other/GSSMethodology_No_17_v2.pdf

  7. Clarke, P.S., Tate, P.F. (2002) An Application of non-ignorable non-response models for gross flows estimation in the British Labour Force Survey. Australian & New Zealand Journal of Statistics 44, 413–425

    Article  MATH  MathSciNet  Google Scholar 

  8. Copas, A.J., Farewell, V.T., Mercer, C.H., Yao, G. (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 A 167, 579–595

    Article  MATH  MathSciNet  Google Scholar 

  9. Cox, D.R., Hinkley, D.V. (1974) Theoretical Statistics. Chapman and Hall, New York

    MATH  Google Scholar 

  10. Ehling, M., Rendtel, U. (2004) Harmonisation of Panel Surveys and Data Quality. Statistisches Bundesamt, Wiesbaden

    Google Scholar 

  11. German Statistical Office (2006) MC-Panel User’s Guide 1996–1999. http://www.destatis.de/download/mv/MZP9699_Handbuch.pdf

  12. Haisken-DeNew, J., Frick, J. (2005) Desktop Companion to the German Socio-Economic Panel Study (GSOEP). Version 8.0 – Updated to Wave 21, DIW, Berlin

  13. Hausman, J. (1978) Specification tests in econometrics. Econometrica 46, 1251–1271

    Article  MATH  MathSciNet  Google Scholar 

  14. Heidenreich, H.-J. (2002) Längsschnittdaten aus dem Mikrozensus. Basis für neue Analysemöglichkeiten. [Longitudinal Data on the Basis of the German Microcensus. A new data base for analysis.] Allgemeines Statistisches Archiv 86, 213–231

  15. Kroh, M., Spiess, M. (2006) Sample Sizes and Panel Attrition in the German Socio-Economic Panel (GSOEP) (1984 until 2005). Data Documentation 15, DIW, Berlin

  16. Liang, K.Y., Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika 73, 13–22

    Article  MATH  MathSciNet  Google Scholar 

  17. Little, R.J.A (1985) Nonresponse adjustments in longitudinal surveys: Models for categorical data. Bulletin of the International Statistical Institute 15, 1–15

    Google Scholar 

  18. Little, R.J.A, Rubin, D.B. (2002) Statistical Analysis with Missing Data, 2nd edn. Wiley, New York

    Google Scholar 

  19. Lundström, S., Särndal, C.-E. (2005) Estimation in Surveys with Nonresponse. Wiley, New York

    MATH  Google Scholar 

  20. Marek, I. (2005) Weighting adjustments in the presence of non-coverage due to residential mobility in the German Microcensus Panel. Arbeitspapier Nr. 10 Projektgruppe MZ Panel. http://www.destatis.de/download/d/mv/arbeitspapier10.pdf

  21. Miller, M.E., Ten Have, T.R., Reboussin, B.A., Lohman, K.K., Rejeski, W.J. (2001) A marginal model for analysing discrete outcomes from longitudinal surveys with outcomes subject to multiple-cause nonresponse. Journal of the American Statistical Association 96, 844–857

    Article  MATH  MathSciNet  Google Scholar 

  22. Rendtel, U. (1995) Panelausfälle und Panelrepräsentativität. Campus Verlag, Frankfurt

    Google Scholar 

  23. Robins, J., Rotnitzky, A., Zhao, L. (1995) Analysis of semi-parametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association 90, 106–121

    Article  MATH  MathSciNet  Google Scholar 

  24. Rubin, D.B. (1976) Inferences and missing data. Biometrika 63, 581–592

    Article  MATH  MathSciNet  Google Scholar 

  25. Särndal, C.-E., Swensson, B., Wretman, J. (1992) Model-assisted Survey Sampling. Springer, Berlin Heidelberg New York

    MATH  Google Scholar 

  26. Sisto, J. (2003) Attrition effects on the design based estimates of disposable household income. Chintex Working Paper No. 9. http://www.destatis.de/chintex/download/paper9.pdf

  27. Stasny, E.A. (1986) Estimating gross flows using panel data with nonresponse: An example from the Canadian Labour Force Survey. Journal of the American Statistical Association 81, 42–47

    Article  Google Scholar 

  28. Tate, P.F. (1999) Utilizing longitudinally linked data from the British Labour Force Survey. Survey Methodology 25, 99–103

    Google Scholar 

  29. Vermunt, J.K. (1997) LEM 1.0: A general program for the analysis of categorical data. Tilburg University. http://spitswww.uvt.nl/web/fsw/mto/lem/manual.pdf

  30. Wagner, M., Mulder, C.H. (2000) Wohneigentum im Lebenslauf: Kohortendynamik, Familiengründung und sozioökonomische Ressourcen. Zeitschrift für Soziologie 29, 44–59

    Google Scholar 

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  1. Institute of Statistics and Econometrics, Freie Universität Berlin, Garystr. 21, 14195, Berlin, Germany

    Edin Basic & Ulrich Rendtel

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  1. Edin Basic
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  2. Ulrich Rendtel
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Correspondence to Edin Basic.

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Basic, E., Rendtel, U. Assessing the bias due to non-coverage of residential movers in the German Microcensus Panel: an evaluation using data from the Socio-Economic Panel . AStA 91, 311–334 (2007). https://doi.org/10.1007/s10182-007-0030-5

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