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

Author

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  • Joachim R. Frick
  • Markus M. Grabka
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 pre-dictor of subsequent unit-non-response, thus supporting the "cooperation continuum" hy-pothesis 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.

Suggested Citation

  • Joachim R. Frick & Markus M. Grabka, 2007. "Item Non-response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," Discussion Papers of DIW Berlin 736, DIW Berlin, German Institute for Economic Research.
    • Handle: RePEc:diw:diwwpp:dp736
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    References listed on IDEAS

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    Cited by:

    1. Christian Aßmann & Ariane Würbach & Solange Goßmann & Ferdinand Geissler & Anika Bela, 2017. "Nonparametric Multiple Imputation for Questionnaires with Individual Skip Patterns and Constraints: The Case of Income Imputation in the National Educational Panel Study," Sociological Methods & Research, , vol. 46(4), pages 864-897, November.
    2. Michael Ziegelmeyer, 2013. "Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(1), pages 49-76, January.
    3. 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.
    4. Ute Hanefeld & Jürgen Schupp, 2008. "Die ersten sechs Wellen des SOEP: das Panelprojekt in den ersten Jahren 1983-1989," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 77(3), pages 27-42.
    5. Brian Nolan & Ive Marx & Wiemer Salverda, 2011. "GINI DP 9: Comparable Indicators of Inequality Across Countries," GINI Discussion Papers 9, AIAS, Amsterdam Institute for Advanced Labour Studies.
    6. Mark Brooks & Rattiya S. Lippe & Hermann Waibel, 2020. "Comprehensive data quality studies as a component of poverty assessments," TVSEP Working Papers wp-019, Leibniz Universitaet Hannover, Institute for Environmental Economics and World Trade, Project TVSEP.
    7. Ziegelmeyer, Michael, 2009. "Documentation of the logical imputation using the panel structure of the 2003-2008 German SAVE Survey," Sonderforschungsbereich 504 Publications 08-41, Sonderforschungsbereich 504, Universität Mannheim;Sonderforschungsbereich 504, University of Mannheim.
    8. Frick, Joachim R. & Grabka, Markus M. & Groh-Samberg, Olaf, 2012. "Dealing With Incomplete Household Panel Data in Inequality Research," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 41(1), pages 89-123.
    9. 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).
    10. Stefaan Walgrave & Jeroen K. Joly, 2018. "Surveying individual political elites: a comparative three-country study," Quality & Quantity: International Journal of Methodology, Springer, vol. 52(5), pages 2221-2237, September.
    11. 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).
    12. 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;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 3(1), pages 67-80, June.
    13. Joachim R. Frick & Markus M. Grabka & Eva M. Sierminska, 2007. "Representative Wealth Data for Germany from the German SOEP: The Impact of Methodological Decisions around Imputation and the Choice of the Aggregation Unit," Discussion Papers of DIW Berlin 672, DIW Berlin, German Institute for Economic Research.
    14. S. Anger & J. R. Frick & J. Goebel & M. M. Grabka & O. Groh-Samberg & H. Haas & E. Holst & P. Krause & M. Kroh & H. Lohmann & R. Pischner & J. Schupp & I. Sieber & T. Siedler & C. Schmitt & C. K. Spie, 2008. "Zur Weiterentwicklung von SOEPsurvey und SOEPservice," Vierteljahrshefte zur Wirtschaftsforschung / Quarterly Journal of Economic Research, DIW Berlin, German Institute for Economic Research, vol. 77(3), pages 157-177.
    15. Robert Breunig & Joseph Mercante, 2010. "The Accuracy of Predicted Wages of the Non‐Employed and Implications for Policy Simulations from Structural Labour Supply Models," The Economic Record, The Economic Society of Australia, vol. 86(272), pages 49-70, March.
    16. 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).
    17. Bernd Hayo & Edith Neuenkirch, 2018. "Survey on Germans’ Attitudes Towards and Knowledge of Monetary Policy Issues: Documentation of Survey Methodology and Descriptive Results," MAGKS Papers on Economics 201821, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    18. 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 and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 127(4), pages 627-654.

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    More about this item

    Keywords

    Item non-response; imputation; income inequality; income mobility; panel data; SOEP; BHPS; HILDA;
    All these keywords.

    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D33 - Microeconomics - - Distribution - - - Factor Income Distribution

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