IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp991.html
   My bibliography  Save this paper

Dealing with Incomplete Household Panel Data in Inequality Research

Author

Listed:
  • Joachim R. Frick
  • Markus M. Grabka
  • Olaf Groh-Samberg

Abstract

Population surveys around the world face the problem of declining cooperation and participation rates of respondents. Not only can item nonresponse and unit nonresponse impair important outcome measures for inequality research such as total household disposable income; there is also a further case of missingness confronting household panel surveys that potentially biases results. The approach commonly used in such surveys of interviewing all adult household members and aggregating their individual incomes to yield a final outcome measure for welfare analyses often suffers from partial unit non-response (PUNR), i.e., the non-response of at least one unit, or member, of an otherwise participating household. In these cases, the aggregate income of all household members lacks at least one individual's income. These processes are typically not random and require appropriate correction. Using data from more than twenty waves of the German Socio-Economic Panel (SOEP) we evaluate four different strategies to deal with this phenomenon: (a) Ignorance, i.e., assuming the missing individual's income to be zero. (b) Adjustment of the equivalence scale to account for differences in household size and composition. (c) Elimination of all households observed to suffer PUNR, and re-weighting of households observed to be at risk of but not affected by PUNR. (d) Longitudinal imputation of the missing income components. The aim of this paper is to show how the choice of technique affects substantive results in the inequality research. We find indications of substantial bias on income inequality and poverty as well as on income mobility. These findings are obviously even more important in cross-national comparative analyses if the data providers in the individual countries deal differently with PUNR in the underlying data.

Suggested Citation

  • Joachim R. Frick & Markus M. Grabka & Olaf Groh-Samberg, 2010. "Dealing with Incomplete Household Panel Data in Inequality Research," Discussion Papers of DIW Berlin 991, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp991
    as

    Download full text from publisher

    File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.354959.de/dp991.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Foster, James & Greer, Joel & Thorbecke, Erik, 1984. "A Class of Decomposable Poverty Measures," Econometrica, Econometric Society, vol. 52(3), pages 761-766, May.
    2. Frick, Joachim R. & Grabka, Markus M., 2007. "Item Non-Response and Imputation of Annual Labor Income in Panel Surveys from a Cross-National Perspective," IZA Discussion Papers 3043, Institute for the Study of Labor (IZA).
    3. Jens Bonke & Hans Uldall-Poulsen, 2007. "Why do families actually pool their income? Evidence from Denmark," Review of Economics of the Household, Springer, vol. 5(2), pages 113-128, June.
    4. Timothy M. Smeeding & Daniel H. Weinberg, 2001. "Toward a Uniform Definition of Household Income," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 47(1), pages 1-24, March.
    5. Joachim R. Frick & Markus M. Grabka, 2003. "Imputed Rent and Income Inequality: A Decomposition Analysis for Great Britain, West Germany and the U.S," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 49(4), pages 513-537, December.
    6. Laurie, Heather & Lynn, Peter, 2008. "The use of respondent incentives on longitudinal surveys," ISER Working Paper Series 2008-42, Institute for Social and Economic Research.
    7. Shorrocks, Anthony, 1978. "Income inequality and income mobility," Journal of Economic Theory, Elsevier, vol. 19(2), pages 376-393, December.
    8. Markus M. Grabka & Joachim R. Frick, 2008. "The Shrinking German Middle Class: Signs of Long-Term Polarization in Disposable Income?," Weekly Report, DIW Berlin, German Institute for Economic Research, vol. 4(4), pages 21-27.
    9. 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.
    10. 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.
    11. Gert G. Wagner & Joachim R. Frick & Jürgen Schupp, 2007. "The German Socio-Economic Panel Study (SOEP) – Scope, Evolution and Enhancements," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 127(1), pages 139-169.
    12. Fields, Gary S & Ok, Efe A, 1999. "Measuring Movement of Incomes," Economica, London School of Economics and Political Science, vol. 66(264), pages 455-471, November.
    13. Kapteyn, Arie & Michaud, Pierre-Carl & Smith, James P. & van Soest, Arthur, 2006. "Effects of Attrition and Non-Response in the Health and Retirement Study," IZA Discussion Papers 2246, Institute for the Study of Labor (IZA).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anthony Barnes Atkinson, 2010. "Macerata Lectures on European Economic Policy. Poverty and the EU: the New Decade," Working Papers 24-2010, Macerata University, Department of Studies on Economic Development (DiSSE), revised May 2010.
    2. Coban, Mustafa & Sauerhammer, Sarah, 2017. "Transmission channels of intergenerational income mobility: Empirical evidence from Germany and the Unites States," Discussion Paper Series 138, Julius Maximilian University of Würzburg, Chair of Economic Order and Social Policy.
    3. Theresa Köhler, 2016. "Income and Wealth Poverty in Germany," SOEPpapers on Multidisciplinary Panel Data Research 857, DIW Berlin, The German Socio-Economic Panel (SOEP).
    4. Markus M. Grabka, 2013. "Codebook for the $PEQUIV File 1984-2012: CNEF Variables with Extended Income Information for the SOEP," Data Documentation 69, DIW Berlin, German Institute for Economic Research.
    5. Markus M. Grabka, 2014. "Codebook for the $PEQUIV File 1984-2013: CNEF Variables with Extended Income Information for the SOEP," Data Documentation 74, DIW Berlin, German Institute for Economic Research.
    6. Markus M. Grabka, 2011. "Codebook for the $PEQUIV File 1984-2010: CNEF Variables with Extended Income Information for the SOEP," Data Documentation 57, DIW Berlin, German Institute for Economic Research.
    7. repec:spr:astaws:v:12:y:2018:i:1:d:10.1007_s11943-018-0221-4 is not listed on IDEAS
    8. Jaenichen, Ursula & Sakshaug, Joseph, 2012. "Multiple imputation of household income in the first wave of PASS," FDZ Methodenreport 201202_en, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    9. Jan Goebel & Peter Krause & Joachim R. Frick & Markus M. Grabka & Gert G. Wagner, 2010. "Eine exemplarische Anwendung der regionalisierten Preisniveau-Daten des BBSR auf die Einkommensverteilung für die Jahre 2005 bis 2008," SOEPpapers on Multidisciplinary Panel Data Research 284, DIW Berlin, The German Socio-Economic Panel (SOEP).
    10. Markus M. Grabka, 2012. "Codebook for the $PEQUIV File 1984-2011: CNEF Variables with Extended Income Information for the SOEP," Data Documentation 65, DIW Berlin, German Institute for Economic Research.

    More about this item

    Keywords

    Household Panel Surveys; Partial Unit Non-Response; Inequality; Mobility; Imputation; SOEP;

    JEL classification:

    • D33 - Microeconomics - - Distribution - - - Factor Income Distribution
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:diw:diwwpp:dp991. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bibliothek). General contact details of provider: http://edirc.repec.org/data/diwbede.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.