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The effects of income imputation on microanalyses: evidence from the European Community Household Panel

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  • Cheti Nicoletti
  • Franco Peracchi

Abstract

Summary. Social surveys are usually affected by item and unit non‐response. Since it is unlikely that a sample of respondents is a random sample, social scientists should take the missing data problem into account in their empirical analyses. Typically, survey methodologists try to simplify the work of data users by ‘completing’ the data, filling the missing variables through imputation. The aim of the paper is to give data users some guidelines on how to assess the effects of imputation on their microlevel analyses. We focus attention on the potential bias that is caused by imputation in the analysis of income variables, using the European Community Household Panel as an illustration.

Suggested Citation

  • 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, July.
  • Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:625-646
    DOI: 10.1111/j.1467-985X.2006.00421.x
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    File URL: https://doi.org/10.1111/j.1467-985X.2006.00421.x
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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. Lorenzo Cappellari & Stephen P. Jenkins, 2004. "Modelling low income transitions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(5), pages 593-610.
    4. Franco Peracchi, 2002. "The European Community Household Panel: A review," Empirical Economics, Springer, vol. 27(1), pages 63-90.
    5. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    6. Jeffrey M. Wooldridge, 1999. "Asymptotic Properties of Weighted M-Estimators for Variable Probability Samples," Econometrica, Econometric Society, vol. 67(6), pages 1385-1406, November.
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    1. Hai Zhong, 2010. "The impact of missing data in the estimation of concentration index: a potential source of bias," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(3), pages 255-266, June.
    2. Nicoletti, Cheti & Peracchi, Franco & Foliano, Francesca, 2011. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(1), pages 61-72.
    3. Andrew E. Clark, 2006. "A Note on Unhappiness and Unemployment Duration," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, vol. 52(4), pages 291-308.
    4. Frick, Joachim R. & Grabka, Markus M. & Groh-Samberg, Olaf, 2012. "Dealing With Incomplete Household Panel Data in Inequality Research," EconStor Open Access Articles, ZBW - Leibniz Information Centre for Economics, pages 89-123.
    5. 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.
    6. Shunji Tada & Koyo Miyoshi, 2015. "Verifying household incomes in Japanese statistics," Public Policy Review, Policy Research Institute, Ministry of Finance Japan, vol. 11(4), pages 531-546, September.
    7. Valentino Dardanoni & Giuseppe De Luca & Salvatore Modica & Franco Peracchi, 2013. "Bayesian Model Averaging for Generalized Linear Models with Missing Covariates," EIEF Working Papers Series 1311, Einaudi Institute for Economics and Finance (EIEF), revised May 2013.

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