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Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys

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  • Vladimir Hlasny

Abstract

Objective This study evaluates the bias to inequality measurement from survey nonrespondents. Methods Sixty‐six Luxembourg Income Study surveys for 38 middle‐ and high‐income countries, encompassing some 900,000 households, are used to derive estimates of the Gini coefficient for countries and selected world regions. Results Household nonresponse typically biases national Ginis downward by 1–8 percentage points. The Gini for North America appears robust to nonresponse, rising by a mere 0.34 percentage points to 44.72 when a correction is applied. The Gini for the European Union Single Market is sensitive to nonresponse, rising by 5.52 percentage points to 37.63. The OECD‐wide Gini rises by 5.93 points to 44.58. Conclusion These results appear consistent with one another and with prior evidence. They suggest that, across countries and selected world regions, household nonresponse biases the Ginis downward by 1–8 percentage points.

Suggested Citation

  • Vladimir Hlasny, 2020. "Nonresponse Bias in Inequality Measurement: Cross‐Country Analysis Using Luxembourg Income Study Surveys," Social Science Quarterly, Southwestern Social Science Association, vol. 101(2), pages 712-731, March.
    • Handle: RePEc:bla:socsci:v:101:y:2020:i:2:p:712-731
    DOI: 10.1111/ssqu.12762
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    Cited by:

    1. Brunori, Paolo & Salas-Rojo, Pedro & Verme, Paolo, 2022. "Estimating Inequality with Missing Incomes," GLO Discussion Paper Series 1138, Global Labor Organization (GLO).
    2. Ercio Muñoz & Salvatore Morelli, 2021. "kmr: A command to correct survey weights for unit nonresponse using groups’ response rates," Stata Journal, StataCorp LP, vol. 21(1), pages 206-219, March.
    3. 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.
    4. Haiyuan Wan & Yangcheng Yu, 2023. "Correction of China's income inequality for missing top incomes," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1769-1791, August.
    5. José María Sarabia & Vanesa Jorda, 2020. "Lorenz Surfaces Based on the Sarmanov–Lee Distribution with Applications to Multidimensional Inequality in Well-Being," Mathematics, MDPI, vol. 8(11), pages 1-17, November.
    6. Rafael Carranza & Marc Morgan & Brian Nolan, 2023. "Top Income Adjustments and Inequality: An Investigation of the EU‐SILC," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(3), pages 725-754, September.
    7. Nishant Yonzan & Branko Milanovic & Salvatore Morelli & Janet Gornick, 2022. "Drawing a Line: Comparing the Estimation of Top Incomes between Tax Data and Household Survey Data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 67-95, March.
    8. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    9. Vladimir Hlasny, 2019. "Redistributive Impacts of Fiscal Policies in Mexico: Corrections for Top Income Measurement Problems," LIS Working papers 765, LIS Cross-National Data Center in Luxembourg.
    10. 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.
    11. Vladimir Hlasny, 2017. "Different Faces of Inequality across Asia: Decomposition of Income Gaps across Demographic Groups," LIS Working papers 691, LIS Cross-National Data Center in Luxembourg.

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