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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

Summary. 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.

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, October.
    • Handle: RePEc:bla:jorssa:v:170:y:2007:i:4:p:923-940
    DOI: 10.1111/j.1467-985X.2007.00492.x
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    Cited by:

    1. 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.
    2. Clementi,Fabio & Fabiani,Michele & Molini,Vasco & Schettino,Francesco, 2022. "Is Inequality Systematically Underestimated in Sub-Saharan Africa ? A Proposal toOvercome the Problem," Policy Research Working Paper Series 10058, The World Bank.
    3. Mathias Silva, 2023. "Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach [Working Papers / Documents de travail]," Working Papers hal-04066544, HAL.
    4. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    5. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    6. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2009. "Measuring Inequality Using Censored Data: A Multiple Imputation Approach," Discussion Papers of DIW Berlin 866, DIW Berlin, German Institute for Economic Research.
    7. Christine N. Kohnen & Jerome P. Reiter, 2009. "Multiple imputation for combining confidential data owned by two agencies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(2), pages 511-528, April.
    8. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    9. F. Clementi & A. L. Dabalen & V. Molini & F. Schettino, 2020. "We forgot the middle class! Inequality underestimation in a changing Sub-Saharan Africa," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 18(1), pages 45-70, March.
    10. Harrison Quick & Scott H. Holan & Christopher K. Wikle, 2018. "Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 649-661, June.
    11. Vladimir Hlasny & Paolo Verme, 2022. "The Impact of Top Incomes Biases on the Measurement of Inequality in the United States," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(4), pages 749-788, August.
    12. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    13. Bartels, Charlotte & Waldenström, Daniel, 2021. "Inequality and top incomes," GLO Discussion Paper Series 959, Global Labor Organization (GLO).

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