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Multiple imputation: an alternative to top coding for statistical disclosure control

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  • Di An
  • Roderick J. A. Little

Abstract

Top coding of extreme values of variables like income is a common method of statistical disclosure control, but it creates problems for the data analyst. The paper proposes two alternative methods to top coding for statistical disclosure control that are based on multiple imputation. We show in simulation studies that the multiple-imputation methods provide better inferences of the publicly released data than top coding, using straightforward multiple-imputation methods of analysis, while maintaining good statistical disclosure control properties. We illustrate the methods on data from the 1995 Chinese household income project. Copyright 2007 Royal Statistical Society.

Suggested Citation

  • Di An & Roderick J. A. Little, 2007. "Multiple imputation: an alternative to top coding for statistical disclosure control," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 923-940.
  • Handle: RePEc:bla:jorssa:v:170:y:2007:i:4:p:923-940
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    Cited by:

    1. Jenkins, Stephen P. & Burkhauser, Richard V. & Feng, Shuaizhang & Larrimore, Jeff, 2009. "Measuring inequality using censored data: a multiple imputation approach," ISER Working Paper Series 2009-04, Institute for Social and Economic Research.
    2. Tapan K. Nayak & Samson A. Adeshiyan, 2016. "On Invariant Post-randomization for Statistical Disclosure Control," International Statistical Review, International Statistical Institute, vol. 84(1), pages 26-42, April.
    3. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, De Gruyter Open, vol. 29(3), pages 425-465, June.
    4. Vladimir Hlasny & Paolo Verme, 2017. "The impact of top incomes biases on the measurement of inequality in the United States," Working Papers 452, ECINEQ, Society for the Study of Economic Inequality.

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