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Item Non-Response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective

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  • Frick, Joachim R.

    (DIW Berlin)

  • Grabka, Markus M.

    ()
    (DIW Berlin)

Abstract

Using data on annual individual labor income from three representative panel datasets (German SOEP, British BHPS, Australian HILDA) we investigate a) the selectivity of item non-response (INR) and b) the impact of imputation as a prominent post-survey means to cope with this type of measurement error on prototypical analyses (earnings inequality, mobility and wage regressions) in a cross-national setting. Given the considerable variation of INR across surveys as well as the varying degree of selectivity build into the missing process, there is substantive and methodological interest in an improved harmonization of (income) data production as well as of imputation strategies across surveys. All three panels make use of longitudinal information in their respective imputation procedures, however, there are marked differences in the implementation. Firstly, although the probability of INR is quantitatively similar across countries, our empirical investigation identifies cross-country differences with respect to the factors driving INR: survey-related aspects as well as indicators accounting for variability and complexity of labor income composition appear to be relevant. Secondly, longitudinal analyses yield a positive correlation of INR on labor income data over time and provide evidence of INR being a predictor of subsequent unit-non-response, thus supporting the “cooperation continuum” hypothesis in all three panels. Thirdly, applying various mobility indicators there is a robust picture about earnings mobility being significantly understated using information from completely observed cases only. Finally, regression results for wage equations based on observed (“complete case analysis”) vs. all cases and controlling for imputation status, indicate that individuals with imputed incomes, ceteris paribus, earn significantly above average in SOEP and HILDA, while this relationship is negative using BHPS data. However, once applying the very same imputation procedure used for HILDA and SOEP, namely the “row-and-column-imputation” approach suggested by Little & Su (1989), also to BHPS-data, this result is reversed, i.e., individuals in the BHPS whose income has been imputed earn above average as well. In our view, the reduction in cross-national variation resulting from sensitivity to the choice of imputation approaches underscores the importance of investing more in the improved cross-national harmonization of imputation techniques.

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Bibliographic Info

Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 3043.

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Length: 35 pages
Date of creation: Sep 2007
Date of revision:
Publication status: published in: Janet A. Harkness et al. (eds): Survey Methods in Multicultural, Multinational, and Multiregional Contexts, Wiley & Sons, 2010
Handle: RePEc:iza:izadps:dp3043

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Keywords: item non-response; income inequality; imputation; income mobility; panel data; SOEP; BHPS; HILDA;

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References

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  1. Regina Riphahn & Oliver Serfling, 2005. "Item non-response on income and wealth questions," Empirical Economics, Springer, Springer, vol. 30(2), pages 521-538, 09.
  2. Susanne Rässler & Regina Riphahn, 2006. "Survey item nonresponse and its treatment," AStA Advances in Statistical Analysis, Springer, Springer, vol. 90(1), pages 217-232, March.
  3. Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP): Scope, Evolution and Enhancements," SOEPpapers on Multidisciplinary Panel Data Research 1, DIW Berlin, The German Socio-Economic Panel (SOEP).
  4. Daniel H. Hill & Robert J. Willis, 2001. "Reducing Panel Attrition: A Search for Effective Policy Instruments," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 416-438.
  5. Heckman, James J, 1979. "Sample Selection Bias as a Specification Error," Econometrica, Econometric Society, Econometric Society, vol. 47(1), pages 153-61, January.
  6. Cheti Nicoletti & Franco Peracchi, 2006. "The effects of income imputation on microanalyses: evidence from the European Community Household Panel," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 625-646.
  7. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, American Statistical Association, vol. 6(3), pages 287-96, July.
  8. Denise Hawkes & Ian Plewis, 2006. "Modelling non-response in the National Child Development Study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 479-491.
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Cited by:
  1. Joachim R. Frick & Markus M. Grabka & Olaf Groh-Samberg, 2010. "Dealing with Incomplete Household Panel Data in Inequality Research," SOEPpapers on Multidisciplinary Panel Data Research 290, DIW Berlin, The German Socio-Economic Panel (SOEP).
  2. Katja Landau & Stephan Klasen & Walter Zucchini, 2012. "Measuring Vulnerability to Poverty Using Long-Term Panel Data," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers 118, Courant Research Centre PEG.
  3. Michael Ziegelmeyer, 2013. "Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey," AStA Advances in Statistical Analysis, Springer, Springer, vol. 97(1), pages 49-76, January.
  4. Ute Hanefeld & Jürgen Schupp, 2008. "The First Six Waves of SOEP: The Panel Project in the Years 1983 to 1989," SOEPpapers on Multidisciplinary Panel Data Research 146, DIW Berlin, The German Socio-Economic Panel (SOEP).
  5. Frick, Joachim R. & Jenkings, Stephen P. & Lillard, Dean R. & Lipps, Oliver & Wooden, Mark, 2007. "The Cross-National Equivalent File (CNEF) and Its Member Country Household Panel Studies," EconStor Open Access Articles, ZBW - German National Library of Economics, pages 627-654.
  6. Joachim R. Frick & Kristina Krell, 2010. "Measuring Income in Household Panel Surveys for Germany: A Comparison of EU-SILC and SOEP," SOEPpapers on Multidisciplinary Panel Data Research 265, DIW Berlin, The German Socio-Economic Panel (SOEP).
  7. S. Anger & F. Frick & J. Goebel & M. Grabka & O. Groh-Samberg & H. Haas & E. Holst & P. Krause & M. Kroh & H. Lohmann & J. Schupp & I. Sieber & T. Siedler & C. Schmitt & C. K. Spieß & I. Tucci & G. G, 2009. "Developing SOEPsurvey and SOEPservice: The (Near) Future of the German Socio-Economic Panel Study (SOEP)," SOEPpapers on Multidisciplinary Panel Data Research 155, DIW Berlin, The German Socio-Economic Panel (SOEP).
  8. Carsten Kuchler & Martin Spiess, 2009. "The data quality concept of accuracy in the context of publicly shared data sets," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer, Springer, vol. 3(1), pages 67-80, June.

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