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The effects of income imputation on micro analyses: evidence from the ECHP

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  • Nicoletti, Cheti
  • Peracchi, Franco

Abstract

Social surveys are usually affected by item and unit nonresponse. 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 this paper is to give data users some guidelines on how to assess the effects of imputation on their micro-level analyses. We focus attention on the potential bias caused by imputation in the analysis of income variables and poverty measures. We consider two methods for evaluating the effects of imputation, using the European Community Household Panel as an illustration.

Suggested Citation

  • Nicoletti, Cheti & Peracchi, Franco, 2004. "The effects of income imputation on micro analyses: evidence from the ECHP," ISER Working Paper Series 2004-19, Institute for Social and Economic Research.
    • Handle: RePEc:ese:iserwp:2004-19
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    References listed on IDEAS

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    1. Horowitz, Joel L. & Manski, Charles F., 1998. "Censoring of outcomes and regressors due to survey nonresponse: Identification and estimation using weights and imputations," Journal of Econometrics, Elsevier, vol. 84(1), pages 37-58, May.
    2. Charles F. Manski & John V. Pepper, 2000. "Monotone Instrumental Variables, with an Application to the Returns to Schooling," Econometrica, Econometric Society, vol. 68(4), pages 997-1012, July.
    3. Nicoletti, Cheti & Peracchi, Franco, 2002. "A cross-country comparison of survey nonparticipation in the ECHP -ISER working paper-," ISER Working Paper Series 2002-32, Institute for Social and Economic Research.
    4. Franco Peracchi, 2002. "The European Community Household Panel: A review," Empirical Economics, Springer, vol. 27(1), pages 63-90.
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    Cited by:

    1. 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.
    2. Bruno Arpino & Elisabetta De Cao & Franco Peracchi, 2011. "Using panel data to partially identify HIV prevalence When HIV status is not missing at random," Working Papers 048, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    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. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2009. "Set identification with Tobin regressors," CeMMAP working papers CWP12/09, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    5. Victor Chernozhukov & Roberto Rigobon & Thomas M. Stoker, 2010. "Set identification and sensitivity analysis with Tobin regressors," Quantitative Economics, Econometric Society, vol. 1(2), pages 255-277, November.

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