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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

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. Copyright 2006 Royal Statistical Society.

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Bibliographic Info

Article provided by Royal Statistical Society in its journal Journal of the Royal Statistical Society: Series A (Statistics in Society).

Volume (Year): 169 (2006)
Issue (Month): 3 ()
Pages: 625-646

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Handle: RePEc:bla:jorssa:v:169:y:2006:i:3:p:625-646

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Cited by:
  1. Cheti Nicoletti & Franco Peracchi & Francesca Foliano, 2009. "Estimating Income Poverty in the Presence of Missing Data and Measurement Error," SOEPpapers on Multidisciplinary Panel Data Research 252, DIW Berlin, The German Socio-Economic Panel (SOEP).
  2. Andrew E. Clark, 2006. "A Note on Unhappiness and Unemployment Duration," Applied Economics Quarterly (formerly: Konjunkturpolitik), Duncker & Humblot, Berlin, Duncker & Humblot, Berlin, vol. 52(4), pages 291-308.
  3. 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.
  4. 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, Springer, vol. 11(3), pages 255-266, June.
  5. Joachim R. Frick & Markus M. Grabka & Olaf Groh-Samberg, 2012. "Dealing With Incomplete Household Panel Data in Inequality Research," Sociological Methods & Research, , , vol. 41(1), pages 89-123, February.
  6. 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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