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Measuring income inequality using survey data: the case of China

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  • Yongwei Chen
  • Dahai Fu

Abstract

The purpose of this paper is to raise awareness of missing data when we evaluate income inequality using survey data. If the income data are not missing completely at random, the calculated income inequalities are more likely to be biased, which may lead to inappropriate conclusions and policy recommendations. To handle the missing data on income, a multiple imputation approach is utilized. In particular, we propose an extended approach to correct the possible sample selection bias in the imputation process. A case study using China’s household survey suggests that extended imputation corrects for biases effectively in the calculation of Gini coefficients and results in gains in efficiency as well. Copyright Springer Science+Business Media New York 2015

Suggested Citation

  • Yongwei Chen & Dahai Fu, 2015. "Measuring income inequality using survey data: the case of China," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 13(2), pages 299-307, June.
    • Handle: RePEc:kap:jecinq:v:13:y:2015:i:2:p:299-307
    DOI: 10.1007/s10888-014-9283-x
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    References listed on IDEAS

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    Cited by:

    1. Prathi Seneviratne, 2017. "Explaining Changes in Sri Lanka’s Wage Distribution, 1992-2014: A Quantile Regression Analysis," Working Papers 2017-01, Carleton College, Department of Economics.
    2. Encarnación Álvarez-Verdejo & Pablo J. Moya-Fernández & Juan F. Muñoz-Rosas, 2021. "Single Imputation Methods and Confidence Intervals for the Gini Index," Mathematics, MDPI, vol. 9(24), pages 1-20, December.
    3. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    4. Zhao, Puying & Haziza, David & Wu, Changbao, 2020. "Survey weighted estimating equation inference with nuisance functionals," Journal of Econometrics, Elsevier, vol. 216(2), pages 516-536.

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