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Simple adjustments of observed distributions for missing income and missing people

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  • François Bourguignon

    (Paris School of Economics)

Abstract

Correcting household survey distribution data for missing income or for undersampling may give an idea of the extent of possible biases in measuring inequality, especially when there are reasons to expect the missing income and people to belong to the top of the distribution. There are simple ways to do so when only an aggregate estimate of how much is missing is available. Atkinson had provided a formula to correct the Gini coefficient for the missing income, which was later generalized by Alvaredo (Econ. Lett. 110(3), 274–277 2011). This paper concentrates on the whole distribution and explores various alternative adjustment methods based on three key parameters: how much income, how many people are missing and on what range of income the correction should bear.

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  • François Bourguignon, 2018. "Simple adjustments of observed distributions for missing income and missing people," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(2), pages 171-188, June.
    • Handle: RePEc:spr:joecin:v:16:y:2018:i:2:d:10.1007_s10888-018-9388-8
    DOI: 10.1007/s10888-018-9388-8
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    1. A. B. Atkinson, 2017. "Pareto and the Upper Tail of the Income Distribution in the UK: 1799 to the Present," Economica, London School of Economics and Political Science, vol. 84(334), pages 129-156, April.
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    Cited by:

    1. Nicolas Frémeaux, 2023. "The More, the Better? Individual and Joint Interviewing in Surveys," Annals of Economics and Statistics, GENES, issue 149, pages 63-96.
    2. 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.
    3. 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.
    4. Advani, Arun, 2021. "Missing Incomes in the UK : Evidence and Policy Implications," The Warwick Economics Research Paper Series (TWERPS) 1364, University of Warwick, Department of Economics.
    5. Alvaredo, Facundo & De Rosa, Mauricio & Flores Beale, Ignacio & Morgan, Marc, 2022. "The Inequality (or the Growth) we Measure: Data Gaps and the Distribution of Incomes," CEPR Discussion Papers 17135, C.E.P.R. Discussion Papers.
    6. Mathias Silva, 2023. "Parametric models of income distributions integrating misreporting and non-response mechanisms," AMSE Working Papers 2311, Aix-Marseille School of Economics, France.
    7. 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.
    8. Lidia Ceriani & Vladimir Hlasny & Paolo Verme, 2021. "Bottom Incomes and the Measurement of Poverty: A Brief Assessment of the Literature," Working Papers 589, ECINEQ, Society for the Study of Economic Inequality.
    9. Thomas Blanchet & Ignacio Flores & Marc Morgan, 2022. "The weight of the rich: improving surveys using tax data," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 20(1), pages 119-150, March.
    10. Pablo Gutiérrez Cubillos, 2022. "Gini and undercoverage at the upper tail: a simple approximation," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 29(2), pages 443-471, April.
    11. Tahnee Christelle Ooms, 2021. "Correcting the Underestimation of Capital Incomes in Inequality Indicators: with an Application to the UK, 1997–2016," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 157(3), pages 929-953, October.
    12. Rami V. Tabri & Mathew J. Elias, 2024. "Testing for Restricted Stochastic Dominance under Survey Nonresponse with Panel Data: Theory and an Evaluation of Poverty in Australia," Papers 2406.15702, arXiv.org.
    13. Lidia Ceriani & Paolo Verme, 2022. "Population Changes and the Measurement of Inequality," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 549-575, July.
    14. Frank Cowell & Emmanuel Flachaire, 2021. "Inequality Measurement: Methods and Data," Post-Print hal-03589066, HAL.
    15. Ooms, Tahnee, 2021. "Correcting the underestimation of capital incomes in inequality indicators: with an application to the UK, 1997–2016," LSE Research Online Documents on Economics 108900, London School of Economics and Political Science, LSE Library.

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